diff --git a/timegpt-docs/about/privacy-notice.mdx b/timegpt-docs/about/privacy-notice.mdx index deedfc8a0..47f692cbc 100644 --- a/timegpt-docs/about/privacy-notice.mdx +++ b/timegpt-docs/about/privacy-notice.mdx @@ -26,7 +26,7 @@ Personal Information You Provide: We collect Personal Information if you create **Usage Data**: We may automatically collect information about your use of the Services, such as the types of content that you view or engage with, the features you use, and the actions you take, as well as your time zone, country, the dates and times of access, user agent and version, type of computer or mobile device, and your computer connection. -**Device Information**: Includes name of the device, operating system, device identifiers, and browser you are using. Information collected may depend on the type of device you use and its settings. +**Device Information**: Includes name of the device, operating system, device identifiers, and browser you are using. Information collected may depend on the type of device you use and its settings. **Cookies**: We use cookies to operate and administer our Services, and improve your experience. @@ -62,13 +62,13 @@ In certain circumstances we may provide your Personal Information to third parti # 4. Your choices and controls -Depending on where you live, you may have the right to exercise certain controls and choices regarding our collection, use, and sharing of your Personal Information. To opt-out of marketing communications please email us at [ops@nixtla.io](mailto:ops@nixtla.io) or by following the instructions included in the email or text correspondence. +Depending on where you live, you may have the right to exercise certain controls and choices regarding our collection, use, and sharing of your Personal Information. To opt-out of marketing communications please email us at [support@nixtla.io](mailto:support@nixtla.io) or by following the instructions included in the email or text correspondence. -Please note that, even if you unsubscribe from certain correspondence, we may still need to contact you with important transactional or administrative information, as permitted by law. Additionally, if you choose not to provide certain Personal Information, we may be unable to provide some or all of our Services to you. +Please note that, even if you unsubscribe from certain correspondence, we may still need to contact you with important transactional or administrative information, as permitted by law. Additionally, if you choose not to provide certain Personal Information, we may be unable to provide some or all of our Services to you. # 5. Children -Our Services are not directed to children under the age of 13. Nixtla does not knowingly collect Personal Information from children under the age of 13. If you have reason to believe that a child under the age of 13 has provided Personal Information to Nixtla through the Services, please email us at [ops@nixtla.io](mailto:ops@nixtla.io) +Our Services are not directed to children under the age of 13. Nixtla does not knowingly collect Personal Information from children under the age of 13. If you have reason to believe that a child under the age of 13 has provided Personal Information to Nixtla through the Services, please email us at [support@nixtla.io](mailto:support@nixtla.io) We will investigate any notification and if appropriate, delete the Personal Information from our systems. If you are 13 or older, but under 18, you must have consent from your parent or guardian to use our Services. @@ -88,4 +88,4 @@ We may update this Privacy Policy from time to time. All changes will be effecti # 9. How to contact us -Please contact us at [ops@nixtla.io](mailto:ops@nixtla.io) if you have any questions or concerns not already addressed in this Privacy Policy. \ No newline at end of file +Please contact us at [support@nixtla.io](mailto:support@nixtla.io) if you have any questions or concerns not already addressed in this Privacy Policy. diff --git a/timegpt-docs/about/terms-and-conditions.mdx b/timegpt-docs/about/terms-and-conditions.mdx index d001ec4dc..82ca80de3 100644 --- a/timegpt-docs/about/terms-and-conditions.mdx +++ b/timegpt-docs/about/terms-and-conditions.mdx @@ -3,6 +3,7 @@ title: "Terms and Conditions" description: "Terms and conditions for using Nixtla Services." icon: "book" --- + Thank you for using Nixtla's TimeGPT and or TimeGEN! These Terms of Use apply when you use the services of Nixtla, Inc. or our affiliates, including our application programming interface, software, tools, developer services, data, documentation, and websites ("**Services**"). The Terms include other terms and conditions, documentation, guidelines, or policies we may provide in writing. By using our Services, you agree to these Terms. Our [Privacy Notice](/about/privacy-notice) explains how we collect and use personal information. @@ -25,7 +26,7 @@ You must be at least 13 years old to use the Services. If you are under 18 you m **(a) Your Content**. You may provide input to the Services ("**Input**"), and receive output generated and returned by the Services based on the Input ("**Output**"). Input and Output are collectively ("**Content**"). As between the parties and to the extent permitted by applicable law, you own all Input. Subject to your compliance with these Terms, Nixtla hereby assigns to you all its rights, title, and interest in and to Output. This means you can use Content for any purpose, including commercial purposes such as sale or publication, if you comply with these Terms. Nixtla may use Content to provide and maintain the Services, comply with applicable law, and enforce our policies. You are responsible for Content, including for ensuring that it does not violate any applicable law or these Terms. -**(b) Use of Content to Improve Services**. In order to improve our Services, we may use Content that you provide to or receive from our API ("**API Content**") to develop or improve our Services. We may use Content from Services other than our API ("**Non-API Content**") to help develop and improve our Services. +**(b) Use of Content to Improve Services**. In order to improve our Services, we may use Content that you provide to or receive from our API ("**API Content**") to develop or improve our Services. We may use Content from Services other than our API ("**Non-API Content**") to help develop and improve our Services. Nixtla may use aggregated, de-identified data to enhance and operate the Services and for other business activities, including creating industry benchmarks and best practice guides for users. @@ -43,7 +44,7 @@ If your payment cannot be completed, we will provide you written notice and may **(c) Price Changes**. We may change our prices by posting notice to your account and/or to our website. Price increases will be effective 14 days after they are posted, except for increases made for legal reasons or increases made to Beta Services, which will be effective immediately. Any price changes will apply to the Fees charged to your account immediately after the effective date of the changes. -**(d) Disputes and Late Payments**. If you want to dispute any Fees or Taxes, please contact [ops@nixtla.io](mailto:ops@nixtla.io) within thirty (30) days of the date of the disputed invoice. Undisputed amounts past due may be subject to a finance charge of 1.5% of the unpaid balance per month. If any amount of your Fees are past due, we may suspend your access to the Services after we provide you written notice of late payment. +**(d) Disputes and Late Payments**. If you want to dispute any Fees or Taxes, please contact [support@nixtla.io](mailto:support@nixtla.io) within thirty (30) days of the date of the disputed invoice. Undisputed amounts past due may be subject to a finance charge of 1.5% of the unpaid balance per month. If any amount of your Fees are past due, we may suspend your access to the Services after we provide you written notice of late payment. **(e) Free Tier**. You may not create more than one account to benefit from credits provided in the free tier of the Services. If we believe you are not using the free tier in good faith, we may charge you standard fees or stop providing access to the Services. @@ -73,15 +74,15 @@ We may terminate these Terms for any reason by providing you at least 30 days' a **(b) Disclaimer**. THE SERVICES ARE PROVIDED "AS IS." EXCEPT TO THE EXTENT PROHIBITED BY LAW, WE AND OUR AFFILIATES AND LICENSORS MAKE NO WARRANTIES (EXPRESS, IMPLIED, STATUTORY OR OTHERWISE) WITH RESPECT TO THE SERVICES, AND DISCLAIM ALL WARRANTIES INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, SATISFACTORY QUALITY, NON-INFRINGEMENT, AND QUIET ENJOYMENT, AND ANY WARRANTIES ARISING OUT OF ANY COURSE OF DEALING OR TRADE USAGE. WE DO NOT WARRANT THAT THE SERVICES WILL BE UNINTERRUPTED, ACCURATE OR ERROR FREE, OR THAT ANY CONTENT WILL BE SECURE OR NOT LOST OR ALTERED. -**(c) Limitations of Liability**. NEITHER WE NOR ANY OF OUR AFFILIATES OR LICENSORS WILL BE LIABLE FOR ANY INDIRECT, INCIDENTAL, SPECIAL, CONSEQUENTIAL OR EXEMPLARY DAMAGES, INCLUDING DAMAGES FOR LOSS OF PROFITS, GOODWILL, USE, OR DATA OR OTHER LOSSES, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. OUR AGGREGATE LIABILITY UNDER THESE TERMS SHALL NOT EXCEED THE GREATER OF THE AMOUNT YOU PAID FOR THE SERVICE THAT GAVE RISE TO THE CLAIM DURING THE 12 MONTHS BEFORE THE LIABILITY AROSE OR ONE HUNDRED DOLLARS ($100). THE LIMITATIONS IN THIS SECTION APPLY ONLY TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW. +**(c) Limitations of Liability**. NEITHER WE NOR ANY OF OUR AFFILIATES OR LICENSORS WILL BE LIABLE FOR ANY INDIRECT, INCIDENTAL, SPECIAL, CONSEQUENTIAL OR EXEMPLARY DAMAGES, INCLUDING DAMAGES FOR LOSS OF PROFITS, GOODWILL, USE, OR DATA OR OTHER LOSSES, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. OUR AGGREGATE LIABILITY UNDER THESE TERMS SHALL NOT EXCEED THE GREATER OF THE AMOUNT YOU PAID FOR THE SERVICE THAT GAVE RISE TO THE CLAIM DURING THE 12 MONTHS BEFORE THE LIABILITY AROSE OR ONE HUNDRED DOLLARS ($100). THE LIMITATIONS IN THIS SECTION APPLY ONLY TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW. # 8. Dispute Resolution YOU AGREE TO THE FOLLOWING MANDATORY ARBITRATION AND CLASS ACTION WAIVER PROVISIONS: -**(a) MANDATORY ARBITRATION**. You and Nixtla agree to resolve any past or present claims relating to these Terms or our Services through final and binding arbitration, except that you have the right to opt out of these arbitration terms, and future changes to these arbitration terms, by emailing [ops@nixtla.io](mailto:ops@nixtla.io) within 30 days of agreeing to these arbitration terms or the relevant changes. +**(a) MANDATORY ARBITRATION**. You and Nixtla agree to resolve any past or present claims relating to these Terms or our Services through final and binding arbitration, except that you have the right to opt out of these arbitration terms, and future changes to these arbitration terms, by emailing [support@nixtla.io](mailto:support@nixtla.io) within 30 days of agreeing to these arbitration terms or the relevant changes. -**(b) Informal Dispute Resolution**. We would like to understand and try to address your concerns prior to formal legal action. Before filing a claim against Nixtla, you agree to try to resolve the dispute informally by sending us notice at [ops@nixtla.io](mailto:ops@nixtla.io) of your name, a description of the dispute, and the relief you seek. If we are unable to resolve a dispute within 60 days, you may bring a formal proceeding. Any statute of limitations will be tolled during the 60-day resolution process. If you reside in the EU, the European Commission provides for an online dispute resolution platform, which you can access at [https://ec.europa.eu/consumers/odr](https://ec.europa.eu/consumers/odr). +**(b) Informal Dispute Resolution**. We would like to understand and try to address your concerns prior to formal legal action. Before filing a claim against Nixtla, you agree to try to resolve the dispute informally by sending us notice at [support@nixtla.io](mailto:support@nixtla.io) of your name, a description of the dispute, and the relief you seek. If we are unable to resolve a dispute within 60 days, you may bring a formal proceeding. Any statute of limitations will be tolled during the 60-day resolution process. If you reside in the EU, the European Commission provides for an online dispute resolution platform, which you can access at [https://ec.europa.eu/consumers/odr](https://ec.europa.eu/consumers/odr). **(c) Arbitration Forum**. Either party may commence binding arbitration through ADR Services, an alternative dispute resolution provider. The parties will pay equal shares of the arbitration fees. If the arbitrator finds that you cannot afford to pay the arbitration fees and cannot obtain a waiver, Nixtla will pay them for you. Nixtla will not seek its attorneys' fees and costs in arbitration unless the arbitrator determines that your claim is frivolous. @@ -126,7 +127,7 @@ Nixtla, Inc. San Francisco, CA 94108 United States. -Attn: Nixtla, Inc. - [ops@nixtla.io](mailto:ops@nixtla.io) +Attn: Nixtla, Inc. - [support@nixtla.io](mailto:support@nixtla.io) **(h) Waiver and Severability**. If you do not comply with these Terms, and Nixtla does not take action right away, this does not mean Nixtla is giving up any of our rights. Except as provided in Section 8, if any part of these Terms is determined to be invalid or unenforceable by a court of competent jurisdiction, that term will be enforced to the maximum extent permissible and it will not affect the enforceability of any other terms. @@ -138,4 +139,4 @@ You represent and warrant that you are not located in any Embargoed Countries an **(k) Entire Agreement**. These Terms and any policies incorporated in these Terms contain the entire agreement between you and Nixtla regarding the use of the Services and, other than any Service specific terms of use or any applicable enterprise agreements, supersedes any prior or contemporaneous agreements, communications, or understandings between you and Nixtla on that subject. -**(l) Jurisdiction, Venue and Choice of Law**. These Terms will be governed by the laws of the State of California, excluding California's conflicts of law rules or principles. Except as provided in the "Dispute Resolution" section, all claims arising out of or relating to these Terms will be brought exclusively in the federal or state courts of San Francisco County, California, USA. \ No newline at end of file +**(l) Jurisdiction, Venue and Choice of Law**. These Terms will be governed by the laws of the State of California, excluding California's conflicts of law rules or principles. Except as provided in the "Dispute Resolution" section, all claims arising out of or relating to these Terms will be brought exclusively in the federal or state courts of San Francisco County, California, USA. diff --git a/timegpt-docs/openapi.json b/timegpt-docs/openapi.json index cd21b9f87..e8a8f311f 100644 --- a/timegpt-docs/openapi.json +++ b/timegpt-docs/openapi.json @@ -1,8032 +1,4822 @@ { - "openapi": "3.1.0", - "info": { - "title": "Nixtla Forecast API", - "description": "API for TimeGPT forecast. Just send your data as json and get results. We do the heavy lifting.", - "version": "2025.8.3" + "openapi": "3.1.0", + "info": { + "title": "Nixtla Forecast API", + "description": "API for TimeGPT forecast. Just send your data as json and get results. We do the heavy lifting.", + "version": "2025.8.3" + }, + "paths": { + "/validate_api_key": { + "get": { + "summary": "Validate Api Key", + "operationId": "validate_api_key_validate_api_key_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ] + } }, - "paths": { - "/validate_api_key": { - "get": { - "summary": "Validate Api Key", - "operationId": "validate_api_key_validate_api_key_get", - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } + "/validate_token": { + "post": { + "tags": ["hidden"], + "summary": "Validate Token", + "operationId": "validate_token_validate_token_post", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "validate_token", + "x-hidden": true + } + }, + "/health": { + "get": { + "tags": ["excluded"], + "summary": "Health", + "description": "Check if server is healthy.\nUsed by the readiness probe to check server is healthy.", + "operationId": "health_health_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-excluded": true + } + }, + "/forecast": { + "post": { + "tags": ["hidden"], + "summary": "Foundational Time Series Model (Beta)", + "description": "This endpoint predicts the future values of a single time series based on the provided data. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io", + "operationId": "forecast_forecast_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SingleSeriesForecast", + "examples": [ + { + "fh": 7, + "y": { + "2015-12-02": 4.390508031418598, + "2015-12-03": 5.721514930979356, + "2015-12-04": 4.822107008573151, + "2015-12-05": 4.359065463975175, + "2015-12-06": 3.3892383947112377, + "2015-12-07": 5.167152904533249, + "2015-12-08": 3.50069769010154, + "2015-12-09": 7.134184006256638, + "2015-12-10": 7.709302084008234, + "2015-12-11": 3.0675321506062216, + "2015-12-12": 6.333800304661317, + "2015-12-13": 4.231159358023236, + "2015-12-14": 4.5443564887514585, + "2015-12-15": 7.404773106341288, + "2015-12-16": 0.5682884655830955, + "2015-12-17": 0.6970343976123257, + "2015-12-18": 0.16174717952260576, + "2015-12-19": 6.660958764383504, + "2015-12-20": 6.225254007598804, + "2015-12-21": 6.960097185974553, + "2015-12-22": 7.828946737862112, + "2015-12-23": 6.393268513733789, + "2015-12-24": 3.6918348980234548, + "2015-12-25": 6.244233410291644, + "2015-12-26": 0.9461954069514658, + "2015-12-27": 5.119368170620191, + "2015-12-28": 1.1468262992723712, + "2015-12-29": 7.557351336396671, + "2015-12-30": 4.174786574000573, + "2015-12-31": 3.3172955199241887, + "2016-01-01": 2.1164448968370158, + "2016-01-02": 6.193869515473733, + "2016-01-03": 3.6492026577323884, + "2016-01-04": 4.547471590949188, + "2016-01-05": 0.15031840349084113, + "2016-01-06": 4.9410839766070165, + "2016-01-07": 4.896765781779371, + "2016-01-08": 4.935471974998055, + "2016-01-09": 7.549984628116993, + "2016-01-10": 5.454562392827867, + "2016-01-11": 2.876063204590288, + "2016-01-12": 3.4962556303947316, + "2016-01-13": 5.581049567418119 + }, + "x": { + "2015-12-02": [0.5701967704178796, 0.6778165367962301], + "2015-12-03": [0.43860151346232035, 0.27000797319216485], + "2015-12-04": [0.9883738380592262, 0.7351940221225949], + "2015-12-05": [0.10204481074802807, 0.9621885451174382], + "2015-12-06": [0.2088767560948347, 0.24875314351995803], + "2015-12-07": [0.16130951788499626, 0.5761573344178369], + "2015-12-08": [0.6531083254653984, 0.592041931271839], + "2015-12-09": [0.2532916025397821, 0.5722519057908734], + "2015-12-10": [0.4663107728563063, 0.2230816326406183], + "2015-12-11": [0.24442559200160274, 0.952749011516985], + "2015-12-12": [0.15896958364551972, 0.44712537861762736], + "2015-12-13": [0.11037514116430513, 0.8464086724711278], + "2015-12-14": [0.6563295894652734, 0.6994792753175043], + "2015-12-15": [0.1381829513486138, 0.29743695085513366], + "2015-12-16": [0.1965823616800535, 0.8137978197024772], + "2015-12-17": [0.3687251706609641, 0.39650574084698464], + "2015-12-18": [0.8209932298479351, 0.8811031971111616], + "2015-12-19": [0.09710127579306127, 0.5812728726358587], + "2015-12-20": [0.8379449074988039, 0.8817353618548528], + "2015-12-21": [0.09609840789396307, 0.6925315900777659], + "2015-12-22": [0.9764594650133958, 0.7252542798196405], + "2015-12-23": [0.4686512016477016, 0.5013243819267023], + "2015-12-24": [0.9767610881903371, 0.9560836347232239], + "2015-12-25": [0.604845519745046, 0.6439901992296374], + "2015-12-26": [0.7392635793983017, 0.4238550485581797], + "2015-12-27": [0.039187792254320675, 0.6063932141279244], + "2015-12-28": [0.2828069625764096, 0.019193198309333526], + "2015-12-29": [0.1201965612131689, 0.30157481667454933], + "2015-12-30": [0.29614019752214493, 0.660173537492685], + "2015-12-31": [0.11872771895424405, 0.29007760721044407], + "2016-01-01": [0.317983179393976, 0.6180154289988415], + "2016-01-02": [0.41426299451466997, 0.42876870094576613], + "2016-01-03": [0.06414749634878436, 0.13547406422245023], + "2016-01-04": [0.6924721193700198, 0.29828232595603077], + "2016-01-05": [0.5666014542065752, 0.5699649107012649], + "2016-01-06": [0.2653894909394454, 0.5908727612481732], + "2016-01-07": [0.5232480534666997, 0.5743252488495788], + "2016-01-08": [0.09394051075844168, 0.6532008198571336], + "2016-01-09": [0.5759464955561793, 0.6521032700016889], + "2016-01-10": [0.9292961975762141, 0.43141843543397396], + "2016-01-11": [0.31856895245132366, 0.896546595851063], + "2016-01-12": [0.6674103799636817, 0.36756187004789653], + "2016-01-13": [0.13179786240439217, 0.4358649252656268], + "2016-01-14": [0.7163272041185655, 0.8919233550156721], + "2016-01-15": [0.2894060929472011, 0.8061939890460857], + "2016-01-16": [0.18319136200711683, 0.7038885835403663], + "2016-01-17": [0.5865129348100832, 0.10022688731230112], + "2016-01-18": [0.020107546187493552, 0.9194826137446735], + "2016-01-19": [0.8289400292173631, 0.7142412995491114], + "2016-01-20": [0.004695476192547066, 0.9988470065678665] + }, + "freq": "D", + "clean_ex_first": true, + "level": [90], + "finetune_steps": 0, + "model": "timegpt-1" + } ] + } } + }, + "required": true }, - "/validate_token": { - "post": { - "tags": [ - "hidden" - ], - "summary": "Validate Token", - "operationId": "validate_token_validate_token_post", - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "validate_token", - "x-hidden": true + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } } + } }, - "/health": { - "get": { - "tags": [ - "excluded" - ], - "summary": "Health", - "description": "Check if server is healthy.\nUsed by the readiness probe to check server is healthy.", - "operationId": "health_health_get", - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-excluded": true + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "forecast", + "x-hidden": true + } + }, + "/historic_forecast": { + "post": { + "tags": ["hidden"], + "summary": "Foundational Time Series Model Historic (Beta)", + "description": "Based on the provided data, this endpoint predicts time series data for the in-sample period (historical period). It takes a JSON as an input, including information like the series frequency and the historical data. (See below for a full description of the parameters.) The response contains the predicted values for the historical period. Usually useful for anomaly detection. Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "historic_forecast_historic_forecast_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SingleSeriesInsampleForecast", + "examples": [ + { + "fh": 7, + "y": { + "2015-12-02": 4.390508031418598, + "2015-12-03": 5.721514930979356, + "2015-12-04": 4.822107008573151, + "2015-12-05": 4.359065463975175, + "2015-12-06": 3.3892383947112377, + "2015-12-07": 5.167152904533249, + "2015-12-08": 3.50069769010154, + "2015-12-09": 7.134184006256638, + "2015-12-10": 7.709302084008234, + "2015-12-11": 3.0675321506062216, + "2015-12-12": 6.333800304661317, + "2015-12-13": 4.231159358023236, + "2015-12-14": 4.5443564887514585, + "2015-12-15": 7.404773106341288, + "2015-12-16": 0.5682884655830955, + "2015-12-17": 0.6970343976123257, + "2015-12-18": 0.16174717952260576, + "2015-12-19": 6.660958764383504, + "2015-12-20": 6.225254007598804, + "2015-12-21": 6.960097185974553, + "2015-12-22": 7.828946737862112, + "2015-12-23": 6.393268513733789, + "2015-12-24": 3.6918348980234548, + "2015-12-25": 6.244233410291644, + "2015-12-26": 0.9461954069514658, + "2015-12-27": 5.119368170620191, + "2015-12-28": 1.1468262992723712, + "2015-12-29": 7.557351336396671, + "2015-12-30": 4.174786574000573, + "2015-12-31": 3.3172955199241887, + "2016-01-01": 2.1164448968370158, + "2016-01-02": 6.193869515473733, + "2016-01-03": 3.6492026577323884, + "2016-01-04": 4.547471590949188, + "2016-01-05": 0.15031840349084113, + "2016-01-06": 4.9410839766070165, + "2016-01-07": 4.896765781779371, + "2016-01-08": 4.935471974998055, + "2016-01-09": 7.549984628116993, + "2016-01-10": 5.454562392827867, + "2016-01-11": 2.876063204590288, + "2016-01-12": 3.4962556303947316, + "2016-01-13": 5.581049567418119 + }, + "freq": "D", + "clean_ex_first": true, + "level": [90], + "model": "timegpt-1" + } + ] + } } + }, + "required": true }, - "/forecast": { - "post": { - "tags": [ - "hidden" - ], - "summary": "Foundational Time Series Model (Beta)", - "description": "This endpoint predicts the future values of a single time series based on the provided data. 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"hidden" - ], - "summary": "Foundational Time Series Model Historic (Beta)", - "description": "Based on the provided data, this endpoint predicts time series data for the in-sample period (historical period). It takes a JSON as an input, including information like the series frequency and the historical data. (See below for a full description of the parameters.) The response contains the predicted values for the historical period. Usually useful for anomaly detection. Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "historic_forecast_historic_forecast_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/SingleSeriesInsampleForecast", - "examples": [ - { - "fh": 7, - "y": { - "2015-12-02": 4.390508031418598, - "2015-12-03": 5.721514930979356, - "2015-12-04": 4.822107008573151, - "2015-12-05": 4.359065463975175, - "2015-12-06": 3.3892383947112377, - "2015-12-07": 5.167152904533249, - "2015-12-08": 3.50069769010154, - "2015-12-09": 7.134184006256638, - "2015-12-10": 7.709302084008234, - "2015-12-11": 3.0675321506062216, - "2015-12-12": 6.333800304661317, - "2015-12-13": 4.231159358023236, - "2015-12-14": 4.5443564887514585, - "2015-12-15": 7.404773106341288, - "2015-12-16": 0.5682884655830955, - "2015-12-17": 0.6970343976123257, - "2015-12-18": 0.16174717952260576, - "2015-12-19": 6.660958764383504, - "2015-12-20": 6.225254007598804, - "2015-12-21": 6.960097185974553, - "2015-12-22": 7.828946737862112, - "2015-12-23": 6.393268513733789, - "2015-12-24": 3.6918348980234548, - "2015-12-25": 6.244233410291644, - "2015-12-26": 0.9461954069514658, - "2015-12-27": 5.119368170620191, - "2015-12-28": 1.1468262992723712, - "2015-12-29": 7.557351336396671, - "2015-12-30": 4.174786574000573, - "2015-12-31": 3.3172955199241887, - "2016-01-01": 2.1164448968370158, - "2016-01-02": 6.193869515473733, - "2016-01-03": 3.6492026577323884, - "2016-01-04": 4.547471590949188, - "2016-01-05": 0.15031840349084113, - "2016-01-06": 4.9410839766070165, - "2016-01-07": 4.896765781779371, - "2016-01-08": 4.935471974998055, - "2016-01-09": 7.549984628116993, - "2016-01-10": 5.454562392827867, - "2016-01-11": 2.876063204590288, - "2016-01-12": 3.4962556303947316, - "2016-01-13": 5.581049567418119 - }, - "freq": "D", - "clean_ex_first": true, - "level": [ - 90 - ], - "model": "timegpt-1" - } - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "historic_forecast", - "x-hidden": true + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "historic_forecast", + "x-hidden": true + } + }, + "/forecast_multi_series": { + "post": { + "tags": ["hidden"], + "summary": "Foundational Time Series Model Multi Series (Beta)", + "description": "Based on the provided data, this endpoint predicts the future values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for each series based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "forecast_multi_series_forecast_multi_series_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MultiSeriesForecast", + "examples": [null] + } } + }, + "required": true }, - "/forecast_multi_series": { - "post": { - "tags": [ - "hidden" - ], - "summary": "Foundational Time Series Model Multi Series (Beta)", - "description": "Based on the provided data, this endpoint predicts the future values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for each series based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "forecast_multi_series_forecast_multi_series_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/MultiSeriesForecast", - "examples": [ - null - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "forecast_multi_series", - "x-hidden": true + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } } + } }, - "/historic_forecast_multi_series": { - "post": { - "tags": [ - "hidden" - ], - "summary": "Foundational Time Series Model Multi Series Historic (Beta)", - "description": "Based on the provided data, this endpoint predicts the in-sample period (historical period) values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for the historical period. Usually useful for anomaly detection. Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "historic_forecast_multi_series_historic_forecast_multi_series_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/MultiSeriesInsampleForecast", - "examples": [ - { - "y": { - "columns": [ - "unique_id", - "ds", - "y" - ], - "data": [ - [ - "PeytonManning", - "2015-12-02", - 4.390508031418598 - ], - [ - "PeytonManning", - "2015-12-03", - 5.721514930979356 - ], - [ - "PeytonManning", - "2015-12-04", - 4.822107008573151 - ], - [ - "PeytonManning", - "2015-12-05", - 4.359065463975175 - ], - [ - "PeytonManning", - "2015-12-06", - 3.3892383947112377 - ], - [ - "PeytonManning", - "2015-12-07", - 5.167152904533249 - ], - [ - "PeytonManning", - "2015-12-08", - 3.50069769010154 - ], - [ - "PeytonManning", - "2015-12-09", - 7.134184006256638 - ], - [ - "PeytonManning", - "2015-12-10", - 7.709302084008234 - ], - [ - "PeytonManning", - "2015-12-11", - 3.0675321506062216 - 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} - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "historic_forecast_multi_series", - "x-hidden": true + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "forecast_multi_series", + "x-hidden": true + } + }, + "/historic_forecast_multi_series": { + "post": { + "tags": ["hidden"], + "summary": "Foundational Time Series Model Multi Series Historic (Beta)", + "description": "Based on the provided data, this endpoint predicts the in-sample period (historical period) values of multiple time series at once. 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Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "historic_forecast_multi_series_historic_forecast_multi_series_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MultiSeriesInsampleForecast", + "examples": [ + { + "y": { + "columns": ["unique_id", "ds", "y"], + "data": [ + ["PeytonManning", "2015-12-02", 4.390508031418598], + ["PeytonManning", "2015-12-03", 5.721514930979356], + ["PeytonManning", "2015-12-04", 4.822107008573151], + ["PeytonManning", "2015-12-05", 4.359065463975175], + ["PeytonManning", "2015-12-06", 3.3892383947112377], + ["PeytonManning", "2015-12-07", 5.167152904533249], + ["PeytonManning", "2015-12-08", 3.50069769010154], + ["PeytonManning", "2015-12-09", 7.134184006256638], + ["PeytonManning", "2015-12-10", 7.709302084008234], + ["PeytonManning", "2015-12-11", 3.0675321506062216], + ["PeytonManning", "2015-12-12", 6.333800304661317], + ["PeytonManning", "2015-12-13", 4.231159358023236], + ["PeytonManning", "2015-12-14", 4.5443564887514585], + ["PeytonManning", "2015-12-15", 7.404773106341288], + ["PeytonManning", "2015-12-16", 0.5682884655830955], + ["PeytonManning", "2015-12-17", 0.6970343976123257], + ["PeytonManning", "2015-12-18", 0.16174717952260576], + ["PeytonManning", "2015-12-19", 6.660958764383504], + ["PeytonManning", "2015-12-20", 6.225254007598804], + ["PeytonManning", "2015-12-21", 6.960097185974553], + ["PeytonManning", "2015-12-22", 7.828946737862112], + ["PeytonManning", "2015-12-23", 6.393268513733789], + ["PeytonManning", "2015-12-24", 3.6918348980234548], + ["PeytonManning", "2015-12-25", 6.244233410291644], + ["PeytonManning", "2015-12-26", 0.9461954069514658], + ["PeytonManning", "2015-12-27", 5.119368170620191], + ["PeytonManning", "2015-12-28", 1.1468262992723712], + ["PeytonManning", "2015-12-29", 7.557351336396671], + ["PeytonManning", "2015-12-30", 4.174786574000573], + ["PeytonManning", "2015-12-31", 3.3172955199241887], + ["PeytonManning", "2016-01-01", 2.1164448968370158], + ["PeytonManning", "2016-01-02", 6.193869515473733], + ["PeytonManning", "2016-01-03", 3.6492026577323884], + ["PeytonManning", "2016-01-04", 4.547471590949188], + ["PeytonManning", "2016-01-05", 0.15031840349084113], + ["PeytonManning", "2016-01-06", 4.9410839766070165], + ["PeytonManning", "2016-01-07", 4.896765781779371], + ["PeytonManning", "2016-01-08", 4.935471974998055], + ["PeytonManning", "2016-01-09", 7.549984628116993], + ["PeytonManning", "2016-01-10", 5.454562392827867], + ["PeytonManning", "2016-01-11", 2.876063204590288], + ["PeytonManning", "2016-01-12", 3.4962556303947316], + ["PeytonManning", "2016-01-13", 5.581049567418119], + ["TomBrady", "2015-12-02", 4.390508031418598], + ["TomBrady", "2015-12-03", 5.721514930979356], + ["TomBrady", "2015-12-04", 4.822107008573151], + ["TomBrady", "2015-12-05", 4.359065463975175], + ["TomBrady", "2015-12-06", 3.3892383947112377], + ["TomBrady", "2015-12-07", 5.167152904533249], + ["TomBrady", "2015-12-08", 3.50069769010154], + ["TomBrady", "2015-12-09", 7.134184006256638], + ["TomBrady", "2015-12-10", 7.709302084008234], + ["TomBrady", "2015-12-11", 3.0675321506062216], + ["TomBrady", "2015-12-12", 6.333800304661317], + ["TomBrady", "2015-12-13", 4.231159358023236], + ["TomBrady", "2015-12-14", 4.5443564887514585], + ["TomBrady", "2015-12-15", 7.404773106341288], + ["TomBrady", "2015-12-16", 0.5682884655830955], + ["TomBrady", "2015-12-17", 0.6970343976123257], + ["TomBrady", "2015-12-18", 0.16174717952260576], + ["TomBrady", "2015-12-19", 6.660958764383504], + ["TomBrady", "2015-12-20", 6.225254007598804], + ["TomBrady", "2015-12-21", 6.960097185974553], + ["TomBrady", "2015-12-22", 7.828946737862112], + ["TomBrady", "2015-12-23", 6.393268513733789], + ["TomBrady", "2015-12-24", 3.6918348980234548], + ["TomBrady", "2015-12-25", 6.244233410291644], + ["TomBrady", "2015-12-26", 0.9461954069514658], + ["TomBrady", "2015-12-27", 5.119368170620191], + ["TomBrady", "2015-12-28", 1.1468262992723712], + ["TomBrady", "2015-12-29", 7.557351336396671], + ["TomBrady", "2015-12-30", 4.174786574000573], + ["TomBrady", "2015-12-31", 3.3172955199241887], + ["TomBrady", "2016-01-01", 2.1164448968370158], + ["TomBrady", "2016-01-02", 6.193869515473733], + ["TomBrady", "2016-01-03", 3.6492026577323884], + ["TomBrady", "2016-01-04", 4.547471590949188], + ["TomBrady", "2016-01-05", 0.15031840349084113], + ["TomBrady", "2016-01-06", 4.9410839766070165], + ["TomBrady", "2016-01-07", 4.896765781779371], + ["TomBrady", "2016-01-08", 4.935471974998055], + ["TomBrady", "2016-01-09", 7.549984628116993], + ["TomBrady", "2016-01-10", 5.454562392827867], + ["TomBrady", "2016-01-11", 2.876063204590288], + ["TomBrady", "2016-01-12", 3.4962556303947316], + ["TomBrady", "2016-01-13", 5.581049567418119] + ] + }, + "freq": "D", + "level": [90], + "model": "timegpt-1" + } + ] + } } + }, + "required": true }, - 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Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "timegpt_historic_timegpt_historic_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/SingleSeriesInsampleForecast", - "examples": [ - { - "fh": 7, - "y": { - "2015-12-02": 4.390508031418598, - "2015-12-03": 5.721514930979356, - "2015-12-04": 4.822107008573151, - "2015-12-05": 4.359065463975175, - "2015-12-06": 3.3892383947112377, - "2015-12-07": 5.167152904533249, - "2015-12-08": 3.50069769010154, - "2015-12-09": 7.134184006256638, - "2015-12-10": 7.709302084008234, - "2015-12-11": 3.0675321506062216, - "2015-12-12": 6.333800304661317, - "2015-12-13": 4.231159358023236, - "2015-12-14": 4.5443564887514585, - "2015-12-15": 7.404773106341288, - "2015-12-16": 0.5682884655830955, - "2015-12-17": 0.6970343976123257, - "2015-12-18": 0.16174717952260576, - "2015-12-19": 6.660958764383504, - "2015-12-20": 6.225254007598804, - "2015-12-21": 6.960097185974553, - "2015-12-22": 7.828946737862112, - "2015-12-23": 6.393268513733789, - "2015-12-24": 3.6918348980234548, - "2015-12-25": 6.244233410291644, - "2015-12-26": 0.9461954069514658, - "2015-12-27": 5.119368170620191, - "2015-12-28": 1.1468262992723712, - "2015-12-29": 7.557351336396671, - "2015-12-30": 4.174786574000573, - "2015-12-31": 3.3172955199241887, - "2016-01-01": 2.1164448968370158, - "2016-01-02": 6.193869515473733, - "2016-01-03": 3.6492026577323884, - "2016-01-04": 4.547471590949188, - "2016-01-05": 0.15031840349084113, - "2016-01-06": 4.9410839766070165, - "2016-01-07": 4.896765781779371, - "2016-01-08": 4.935471974998055, - "2016-01-09": 7.549984628116993, - "2016-01-10": 5.454562392827867, - "2016-01-11": 2.876063204590288, - "2016-01-12": 3.4962556303947316, - "2016-01-13": 5.581049567418119 - }, - "freq": "D", - "clean_ex_first": true, - "level": [ - 90 - ], - "model": "timegpt-1" - } - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "deprecated": true, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "timegpt_historic", - "x-excluded": true + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } } + } }, - "/timegpt_multi_series": { - "post": { - "tags": [ - "excluded" - ], - "summary": "Foundational Time Series Model Multi Series (Beta)", - "description": "Based on the provided data, this endpoint predicts the future values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for each series based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "timegpt_multi_series_timegpt_multi_series_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/MultiSeriesForecast", - "examples": [ - null - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "deprecated": true, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "timegpt_multi_series", - "x-excluded": true + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "cross_validation_multi_series", + "x-hidden": true + } + }, + "/model_params": { + "get": { + "tags": ["excluded"], + "summary": "Get Model Params", + "operationId": "get_model_params_model_params_get", + "security": [ + { + "HTTPBearer": [] + } + ], + "parameters": [ + { + "name": "model", + "in": "query", + "required": true, + "schema": { + "title": "Model" + } + }, + { + "name": "freq", + "in": "query", + "required": true, + "schema": { + "type": "string", + "title": "Freq" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } }, - "/timegpt_multi_series_historic": { - "post": { - "tags": [ - "excluded" - ], - "summary": "Foundational Time Series Model Multi Series Historic (Beta)", - "description": "Based on the provided data, this endpoint predicts the in-sample period (historical period) values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for the historical period. Usually useful for anomaly detection. Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "timegpt_multi_series_historic_timegpt_multi_series_historic_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/MultiSeriesInsampleForecast", - "examples": [ - { - "y": { - "columns": [ - "unique_id", - "ds", - "y" - ], - "data": [ - [ - "PeytonManning", - "2015-12-02", - 4.390508031418598 - ], - [ - "PeytonManning", - "2015-12-03", - 5.721514930979356 - ], - [ - "PeytonManning", - "2015-12-04", - 4.822107008573151 - ], - [ - "PeytonManning", - "2015-12-05", - 4.359065463975175 - ], - [ - "PeytonManning", - "2015-12-06", - 3.3892383947112377 - ], - [ - "PeytonManning", - "2015-12-07", - 5.167152904533249 - ], - [ - "PeytonManning", - "2015-12-08", - 3.50069769010154 - ], - [ - "PeytonManning", - "2015-12-09", - 7.134184006256638 - ], - [ - "PeytonManning", - "2015-12-10", - 7.709302084008234 - ], - [ - "PeytonManning", - "2015-12-11", - 3.0675321506062216 - 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5.119368170620191 - ], - [ - "PeytonManning", - "2015-12-28", - 1.1468262992723712 - ], - [ - "PeytonManning", - "2015-12-29", - 7.557351336396671 - ], - [ - "PeytonManning", - "2015-12-30", - 4.174786574000573 - ], - [ - "PeytonManning", - "2015-12-31", - 3.3172955199241887 - ], - [ - "PeytonManning", - "2016-01-01", - 2.1164448968370158 - ], - [ - "PeytonManning", - "2016-01-02", - 6.193869515473733 - ], - [ - "PeytonManning", - "2016-01-03", - 3.6492026577323884 - ], - [ - "PeytonManning", - "2016-01-04", - 4.547471590949188 - ], - [ - "PeytonManning", - "2016-01-05", - 0.15031840349084113 - ], - [ - "PeytonManning", - "2016-01-06", - 4.9410839766070165 - ], - [ - "PeytonManning", - "2016-01-07", - 4.896765781779371 - ], - [ - "PeytonManning", - "2016-01-08", - 4.935471974998055 - ], - [ - "PeytonManning", - "2016-01-09", - 7.549984628116993 - ], - [ - "PeytonManning", - "2016-01-10", - 5.454562392827867 - ], - [ - "PeytonManning", - "2016-01-11", - 2.876063204590288 - ], - [ - "PeytonManning", - 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"2016-01-03", - 3.6492026577323884 - ], - [ - "TomBrady", - "2016-01-04", - 4.547471590949188 - ], - [ - "TomBrady", - "2016-01-05", - 0.15031840349084113 - ], - [ - "TomBrady", - "2016-01-06", - 4.9410839766070165 - ], - [ - "TomBrady", - "2016-01-07", - 4.896765781779371 - ], - [ - "TomBrady", - "2016-01-08", - 4.935471974998055 - ], - [ - "TomBrady", - "2016-01-09", - 7.549984628116993 - ], - [ - "TomBrady", - "2016-01-10", - 5.454562392827867 - ], - [ - "TomBrady", - "2016-01-11", - 2.876063204590288 - ], - [ - "TomBrady", - "2016-01-12", - 3.4962556303947316 - ], - [ - "TomBrady", - "2016-01-13", - 5.581049567418119 - ] - ] - }, - "freq": "D", - "level": [ - 90 - ], - "model": "timegpt-1" - } - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "deprecated": true, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "timegpt_multi_series_historic", - "x-excluded": true + "x-excluded": true + }, + "post": { + "tags": ["excluded"], + "summary": "Model Params", + "operationId": "model_params_model_params_post", + "security": [ + { + "HTTPBearer": [] + } + ], + "requestBody": { + "required": true, + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SingleSeriesForecast" + } } + } }, - "/timegpt_multi_series_anomalies": { - "post": { - "tags": [ - "excluded" - ], - "summary": "Foundational Time Series Model Multi Series Anomaly Detector (Beta)", - "description": "Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "timegpt_multi_series_anomalies_timegpt_multi_series_anomalies_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/MultiSeriesAnomaly", - "examples": [ - null - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "deprecated": true, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "timegpt_multi_series_anomalies", - "x-excluded": true + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } } + } }, - "/timegpt_multi_series_cross_validation": { - "post": { - "tags": [ - "excluded" - ], - "summary": "Foundational Time Series Model Multi Series Cross Validation (Beta)", - "description": "Perform Cross Validation for multiple series", - "operationId": "timegpt_multi_series_cross_validation_timegpt_multi_series_cross_validation_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/MultiSeriesCrossValidation", - "examples": [ - null - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": {} - } - } + "x-fern-sdk-method-name": "model_params", + "x-excluded": true + } + }, + "/timegpt": { + "post": { + "tags": ["excluded"], + "summary": "Foundational Time Series Model (Beta)", + "description": "This endpoint predicts the future values of a single time series based on the provided data. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io", + "operationId": "timegpt_timegpt_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SingleSeriesForecast", + "examples": [ + { + "fh": 7, + "y": { + "2015-12-02": 4.390508031418598, + "2015-12-03": 5.721514930979356, + "2015-12-04": 4.822107008573151, + "2015-12-05": 4.359065463975175, + "2015-12-06": 3.3892383947112377, + "2015-12-07": 5.167152904533249, + "2015-12-08": 3.50069769010154, + "2015-12-09": 7.134184006256638, + "2015-12-10": 7.709302084008234, + "2015-12-11": 3.0675321506062216, + "2015-12-12": 6.333800304661317, + "2015-12-13": 4.231159358023236, + "2015-12-14": 4.5443564887514585, + "2015-12-15": 7.404773106341288, + "2015-12-16": 0.5682884655830955, + "2015-12-17": 0.6970343976123257, + "2015-12-18": 0.16174717952260576, + "2015-12-19": 6.660958764383504, + "2015-12-20": 6.225254007598804, + "2015-12-21": 6.960097185974553, + "2015-12-22": 7.828946737862112, + "2015-12-23": 6.393268513733789, + "2015-12-24": 3.6918348980234548, + "2015-12-25": 6.244233410291644, + "2015-12-26": 0.9461954069514658, + "2015-12-27": 5.119368170620191, + "2015-12-28": 1.1468262992723712, + "2015-12-29": 7.557351336396671, + "2015-12-30": 4.174786574000573, + "2015-12-31": 3.3172955199241887, + "2016-01-01": 2.1164448968370158, + "2016-01-02": 6.193869515473733, + "2016-01-03": 3.6492026577323884, + "2016-01-04": 4.547471590949188, + "2016-01-05": 0.15031840349084113, + "2016-01-06": 4.9410839766070165, + "2016-01-07": 4.896765781779371, + "2016-01-08": 4.935471974998055, + "2016-01-09": 7.549984628116993, + "2016-01-10": 5.454562392827867, + "2016-01-11": 2.876063204590288, + "2016-01-12": 3.4962556303947316, + "2016-01-13": 5.581049567418119 }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "deprecated": true, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "timegpt_multi_series_cross_validation", - "x-excluded": true + "x": { + "2015-12-02": [0.5701967704178796, 0.6778165367962301], + "2015-12-03": [0.43860151346232035, 0.27000797319216485], + "2015-12-04": [0.9883738380592262, 0.7351940221225949], + "2015-12-05": [0.10204481074802807, 0.9621885451174382], + "2015-12-06": [0.2088767560948347, 0.24875314351995803], + "2015-12-07": [0.16130951788499626, 0.5761573344178369], + "2015-12-08": [0.6531083254653984, 0.592041931271839], + "2015-12-09": [0.2532916025397821, 0.5722519057908734], + "2015-12-10": [0.4663107728563063, 0.2230816326406183], + "2015-12-11": [0.24442559200160274, 0.952749011516985], + "2015-12-12": [0.15896958364551972, 0.44712537861762736], + "2015-12-13": [0.11037514116430513, 0.8464086724711278], + "2015-12-14": [0.6563295894652734, 0.6994792753175043], + "2015-12-15": [0.1381829513486138, 0.29743695085513366], + "2015-12-16": [0.1965823616800535, 0.8137978197024772], + "2015-12-17": [0.3687251706609641, 0.39650574084698464], + "2015-12-18": [0.8209932298479351, 0.8811031971111616], + "2015-12-19": [0.09710127579306127, 0.5812728726358587], + "2015-12-20": [0.8379449074988039, 0.8817353618548528], + "2015-12-21": [0.09609840789396307, 0.6925315900777659], + "2015-12-22": [0.9764594650133958, 0.7252542798196405], + "2015-12-23": [0.4686512016477016, 0.5013243819267023], + "2015-12-24": [0.9767610881903371, 0.9560836347232239], + "2015-12-25": [0.604845519745046, 0.6439901992296374], + "2015-12-26": [0.7392635793983017, 0.4238550485581797], + "2015-12-27": [0.039187792254320675, 0.6063932141279244], + "2015-12-28": [0.2828069625764096, 0.019193198309333526], + "2015-12-29": [0.1201965612131689, 0.30157481667454933], + "2015-12-30": [0.29614019752214493, 0.660173537492685], + "2015-12-31": [0.11872771895424405, 0.29007760721044407], + "2016-01-01": [0.317983179393976, 0.6180154289988415], + "2016-01-02": [0.41426299451466997, 0.42876870094576613], + "2016-01-03": [0.06414749634878436, 0.13547406422245023], + "2016-01-04": [0.6924721193700198, 0.29828232595603077], + "2016-01-05": [0.5666014542065752, 0.5699649107012649], + "2016-01-06": [0.2653894909394454, 0.5908727612481732], + "2016-01-07": [0.5232480534666997, 0.5743252488495788], + "2016-01-08": [0.09394051075844168, 0.6532008198571336], + "2016-01-09": [0.5759464955561793, 0.6521032700016889], + "2016-01-10": [0.9292961975762141, 0.43141843543397396], + "2016-01-11": [0.31856895245132366, 0.896546595851063], + "2016-01-12": [0.6674103799636817, 0.36756187004789653], + "2016-01-13": [0.13179786240439217, 0.4358649252656268], + "2016-01-14": [0.7163272041185655, 0.8919233550156721], + "2016-01-15": [0.2894060929472011, 0.8061939890460857], + "2016-01-16": [0.18319136200711683, 0.7038885835403663], + "2016-01-17": [0.5865129348100832, 0.10022688731230112], + "2016-01-18": [0.020107546187493552, 0.9194826137446735], + "2016-01-19": [0.8289400292173631, 0.7142412995491114], + "2016-01-20": [0.004695476192547066, 0.9988470065678665] + }, + "freq": "D", + "clean_ex_first": true, + "level": [90], + "finetune_steps": 0, + "model": "timegpt-1" + } + ] + } } + }, + "required": true }, - "/v2/forecast": { - "post": { - "summary": "Foundational Time Series Model Multi Series", - "description": "Based on the provided data, this endpoint predicts the future values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for each series based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "v2_forecast_v2_forecast_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/ForecastInput", - "examples": [ - { - "series": { - "sizes": [ - 5, - 3 - ], - "y": [ - 1, - 2, - 3, - 4, - 5, - 10, - 20, - 30 - ] - }, - "h": 2, - "freq": "D" - } - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/ForecastOutput" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "v2/forecast" + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } }, - "/v2/cross_validation": { - "post": { - "summary": "Foundational Time Series Model Multi Series Cross Validation", - "description": "Perform Cross Validation for multiple series", - "operationId": "v2_cross_validation_v2_cross_validation_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/CrossValidationInput", - "examples": [ - { - "series": { - "sizes": [ - 5, - 3 - ], - "y": [ - 1, - 2, - 3, - 4, - 5, - 10, - 20, - 30 - ] - }, - "h": 2, - "n_windows": 1, - "freq": "D" - } - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/CrossValidationOutput" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "v2/cross_validation" + "deprecated": true, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "timegpt", + "x-excluded": true + } + }, + "/timegpt_historic": { + "post": { + "tags": ["excluded"], + "summary": "Foundational Time Series Model Historic (Beta)", + "description": "Based on the provided data, this endpoint predicts time series data for the in-sample period (historical period). It takes a JSON as an input, including information like the series frequency and the historical data. (See below for a full description of the parameters.) The response contains the predicted values for the historical period. Usually useful for anomaly detection. Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "timegpt_historic_timegpt_historic_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SingleSeriesInsampleForecast", + "examples": [ + { + "fh": 7, + "y": { + "2015-12-02": 4.390508031418598, + "2015-12-03": 5.721514930979356, + "2015-12-04": 4.822107008573151, + "2015-12-05": 4.359065463975175, + "2015-12-06": 3.3892383947112377, + "2015-12-07": 5.167152904533249, + "2015-12-08": 3.50069769010154, + "2015-12-09": 7.134184006256638, + "2015-12-10": 7.709302084008234, + "2015-12-11": 3.0675321506062216, + "2015-12-12": 6.333800304661317, + "2015-12-13": 4.231159358023236, + "2015-12-14": 4.5443564887514585, + "2015-12-15": 7.404773106341288, + "2015-12-16": 0.5682884655830955, + "2015-12-17": 0.6970343976123257, + "2015-12-18": 0.16174717952260576, + "2015-12-19": 6.660958764383504, + "2015-12-20": 6.225254007598804, + "2015-12-21": 6.960097185974553, + "2015-12-22": 7.828946737862112, + "2015-12-23": 6.393268513733789, + "2015-12-24": 3.6918348980234548, + "2015-12-25": 6.244233410291644, + "2015-12-26": 0.9461954069514658, + "2015-12-27": 5.119368170620191, + "2015-12-28": 1.1468262992723712, + "2015-12-29": 7.557351336396671, + "2015-12-30": 4.174786574000573, + "2015-12-31": 3.3172955199241887, + "2016-01-01": 2.1164448968370158, + "2016-01-02": 6.193869515473733, + "2016-01-03": 3.6492026577323884, + "2016-01-04": 4.547471590949188, + "2016-01-05": 0.15031840349084113, + "2016-01-06": 4.9410839766070165, + "2016-01-07": 4.896765781779371, + "2016-01-08": 4.935471974998055, + "2016-01-09": 7.549984628116993, + "2016-01-10": 5.454562392827867, + "2016-01-11": 2.876063204590288, + "2016-01-12": 3.4962556303947316, + "2016-01-13": 5.581049567418119 + }, + "freq": "D", + "clean_ex_first": true, + "level": [90], + "model": "timegpt-1" + } + ] + } } + }, + "required": true }, - "/v2/historic_forecast": { - "post": { - "summary": "Foundational Time Series Model Multi Series Historic", - "description": "Based on the provided data, this endpoint predicts the in-sample period (historical period) values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for the historical period. Usually useful for anomaly detection. Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "v2_historic_forecast_v2_historic_forecast_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/InSampleInput", - "examples": [ - { - "series": { - "sizes": [ - 35 - ], - "y": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 0, - 1, - 2, - 10, - 4, - 5, - 6 - ] - }, - "freq": "D" - } - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/InSampleOutput" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "v2/historic_forecast" + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } }, - "/v2/anomaly_detection": { - "post": { - "summary": "Foundational Time Series Model Multi Series Anomaly Detector", - "description": "Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "v2_anomaly_detection_v2_anomaly_detection_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/AnomalyDetectionInput", - "examples": [ - { - "series": { - "sizes": [ - 35 - ], - "y": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 0, - 1, - 2, - 10, - 4, - 5, - 6 - ] - }, - "freq": "D", - "level": 90 - } - ] - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/AnomalyDetectionOutput" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "v2/anomaly_detection" + "deprecated": true, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "timegpt_historic", + "x-excluded": true + } + }, + "/timegpt_multi_series": { + "post": { + "tags": ["excluded"], + "summary": "Foundational Time Series Model Multi Series (Beta)", + "description": "Based on the provided data, this endpoint predicts the future values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for each series based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "timegpt_multi_series_timegpt_multi_series_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MultiSeriesForecast", + "examples": [null] + } } + }, + "required": true }, - "/v2/online_anomaly_detection": { - "post": { - "summary": "Foundational Time Series Model Online Multi Series Anomaly Detector", - "description": "This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "v2_online_anomaly_detection_v2_online_anomaly_detection_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/OnlineAnomalyInput", - "examples": [ - { - "series": { - "sizes": [ - 320 - ], - "y": [ - 12.0, - 12.99833416646828, - 13.986693307950611, - 14.955202066613396, - 15.894183423086506, - 16.794255386042032, - 17.646424733950354, - 18.442176872376912, - 19.173560908995228, - 19.833269096274833, - 20.414709848078964, - 20.912073600614356, - 21.320390859672266, - 21.63558185417193, - 21.854497299884603, - 21.974949866040546, - 21.995736030415053, - 21.916648104524686, - 21.73847630878195, - 21.463000876874144, - 21.092974268256818, - 20.632093666488736, - 20.0849640381959, - 19.457052121767198, - 18.754631805511508, - 17.984721441039564, - 17.15501371821464, - 16.2737988023383, - 15.349881501559047, - 14.39249329213982, - 13.411200080598672, - 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It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for the historical period. Usually useful for anomaly detection. Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "timegpt_multi_series_historic_timegpt_multi_series_historic_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MultiSeriesInsampleForecast", + "examples": [ + { + "y": { + "columns": ["unique_id", "ds", "y"], + "data": [ + ["PeytonManning", "2015-12-02", 4.390508031418598], + ["PeytonManning", "2015-12-03", 5.721514930979356], + ["PeytonManning", "2015-12-04", 4.822107008573151], + ["PeytonManning", "2015-12-05", 4.359065463975175], + ["PeytonManning", "2015-12-06", 3.3892383947112377], + ["PeytonManning", "2015-12-07", 5.167152904533249], + ["PeytonManning", "2015-12-08", 3.50069769010154], + ["PeytonManning", "2015-12-09", 7.134184006256638], + ["PeytonManning", "2015-12-10", 7.709302084008234], + ["PeytonManning", "2015-12-11", 3.0675321506062216], + ["PeytonManning", "2015-12-12", 6.333800304661317], + ["PeytonManning", "2015-12-13", 4.231159358023236], + ["PeytonManning", "2015-12-14", 4.5443564887514585], + ["PeytonManning", "2015-12-15", 7.404773106341288], + ["PeytonManning", "2015-12-16", 0.5682884655830955], + ["PeytonManning", "2015-12-17", 0.6970343976123257], + ["PeytonManning", "2015-12-18", 0.16174717952260576], + ["PeytonManning", "2015-12-19", 6.660958764383504], + ["PeytonManning", "2015-12-20", 6.225254007598804], + ["PeytonManning", "2015-12-21", 6.960097185974553], + ["PeytonManning", "2015-12-22", 7.828946737862112], + ["PeytonManning", "2015-12-23", 6.393268513733789], + ["PeytonManning", "2015-12-24", 3.6918348980234548], + ["PeytonManning", "2015-12-25", 6.244233410291644], + ["PeytonManning", "2015-12-26", 0.9461954069514658], + ["PeytonManning", "2015-12-27", 5.119368170620191], + ["PeytonManning", "2015-12-28", 1.1468262992723712], + ["PeytonManning", "2015-12-29", 7.557351336396671], + ["PeytonManning", "2015-12-30", 4.174786574000573], + ["PeytonManning", "2015-12-31", 3.3172955199241887], + ["PeytonManning", "2016-01-01", 2.1164448968370158], + ["PeytonManning", "2016-01-02", 6.193869515473733], + ["PeytonManning", "2016-01-03", 3.6492026577323884], + ["PeytonManning", "2016-01-04", 4.547471590949188], + ["PeytonManning", "2016-01-05", 0.15031840349084113], + ["PeytonManning", "2016-01-06", 4.9410839766070165], + ["PeytonManning", "2016-01-07", 4.896765781779371], + ["PeytonManning", "2016-01-08", 4.935471974998055], + ["PeytonManning", "2016-01-09", 7.549984628116993], + ["PeytonManning", "2016-01-10", 5.454562392827867], + ["PeytonManning", "2016-01-11", 2.876063204590288], + ["PeytonManning", "2016-01-12", 3.4962556303947316], + ["PeytonManning", "2016-01-13", 5.581049567418119], + ["TomBrady", "2015-12-02", 4.390508031418598], + ["TomBrady", "2015-12-03", 5.721514930979356], + ["TomBrady", "2015-12-04", 4.822107008573151], + ["TomBrady", "2015-12-05", 4.359065463975175], + ["TomBrady", "2015-12-06", 3.3892383947112377], + ["TomBrady", "2015-12-07", 5.167152904533249], + ["TomBrady", "2015-12-08", 3.50069769010154], + ["TomBrady", "2015-12-09", 7.134184006256638], + ["TomBrady", "2015-12-10", 7.709302084008234], + ["TomBrady", "2015-12-11", 3.0675321506062216], + ["TomBrady", "2015-12-12", 6.333800304661317], + ["TomBrady", "2015-12-13", 4.231159358023236], + ["TomBrady", "2015-12-14", 4.5443564887514585], + ["TomBrady", "2015-12-15", 7.404773106341288], + ["TomBrady", "2015-12-16", 0.5682884655830955], + ["TomBrady", "2015-12-17", 0.6970343976123257], + ["TomBrady", "2015-12-18", 0.16174717952260576], + ["TomBrady", "2015-12-19", 6.660958764383504], + ["TomBrady", "2015-12-20", 6.225254007598804], + ["TomBrady", "2015-12-21", 6.960097185974553], + ["TomBrady", "2015-12-22", 7.828946737862112], + ["TomBrady", "2015-12-23", 6.393268513733789], + ["TomBrady", "2015-12-24", 3.6918348980234548], + ["TomBrady", "2015-12-25", 6.244233410291644], + ["TomBrady", "2015-12-26", 0.9461954069514658], + ["TomBrady", "2015-12-27", 5.119368170620191], + ["TomBrady", "2015-12-28", 1.1468262992723712], + ["TomBrady", "2015-12-29", 7.557351336396671], + ["TomBrady", "2015-12-30", 4.174786574000573], + ["TomBrady", "2015-12-31", 3.3172955199241887], + ["TomBrady", "2016-01-01", 2.1164448968370158], + ["TomBrady", "2016-01-02", 6.193869515473733], + ["TomBrady", "2016-01-03", 3.6492026577323884], + ["TomBrady", "2016-01-04", 4.547471590949188], + ["TomBrady", "2016-01-05", 0.15031840349084113], + ["TomBrady", "2016-01-06", 4.9410839766070165], + ["TomBrady", "2016-01-07", 4.896765781779371], + ["TomBrady", "2016-01-08", 4.935471974998055], + ["TomBrady", "2016-01-09", 7.549984628116993], + ["TomBrady", "2016-01-10", 5.454562392827867], + ["TomBrady", "2016-01-11", 2.876063204590288], + ["TomBrady", "2016-01-12", 3.4962556303947316], + ["TomBrady", "2016-01-13", 5.581049567418119] + ] + }, + "freq": "D", + "level": [90], + "model": "timegpt-1" + } + ] + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "deprecated": true, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "timegpt_multi_series_historic", + "x-excluded": true + } + }, + "/timegpt_multi_series_anomalies": { + "post": { + "tags": ["excluded"], + "summary": "Foundational Time Series Model Multi Series Anomaly Detector (Beta)", + "description": "Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "timegpt_multi_series_anomalies_timegpt_multi_series_anomalies_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MultiSeriesAnomaly", + "examples": [null] + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "deprecated": true, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "timegpt_multi_series_anomalies", + "x-excluded": true + } + }, + "/timegpt_multi_series_cross_validation": { + "post": { + "tags": ["excluded"], + "summary": "Foundational Time Series Model Multi Series Cross Validation (Beta)", + "description": "Perform Cross Validation for multiple series", + "operationId": "timegpt_multi_series_cross_validation_timegpt_multi_series_cross_validation_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MultiSeriesCrossValidation", + "examples": [null] + } } + }, + "required": true }, - "/v2/finetune": { - "post": { - "summary": "Foundational Time Series Model Multi Series Finetuning", - "description": "Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://dashboard.nixtla.io.", - "operationId": "v2_finetune_v2_finetune_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/FinetuneInput", - "examples": [ - { - "series": { - "y": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35 - ], - "sizes": [ - 36 - ] - }, - "finetune_steps": 10, - "freq": "MS", - "model": "timegpt-1" - } - ] - } - } + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "deprecated": true, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "timegpt_multi_series_cross_validation", + "x-excluded": true + } + }, + "/v2/forecast": { + "post": { + "summary": "Foundational Time Series Model Multi Series", + "description": "Based on the provided data, this endpoint predicts the future values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for each series based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "v2_forecast_v2_forecast_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ForecastInput", + "examples": [ + { + "series": { + "sizes": [5, 3], + "y": [1, 2, 3, 4, 5, 10, 20, 30] }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/FinetuneOutput" - } - } - } + "h": 2, + "freq": "D" + } + ] + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ForecastOutput" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "v2/forecast" + } + }, + "/v2/cross_validation": { + "post": { + "summary": "Foundational Time Series Model Multi Series Cross Validation", + "description": "Perform Cross Validation for multiple series", + "operationId": "v2_cross_validation_v2_cross_validation_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CrossValidationInput", + "examples": [ + { + "series": { + "sizes": [5, 3], + "y": [1, 2, 3, 4, 5, 10, 20, 30] }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } - ], - "x-fern-sdk-method-name": "v2/finetune" + "h": 2, + "n_windows": 1, + "freq": "D" + } + ] + } } + }, + "required": true }, - "/v2/finetuned_models": { - "get": { - "summary": "List Fine-tuned Models", - "description": "List all the finetuned models that you have created. The response contains a list with the IDs of the models that you have fine-tuned and are available to make forecasts.", - "operationId": "v2_finetuned_models_v2_finetuned_models_get", - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/FinetunedModelsOutput" - } - } - } - } - }, - "security": [ - { - "HTTPBearer": [] - } + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CrossValidationOutput" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "v2/cross_validation" + } + }, + "/v2/historic_forecast": { + "post": { + "summary": "Foundational Time Series Model Multi Series Historic", + "description": "Based on the provided data, this endpoint predicts the in-sample period (historical period) values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for the historical period. Usually useful for anomaly detection. Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "v2_historic_forecast_v2_historic_forecast_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/InSampleInput", + "examples": [ + { + "series": { + "sizes": [35], + "y": [ + 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, + 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 10, 4, 5, 6 + ] + }, + "freq": "D" + } ] + } } + }, + "required": true }, - "/v2/finetuned_models/{finetuned_model_id}": { - "get": { - "summary": "Get single Fine-tuned Model", - "description": "Retrieve metadata for a previously fine-tuned model. The response contains the metadata of a model that you have fine-tuned and is available to make forecasts.", - "operationId": "v2_finetuned_model_v2_finetuned_models__finetuned_model_id__get", - "security": [ - { - "HTTPBearer": [] - } - ], - "parameters": [ - { - "name": "finetuned_model_id", - "in": "path", - "required": true, - "schema": { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", - "title": "Finetuned Model Id" - } - } - ], - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/FinetunedModel" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/InSampleOutput" } - }, - "delete": { - "summary": "Delete Fine-tuned Model", - "description": "Delete a previously saved finetuned model. It takes the ID of the model that you want to delete as a path parameter.", - "operationId": "v2_finetuned_models_delete_v2_finetuned_models__finetuned_model_id__delete", - "security": [ - { - "HTTPBearer": [] - } - ], - "parameters": [ - { - "name": "finetuned_model_id", - "in": "path", - "required": true, - "schema": { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", - "title": "Finetuned Model Id" - } - } - ], - "responses": { - "204": { - "description": "Successful Response" - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/HTTPValidationError" - } - } - } - } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" } + } } - } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "v2/historic_forecast" + } }, - "components": { - "schemas": { - "AnomalyDetectionInput": { - "properties": { + "/v2/anomaly_detection": { + "post": { + "summary": "Foundational Time Series Model Multi Series Anomaly Detector", + "description": "Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "v2_anomaly_detection_v2_anomaly_detection_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/AnomalyDetectionInput", + "examples": [ + { "series": { - "$ref": "#/components/schemas/SeriesWithExogenous" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." - }, - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true - }, - "finetuned_model_id": { - "anyOf": [ - { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" - }, - { - "type": "null" - } - ], - "title": "Finetuned Model Id", - "description": "ID of previously finetuned model" - }, - "level": { - "anyOf": [ - { - "type": "integer", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - }, - { - "type": "number", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - } - ], - "title": "Level", - "description": "Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.", - "default": 99 - } - }, - "type": "object", - "required": [ - "series", - "freq" - ], - "title": "AnomalyDetectionInput" - }, - "AnomalyDetectionOutput": { - "properties": { - "input_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Input Tokens" - }, - "output_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Output Tokens" - }, - "finetune_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Tokens" - }, - "mean": { - "items": { - "type": "number" - }, - "type": "array", - "title": "Mean" - }, - "sizes": { - "items": { - "type": "integer" - }, - "type": "array", - "title": "Sizes" - }, - "intervals": { - "anyOf": [ - { - "additionalProperties": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "object" - }, - { - "type": "null" - } - ], - "title": "Intervals" - }, - "weights_x": { - "anyOf": [ - { - "items": { - "type": "number" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Weights X" - }, - "feature_contributions": { - "anyOf": [ - { - "items": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Feature Contributions" - }, - "anomaly": { - "items": { - "type": "boolean" - }, - "type": "array", - "title": "Anomaly" - } - }, - "type": "object", - "required": [ - "input_tokens", - "output_tokens", - "finetune_tokens", - "mean", - "sizes", - "anomaly" - ], - "title": "AnomalyDetectionOutput" - }, - "CrossValidationInput": { - "properties": { + "sizes": [35], + "y": [ + 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, + 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 10, 4, 5, 6 + ] + }, + "freq": "D", + "level": 90 + } + ] + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/AnomalyDetectionOutput" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "v2/anomaly_detection" + } + }, + "/v2/online_anomaly_detection": { + "post": { + "summary": "Foundational Time Series Model Online Multi Series Anomaly Detector", + "description": "This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "v2_online_anomaly_detection_v2_online_anomaly_detection_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OnlineAnomalyInput", + "examples": [ + { "series": { - "$ref": "#/components/schemas/SeriesWithExogenous" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." - }, - "n_windows": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "N Windows", - "description": "Number of windows to evaluate." - }, - "h": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "H", - "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict." - }, - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true - }, - "level": { - "anyOf": [ - { - "items": { - "anyOf": [ - { - "type": "integer", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - }, - { - "type": "number", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - } - ] - }, - "type": "array", - "minItems": 1 - }, - { - "type": "null" - } - ], - "title": "Level", - "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." - }, - "finetune_steps": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Steps", - "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", - "default": 0 - }, - "finetune_loss": { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape", - "poisson" - ], - "title": "Finetune Loss", - "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", - "default": "default" - }, - "finetune_depth": { - "type": "integer", - "enum": [ - 1, - 2, - 3, - 4, - 5 - ], - "title": "Finetune Depth", - "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. By default, the value is set to 1.", - "default": 1 - }, - "finetuned_model_id": { - "anyOf": [ - { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" - }, - { - "type": "null" - } - ], - "title": "Finetuned Model Id", - "description": "ID of previously finetuned model" - }, - "step_size": { - "anyOf": [ - { - "type": "integer", - "exclusiveMinimum": 0.0 - }, - { - "type": "null" - } - ], - "title": "Step Size", - "description": "Step size between each cross validation window. If None it will be equal to the forecasting horizon." - }, - "hist_exog": { - "anyOf": [ - { - "items": { - "type": "integer", - "minimum": 0.0 - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Hist Exog", - "description": "Zero-based indices of the exogenous features to treat as historical." - }, - "refit": { - "type": "boolean", - "title": "Refit", - "description": "Fine-tune the model in each window. If `False`, only fine-tunes on the first window. Only used if `finetune_steps` > 0.", - "default": true - } - }, - "type": "object", - "required": [ - "series", - "freq", - "n_windows", - "h" - ], - "title": "CrossValidationInput" - }, - "CrossValidationOutput": { - "properties": { - "input_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Input Tokens" - }, - "output_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Output Tokens" - }, - "finetune_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Tokens" - }, - "mean": { - "items": { - "type": "number" - }, - "type": "array", - "title": "Mean" - }, - "sizes": { - "items": { - "type": "integer" - }, - "type": "array", - "title": "Sizes" - }, - "idxs": { - "items": { - "type": "integer" - }, - "type": "array", - "title": "Idxs" - }, - "intervals": { - "anyOf": [ - { - "additionalProperties": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "object" - }, - { - "type": "null" - } - ], - "title": "Intervals" - } - }, - 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"$ref": "#/components/schemas/OnlineAnomalyOutput" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "v2/online_anomaly_detection" + } + }, + "/v2/finetune": { + "post": { + "summary": "Foundational Time Series Model Multi Series Finetuning", + "description": "Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://dashboard.nixtla.io.", + "operationId": "v2_finetune_v2_finetune_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FinetuneInput", + "examples": [ + { "series": { - "$ref": "#/components/schemas/Series" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." - }, - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "finetune_steps": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "Finetune Steps", - "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", - "default": 10 - }, - "finetune_loss": { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape", - "poisson" - ], - "title": "Finetune Loss", - "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", - "default": "default" - }, - "finetune_depth": { - "type": "integer", - "enum": [ - 1, - 2, - 3, - 4, - 5 - ], - "title": "Finetune Depth", - "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. By default, the value is set to 1.", - "default": 1 - }, - "output_model_id": { - "anyOf": [ - { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" - }, - { - "type": "null" - } - ], - "title": "Output Model Id", - "description": "ID to assign to the finetuned model" - }, - "finetuned_model_id": { - "anyOf": [ - { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" - }, - { - "type": "null" - } - ], - "title": "Finetuned Model Id", - "description": "ID of previously finetuned model" - } - }, - "type": "object", - "required": [ - "series", - "freq" - ], - "title": "FinetuneInput" - }, - "FinetuneOutput": { - "properties": { - "input_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Input Tokens" - }, - "output_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Output Tokens" - }, - "finetune_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Tokens" - }, - "finetuned_model_id": { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", - "title": "Finetuned Model Id" - } - }, - "type": "object", - "required": [ - "input_tokens", - "output_tokens", - "finetune_tokens", - "finetuned_model_id" - ], - "title": "FinetuneOutput" + "y": [ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, + 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35 + ], + "sizes": [36] + }, + "finetune_steps": 10, + "freq": "MS", + "model": "timegpt-1" + } + ] + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FinetuneOutput" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ], + "x-fern-sdk-method-name": "v2/finetune" + } + }, + "/v2/finetuned_models": { + "get": { + "summary": "List Fine-tuned Models", + "description": "List all the finetuned models that you have created. The response contains a list with the IDs of the models that you have fine-tuned and are available to make forecasts.", + "operationId": "v2_finetuned_models_v2_finetuned_models_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FinetunedModelsOutput" + } + } + } + } + }, + "security": [ + { + "HTTPBearer": [] + } + ] + } + }, + "/v2/finetuned_models/{finetuned_model_id}": { + "get": { + "summary": "Get single Fine-tuned Model", + "description": "Retrieve metadata for a previously fine-tuned model. The response contains the metadata of a model that you have fine-tuned and is available to make forecasts.", + "operationId": "v2_finetuned_model_v2_finetuned_models__finetuned_model_id__get", + "security": [ + { + "HTTPBearer": [] + } + ], + "parameters": [ + { + "name": "finetuned_model_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", + "title": "Finetuned Model Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/FinetunedModel" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + }, + "delete": { + "summary": "Delete Fine-tuned Model", + "description": "Delete a previously saved finetuned model. It takes the ID of the model that you want to delete as a path parameter.", + "operationId": "v2_finetuned_models_delete_v2_finetuned_models__finetuned_model_id__delete", + "security": [ + { + "HTTPBearer": [] + } + ], + "parameters": [ + { + "name": "finetuned_model_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", + "title": "Finetuned Model Id" + } + } + ], + "responses": { + "204": { + "description": "Successful Response" + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + } + }, + "components": { + "schemas": { + "AnomalyDetectionInput": { + "properties": { + "series": { + "$ref": "#/components/schemas/SeriesWithExogenous" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." + }, + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + }, + "finetuned_model_id": { + "anyOf": [ + { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" + }, + { + "type": "null" + } + ], + "title": "Finetuned Model Id", + "description": "ID of previously finetuned model" + }, + "level": { + "anyOf": [ + { + "type": "integer", + "exclusiveMaximum": 100.0, + "minimum": 0.0 + }, + { + "type": "number", + "exclusiveMaximum": 100.0, + "minimum": 0.0 + } + ], + "title": "Level", + "description": "Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.", + "default": 99 + } + }, + "type": "object", + "required": ["series", "freq"], + "title": "AnomalyDetectionInput" + }, + "AnomalyDetectionOutput": { + "properties": { + "input_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Input Tokens" + }, + "output_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Output Tokens" + }, + "finetune_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Tokens" + }, + "mean": { + "items": { + "type": "number" }, - "FinetunedModel": { - "properties": { - "id": { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", - "title": "Id" - }, - "created_at": { - "type": "string", - "title": "Created At" - }, - "created_by": { - "type": "string", - "title": "Created By", - "default": "user" - }, - "base_model_id": { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", - "title": "Base Model Id" - }, - "steps": { - "type": "integer", - "title": "Steps" - }, - "depth": { - "type": "integer", - "title": "Depth" - }, - "loss": { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape", - "poisson" - ], - "title": "Loss" - }, - "model": { - "type": "string", - "title": "Model" - }, - "freq": { - "type": "string", - "title": "Freq" - } - }, - "type": "object", - "required": [ - "id", - "created_at", - "base_model_id", - "steps", - "depth", - "loss", - "model", - "freq" - ], - "title": "FinetunedModel" + "type": "array", + "title": "Mean" + }, + "sizes": { + "items": { + "type": "integer" }, - "FinetunedModelsOutput": { - "properties": { - "finetuned_models": { - "items": { - "$ref": "#/components/schemas/FinetunedModel" - }, - "type": "array", - "title": "Finetuned Models" - } + "type": "array", + "title": "Sizes" + }, + "intervals": { + "anyOf": [ + { + "additionalProperties": { + "items": { + "type": "number" + }, + "type": "array" }, - "type": "object", - "required": [ - "finetuned_models" - ], - "title": "FinetunedModelsOutput" - }, - "ForecastInput": { - "properties": { - "series": { - "$ref": "#/components/schemas/SeriesWithFutureExogenous" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." - }, - "h": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "H", - "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict." - }, - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true - }, - "level": { - "anyOf": [ - { - "items": { - "anyOf": [ - { - "type": "integer", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - }, - { - "type": "number", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - } - ] - }, - "type": "array", - "minItems": 1 - }, - { - "type": "null" - } - ], - "title": "Level", - "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." - }, - "finetune_steps": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Steps", - "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", - "default": 0 - }, - "finetune_loss": { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape", - "poisson" - ], - "title": "Finetune Loss", - "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", - "default": "default" - }, - "finetune_depth": { - "type": "integer", - "enum": [ - 1, - 2, - 3, - 4, - 5 - ], - "title": "Finetune Depth", - "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. By default, the value is set to 1.", - "default": 1 - }, - "finetuned_model_id": { - "anyOf": [ - { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" - }, - { - "type": "null" - } - ], - "title": "Finetuned Model Id", - "description": "ID of previously finetuned model" - }, - "feature_contributions": { - "type": "boolean", - "title": "Feature Contributions", - "description": "Compute the exogenous features contributions to the forecast.", - "default": false - } + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Intervals" + }, + "weights_x": { + "anyOf": [ + { + "items": { + "type": "number" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Weights X" + }, + "feature_contributions": { + "anyOf": [ + { + "items": { + "items": { + "type": "number" + }, + "type": "array" }, - "type": "object", - "required": [ - "series", - "freq", - "h" - ], - "title": "ForecastInput" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Feature Contributions" + }, + "anomaly": { + "items": { + "type": "boolean" }, - "ForecastOutput": { - "properties": { - "input_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Input Tokens" - }, - "output_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Output Tokens" - }, - "finetune_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Tokens" - }, - "mean": { - "items": { - "type": "number" - }, - "type": "array", - "title": "Mean" - }, - "intervals": { - "anyOf": [ - { - "additionalProperties": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "object" - }, - { - "type": "null" - } - ], - "title": "Intervals" - }, - "weights_x": { - "anyOf": [ - { - "items": { - "type": "number" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Weights X" + "type": "array", + "title": "Anomaly" + } + }, + "type": "object", + "required": [ + "input_tokens", + "output_tokens", + "finetune_tokens", + "mean", + "sizes", + "anomaly" + ], + "title": "AnomalyDetectionOutput" + }, + "CrossValidationInput": { + "properties": { + "series": { + "$ref": "#/components/schemas/SeriesWithExogenous" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." + }, + "n_windows": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "N Windows", + "description": "Number of windows to evaluate." + }, + "h": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "H", + "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict." + }, + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + }, + "level": { + "anyOf": [ + { + "items": { + "anyOf": [ + { + "type": "integer", + "exclusiveMaximum": 100.0, + "minimum": 0.0 }, - "feature_contributions": { - "anyOf": [ - { - "items": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Feature Contributions" + { + "type": "number", + "exclusiveMaximum": 100.0, + "minimum": 0.0 } + ] }, - "type": "object", - "required": [ - "input_tokens", - "output_tokens", - "finetune_tokens", - "mean" - ], - "title": "ForecastOutput" - }, - "HTTPValidationError": { - "properties": { - "detail": { - "items": { - "$ref": "#/components/schemas/ValidationError" - }, - "type": "array", - "title": "Detail" - } + "type": "array", + "minItems": 1 + }, + { + "type": "null" + } + ], + "title": "Level", + "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." + }, + "finetune_steps": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Steps", + "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", + "default": 0 + }, + "finetune_loss": { + "type": "string", + "enum": [ + "default", + "mae", + "mse", + "rmse", + "mape", + "smape", + "poisson" + ], + "title": "Finetune Loss", + "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", + "default": "default" + }, + "finetune_depth": { + "type": "integer", + "enum": [1, 2, 3, 4, 5], + "title": "Finetune Depth", + "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. By default, the value is set to 1.", + "default": 1 + }, + "finetuned_model_id": { + "anyOf": [ + { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" + }, + { + "type": "null" + } + ], + "title": "Finetuned Model Id", + "description": "ID of previously finetuned model" + }, + "step_size": { + "anyOf": [ + { + "type": "integer", + "exclusiveMinimum": 0.0 + }, + { + "type": "null" + } + ], + "title": "Step Size", + "description": "Step size between each cross validation window. If None it will be equal to the forecasting horizon." + }, + "hist_exog": { + "anyOf": [ + { + "items": { + "type": "integer", + "minimum": 0.0 }, - "type": "object", - "title": "HTTPValidationError" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Hist Exog", + "description": "Zero-based indices of the exogenous features to treat as historical." + }, + "refit": { + "type": "boolean", + "title": "Refit", + "description": "Fine-tune the model in each window. If `False`, only fine-tunes on the first window. Only used if `finetune_steps` > 0.", + "default": true + } + }, + "type": "object", + "required": ["series", "freq", "n_windows", "h"], + "title": "CrossValidationInput" + }, + "CrossValidationOutput": { + "properties": { + "input_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Input Tokens" + }, + "output_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Output Tokens" + }, + "finetune_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Tokens" + }, + "mean": { + "items": { + "type": "number" }, - "InSampleInput": { - "properties": { - "series": { - "$ref": "#/components/schemas/SeriesWithExogenous" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." - }, - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true - }, - "finetuned_model_id": { - "anyOf": [ - { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" - }, - { - "type": "null" - } - ], - "title": "Finetuned Model Id", - "description": "ID of previously finetuned model" - }, - "level": { - "anyOf": [ - { - "items": { - "anyOf": [ - { - "type": "integer", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - }, - { - "type": "number", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - } - ] - }, - "type": "array", - "minItems": 1 - }, - { - "type": "null" - } - ], - "title": "Level", - "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." - }, - "feature_contributions": { - "type": "boolean", - "title": "Feature Contributions", - "description": "Compute the exogenous features contributions to the forecast.", - "default": false - } - }, - "type": "object", - "required": [ - "series", - "freq" - ], - "title": "InSampleInput" + "type": "array", + "title": "Mean" + }, + "sizes": { + "items": { + "type": "integer" }, - "InSampleOutput": { - "properties": { - "input_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Input Tokens" - }, - "output_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Output Tokens" - }, - "finetune_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Tokens" - }, - "mean": { - "items": { - "type": "number" - }, - "type": "array", - "title": "Mean" - }, - "sizes": { - "items": { - "type": "integer" - }, - "type": "array", - "title": "Sizes" - }, - "intervals": { - "anyOf": [ - { - "additionalProperties": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "object" - }, - { - "type": "null" - } - ], - "title": "Intervals" - }, - "weights_x": { - "anyOf": [ - { - "items": { - "type": "number" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Weights X" - }, - "feature_contributions": { - "anyOf": [ - { - "items": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Feature Contributions" - } - }, - "type": "object", - "required": [ - "input_tokens", - "output_tokens", - "finetune_tokens", - "mean", - "sizes" - ], - "title": "InSampleOutput" + "type": "array", + "title": "Sizes" + }, + "idxs": { + "items": { + "type": "integer" }, - "MultiSeriesAnomaly": { - "properties": { - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", - "default": "D" - }, - "level": { - "items": {}, - "type": "array", - "title": "Level", - "description": "Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.", - "default": [ - 99 - ] - }, - "y": { - "title": "Y", - "description": "The historical time series data provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"y\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.7], [\"ts_0\", \"2021-01-02\", 0.8]}.", - "default": { - "columns": [ - "unique_id", - "ds", - "y" - ], - "data": [ - [ - "PeytonManning", - "2015-12-02", - 4.390508031418598 - ], - [ - "PeytonManning", - "2015-12-03", - 5.721514930979356 - ], - [ - "PeytonManning", - "2015-12-04", - 4.822107008573151 - ], - [ - "PeytonManning", - "2015-12-05", - 4.359065463975175 - ], - [ - "PeytonManning", - "2015-12-06", - 3.3892383947112377 - ], - [ - "PeytonManning", - "2015-12-07", - 5.167152904533249 - ], - [ - "PeytonManning", - "2015-12-08", - 3.50069769010154 - ], - [ - "PeytonManning", - "2015-12-09", - 7.134184006256638 - ], - [ - "PeytonManning", - "2015-12-10", - 7.709302084008234 - ], - [ - "PeytonManning", - "2015-12-11", - 3.0675321506062216 - ], - [ - "PeytonManning", - "2015-12-12", - 6.333800304661317 - ], - [ - "PeytonManning", - "2015-12-13", - 4.231159358023236 - ], - [ - "PeytonManning", - "2015-12-14", - 4.5443564887514585 - ], - [ - "PeytonManning", - "2015-12-15", - 7.404773106341288 - ], - [ - "PeytonManning", - "2015-12-16", - 0.5682884655830955 - ], - [ - "PeytonManning", - "2015-12-17", - 0.6970343976123257 - ], - [ - "PeytonManning", - "2015-12-18", - 0.16174717952260576 - ], - [ - "PeytonManning", - "2015-12-19", - 6.660958764383504 - ], - [ - "PeytonManning", - "2015-12-20", - 6.225254007598804 - ], - [ - "PeytonManning", - "2015-12-21", - 6.960097185974553 - ], - [ - "PeytonManning", - "2015-12-22", - 7.828946737862112 - ], - [ - "PeytonManning", - "2015-12-23", - 6.393268513733789 - ], - [ - "PeytonManning", - "2015-12-24", - 3.6918348980234548 - ], - [ - "PeytonManning", - "2015-12-25", - 6.244233410291644 - ], - [ - "PeytonManning", - "2015-12-26", - 0.9461954069514658 - ], - [ - "PeytonManning", - "2015-12-27", - 5.119368170620191 - ], - [ - "PeytonManning", - "2015-12-28", - 1.1468262992723712 - ], - [ - "PeytonManning", - "2015-12-29", - 7.557351336396671 - ], - [ - "PeytonManning", - "2015-12-30", - 4.174786574000573 - ], - [ - "PeytonManning", - "2015-12-31", - 3.3172955199241887 - ], - [ - "PeytonManning", - "2016-01-01", - 2.1164448968370158 - ], - [ - "PeytonManning", - "2016-01-02", - 6.193869515473733 - ], - [ - "PeytonManning", - "2016-01-03", - 3.6492026577323884 - ], - [ - "PeytonManning", - "2016-01-04", - 4.547471590949188 - ], - [ - "PeytonManning", - "2016-01-05", - 0.15031840349084113 - ], - [ - "PeytonManning", - "2016-01-06", - 4.9410839766070165 - ], - [ - "PeytonManning", - "2016-01-07", - 4.896765781779371 - ], - [ - "PeytonManning", - "2016-01-08", - 4.935471974998055 - ], - [ - "PeytonManning", - "2016-01-09", - 7.549984628116993 - ], - [ - "PeytonManning", - "2016-01-10", - 5.454562392827867 - ], - [ - "PeytonManning", - "2016-01-11", - 2.876063204590288 - ], - [ - "PeytonManning", - "2016-01-12", - 3.4962556303947316 - ], - [ - "PeytonManning", - "2016-01-13", - 5.581049567418119 - ], - [ - "TomBrady", - "2015-12-02", - 4.390508031418598 - ], - [ - "TomBrady", - "2015-12-03", - 5.721514930979356 - ], - [ - "TomBrady", - "2015-12-04", - 4.822107008573151 - ], - [ - "TomBrady", - "2015-12-05", - 4.359065463975175 - ], - [ - "TomBrady", - "2015-12-06", - 3.3892383947112377 - ], - [ - "TomBrady", - "2015-12-07", - 5.167152904533249 - ], - [ - "TomBrady", - "2015-12-08", - 3.50069769010154 - ], - [ - "TomBrady", - "2015-12-09", - 7.134184006256638 - ], - [ - "TomBrady", - "2015-12-10", - 7.709302084008234 - ], - [ - "TomBrady", - "2015-12-11", - 3.0675321506062216 - ], - [ - "TomBrady", - "2015-12-12", - 6.333800304661317 - ], - [ - "TomBrady", - "2015-12-13", - 4.231159358023236 - ], - [ - "TomBrady", - "2015-12-14", - 4.5443564887514585 - ], - [ - "TomBrady", - "2015-12-15", - 7.404773106341288 - ], - [ - "TomBrady", - "2015-12-16", - 0.5682884655830955 - ], - [ - "TomBrady", - "2015-12-17", - 0.6970343976123257 - ], - [ - "TomBrady", - "2015-12-18", - 0.16174717952260576 - ], - [ - "TomBrady", - "2015-12-19", - 6.660958764383504 - ], - [ - "TomBrady", - "2015-12-20", - 6.225254007598804 - ], - [ - "TomBrady", - "2015-12-21", - 6.960097185974553 - ], - [ - "TomBrady", - "2015-12-22", - 7.828946737862112 - ], - [ - "TomBrady", - "2015-12-23", - 6.393268513733789 - ], - [ - "TomBrady", - "2015-12-24", - 3.6918348980234548 - ], - [ - "TomBrady", - "2015-12-25", - 6.244233410291644 - ], - [ - "TomBrady", - "2015-12-26", - 0.9461954069514658 - ], - [ - "TomBrady", - "2015-12-27", - 5.119368170620191 - ], - [ - "TomBrady", - "2015-12-28", - 1.1468262992723712 - ], - [ - "TomBrady", - "2015-12-29", - 7.557351336396671 - ], - [ - "TomBrady", - "2015-12-30", - 4.174786574000573 - ], - [ - "TomBrady", - "2015-12-31", - 3.3172955199241887 - ], - [ - "TomBrady", - "2016-01-01", - 2.1164448968370158 - ], - [ - "TomBrady", - "2016-01-02", - 6.193869515473733 - ], - [ - "TomBrady", - "2016-01-03", - 3.6492026577323884 - ], - [ - "TomBrady", - "2016-01-04", - 4.547471590949188 - ], - [ - "TomBrady", - "2016-01-05", - 0.15031840349084113 - ], - [ - "TomBrady", - "2016-01-06", - 4.9410839766070165 - ], - [ - "TomBrady", - "2016-01-07", - 4.896765781779371 - ], - [ - "TomBrady", - "2016-01-08", - 4.935471974998055 - ], - [ - "TomBrady", - "2016-01-09", - 7.549984628116993 - ], - [ - "TomBrady", - "2016-01-10", - 5.454562392827867 - ], - [ - "TomBrady", - "2016-01-11", - 2.876063204590288 - ], - [ - "TomBrady", - "2016-01-12", - 3.4962556303947316 - ], - [ - "TomBrady", - "2016-01-13", - 5.581049567418119 - ] - ] - } - }, - "x": { - "anyOf": [ - { - "$ref": "#/components/schemas/MultiSeriesInput" - }, - { - "type": "null" - } - ], - "description": "The exogenous variables provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"ex_1\", \"ex_2\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.2, 0.67], [\"ts_0\", \"2021-01-02\", 0.4, 0.7]}. This should also include forecasting horizon (fh) additional timestamps for each unique_id to calculate the future values." - }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true - } + "type": "array", + "title": "Idxs" + }, + "intervals": { + "anyOf": [ + { + "additionalProperties": { + "items": { + "type": "number" + }, + "type": "array" }, - "type": "object", - "title": "MultiSeriesAnomaly" + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Intervals" + } + }, + "type": "object", + "required": [ + "input_tokens", + "output_tokens", + "finetune_tokens", + "mean", + "sizes", + "idxs" + ], + "title": "CrossValidationOutput" + }, + "FinetuneInput": { + "properties": { + "series": { + "$ref": "#/components/schemas/Series" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." + }, + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "finetune_steps": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "Finetune Steps", + "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", + "default": 10 + }, + "finetune_loss": { + "type": "string", + "enum": [ + "default", + "mae", + "mse", + "rmse", + "mape", + "smape", + "poisson" + ], + "title": "Finetune Loss", + "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", + "default": "default" + }, + "finetune_depth": { + "type": "integer", + "enum": [1, 2, 3, 4, 5], + "title": "Finetune Depth", + "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. By default, the value is set to 1.", + "default": 1 + }, + "output_model_id": { + "anyOf": [ + { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" + }, + { + "type": "null" + } + ], + "title": "Output Model Id", + "description": "ID to assign to the finetuned model" + }, + "finetuned_model_id": { + "anyOf": [ + { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" + }, + { + "type": "null" + } + ], + "title": "Finetuned Model Id", + "description": "ID of previously finetuned model" + } + }, + "type": "object", + "required": ["series", "freq"], + "title": "FinetuneInput" + }, + "FinetuneOutput": { + "properties": { + "input_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Input Tokens" + }, + "output_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Output Tokens" + }, + "finetune_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Tokens" + }, + "finetuned_model_id": { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", + "title": "Finetuned Model Id" + } + }, + "type": "object", + "required": [ + "input_tokens", + "output_tokens", + "finetune_tokens", + "finetuned_model_id" + ], + "title": "FinetuneOutput" + }, + "FinetunedModel": { + "properties": { + "id": { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", + "title": "Id" + }, + "created_at": { + "type": "string", + "title": "Created At" + }, + "created_by": { + "type": "string", + "title": "Created By", + "default": "user" + }, + "base_model_id": { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$", + "title": "Base Model Id" + }, + "steps": { + "type": "integer", + "title": "Steps" + }, + "depth": { + "type": "integer", + "title": "Depth" + }, + "loss": { + "type": "string", + "enum": [ + "default", + "mae", + "mse", + "rmse", + "mape", + "smape", + "poisson" + ], + "title": "Loss" + }, + "model": { + "type": "string", + "title": "Model" + }, + "freq": { + "type": "string", + "title": "Freq" + } + }, + "type": "object", + "required": [ + "id", + "created_at", + "base_model_id", + "steps", + "depth", + "loss", + "model", + "freq" + ], + "title": "FinetunedModel" + }, + "FinetunedModelsOutput": { + "properties": { + "finetuned_models": { + "items": { + "$ref": "#/components/schemas/FinetunedModel" }, - "MultiSeriesCrossValidation": { - "properties": { - "fewshot_steps": { - "anyOf": [ - { - "type": "integer" - }, - { - "type": "null" - } - ], - "title": "Fewshot Steps", - "description": "Deprecated. Please use finetune_steps instead.", - "deprecated": true - }, - "fewshot_loss": { - "anyOf": [ - { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape" - ] - }, - { - "type": "null" - } - ], - "title": "Fewshot Loss", - "description": "Deprecated. Please use finetune_loss instead.", - "deprecated": true - }, - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", - "default": "D" - }, - "level": { - "anyOf": [ - { - "items": { - "anyOf": [ - { - "type": "integer" - }, - { - "type": "number" - } - ] - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Level", - "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." - }, - "fh": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "Fh", - "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict.", - "default": 7 - }, - "y": { - "title": "Y", - "description": "The historical time series data provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"y\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.7], [\"ts_0\", \"2021-01-02\", 0.8]}.", - "default": { - "columns": [ - "unique_id", - "ds", - "y" - ], - "data": [ - [ - "PeytonManning", - "2015-12-02", - 4.390508031418598 - ], - [ - "PeytonManning", - "2015-12-03", - 5.721514930979356 - ], - [ - "PeytonManning", - "2015-12-04", - 4.822107008573151 - ], - [ - "PeytonManning", - "2015-12-05", - 4.359065463975175 - ], - [ - "PeytonManning", - "2015-12-06", - 3.3892383947112377 - ], - [ - "PeytonManning", - "2015-12-07", - 5.167152904533249 - ], - [ - "PeytonManning", - "2015-12-08", - 3.50069769010154 - ], - [ - "PeytonManning", - "2015-12-09", - 7.134184006256638 - ], - [ - "PeytonManning", - "2015-12-10", - 7.709302084008234 - ], - [ - "PeytonManning", - "2015-12-11", - 3.0675321506062216 - ], - [ - "PeytonManning", - "2015-12-12", - 6.333800304661317 - ], - [ - "PeytonManning", - "2015-12-13", - 4.231159358023236 - ], - [ - "PeytonManning", - "2015-12-14", - 4.5443564887514585 - ], - [ - "PeytonManning", - "2015-12-15", - 7.404773106341288 - ], - [ - "PeytonManning", - "2015-12-16", - 0.5682884655830955 - ], - [ - "PeytonManning", - "2015-12-17", - 0.6970343976123257 - ], - [ - "PeytonManning", - "2015-12-18", - 0.16174717952260576 - ], - [ - "PeytonManning", - "2015-12-19", - 6.660958764383504 - ], - [ - "PeytonManning", - "2015-12-20", - 6.225254007598804 - ], - [ - "PeytonManning", - "2015-12-21", - 6.960097185974553 - ], - [ - "PeytonManning", - "2015-12-22", - 7.828946737862112 - ], - [ - "PeytonManning", - "2015-12-23", - 6.393268513733789 - ], - [ - "PeytonManning", - "2015-12-24", - 3.6918348980234548 - ], - [ - "PeytonManning", - "2015-12-25", - 6.244233410291644 - ], - [ - "PeytonManning", - "2015-12-26", - 0.9461954069514658 - ], - [ - "PeytonManning", - "2015-12-27", - 5.119368170620191 - ], - [ - "PeytonManning", - "2015-12-28", - 1.1468262992723712 - ], - [ - "PeytonManning", - "2015-12-29", - 7.557351336396671 - 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The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"ex_1\", \"ex_2\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.2, 0.67], [\"ts_0\", \"2021-01-02\", 0.4, 0.7]}. This should also include forecasting horizon (fh) additional timestamps for each unique_id to calculate the future values." - }, - "n_windows": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "N Windows", - "description": "Number of windows to evaluate.", - "default": 1 - }, - "step_size": { - "anyOf": [ - { - "type": "integer", - "exclusiveMinimum": 0.0 - }, - { - "type": "null" - } - ], - "title": "Step Size", - "description": "Step size between each cross validation window. If None it will be equal to the forecasting horizon." - }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true - }, - "finetune_steps": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Steps", - "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", - "default": 0 + "type": "array", + "title": "Finetuned Models" + } + }, + "type": "object", + "required": ["finetuned_models"], + "title": "FinetunedModelsOutput" + }, + "ForecastInput": { + "properties": { + "series": { + "$ref": "#/components/schemas/SeriesWithFutureExogenous" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." + }, + "h": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "H", + "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict." + }, + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + }, + "level": { + "anyOf": [ + { + "items": { + "anyOf": [ + { + "type": "integer", + "exclusiveMaximum": 100.0, + "minimum": 0.0 }, - "finetune_loss": { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape" - ], - "title": "Finetune Loss", - "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", - "default": "default" + { + "type": "number", + "exclusiveMaximum": 100.0, + "minimum": 0.0 } + ] }, - "type": "object", - "title": "MultiSeriesCrossValidation" + "type": "array", + "minItems": 1 + }, + { + "type": "null" + } + ], + "title": "Level", + "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." + }, + "finetune_steps": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Steps", + "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", + "default": 0 + }, + "finetune_loss": { + "type": "string", + "enum": [ + "default", + "mae", + "mse", + "rmse", + "mape", + "smape", + "poisson" + ], + "title": "Finetune Loss", + "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", + "default": "default" + }, + "finetune_depth": { + "type": "integer", + "enum": [1, 2, 3, 4, 5], + "title": "Finetune Depth", + "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. By default, the value is set to 1.", + "default": 1 + }, + "finetuned_model_id": { + "anyOf": [ + { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" + }, + { + "type": "null" + } + ], + "title": "Finetuned Model Id", + "description": "ID of previously finetuned model" + }, + "feature_contributions": { + "type": "boolean", + "title": "Feature Contributions", + "description": "Compute the exogenous features contributions to the forecast.", + "default": false + } + }, + "type": "object", + "required": ["series", "freq", "h"], + "title": "ForecastInput" + }, + "ForecastOutput": { + "properties": { + "input_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Input Tokens" + }, + "output_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Output Tokens" + }, + "finetune_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Tokens" + }, + "mean": { + "items": { + "type": "number" }, - "MultiSeriesForecast": { - "properties": { - "fewshot_steps": { - "anyOf": [ - { - "type": "integer" - }, - { - "type": "null" - } - ], - "title": "Fewshot Steps", - "description": "Deprecated. Please use finetune_steps instead.", - "deprecated": true - }, - "fewshot_loss": { - "anyOf": [ - { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape" - ] - }, - { - "type": "null" - } - ], - "title": "Fewshot Loss", - "description": "Deprecated. Please use finetune_loss instead.", - "deprecated": true - }, - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", - "default": "D" - }, - "level": { - "anyOf": [ - { - "items": { - "anyOf": [ - { - "type": "integer" - }, - { - "type": "number" - } - ] - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Level", - "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." - }, - "fh": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "Fh", - "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict.", - "default": 7 - }, - "y": { - "title": "Y", - "description": "The historical time series data provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"y\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.7], [\"ts_0\", \"2021-01-02\", 0.8]}.", - "default": { - "columns": [ - "unique_id", - "ds", - "y" - ], - "data": [ - [ - "PeytonManning", - "2015-12-02", - 4.390508031418598 - ], - [ - "PeytonManning", - "2015-12-03", - 5.721514930979356 - ], - [ - "PeytonManning", - "2015-12-04", - 4.822107008573151 - ], - [ - "PeytonManning", - "2015-12-05", - 4.359065463975175 - ], - [ - "PeytonManning", - "2015-12-06", - 3.3892383947112377 - ], - [ - "PeytonManning", - "2015-12-07", - 5.167152904533249 - ], - [ - "PeytonManning", - "2015-12-08", - 3.50069769010154 - ], - [ - "PeytonManning", - "2015-12-09", - 7.134184006256638 - ], - [ - "PeytonManning", - "2015-12-10", - 7.709302084008234 - ], - [ - "PeytonManning", - "2015-12-11", - 3.0675321506062216 - ], - [ - "PeytonManning", - "2015-12-12", - 6.333800304661317 - ], - [ - "PeytonManning", - "2015-12-13", - 4.231159358023236 - ], - [ - "PeytonManning", - 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The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"ex_1\", \"ex_2\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.2, 0.67], [\"ts_0\", \"2021-01-02\", 0.4, 0.7]}. This should also include forecasting horizon (fh) additional timestamps for each unique_id to calculate the future values." - }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true - }, - "finetune_steps": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Steps", - "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", - "default": 0 - }, - "finetune_loss": { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape" - ], - "title": "Finetune Loss", - "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", - "default": "default" - } + "type": "array", + "title": "Mean" + }, + "intervals": { + "anyOf": [ + { + "additionalProperties": { + "items": { + "type": "number" + }, + "type": "array" }, - "type": "object", - "title": "MultiSeriesForecast" - }, - "MultiSeriesInput": { - "properties": { - "columns": { - "items": { - "type": "string" - }, - "type": "array", - "title": "Columns" - }, - "data": { - "items": {}, - "type": "array", - "title": "Data" - } + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Intervals" + }, + "weights_x": { + "anyOf": [ + { + "items": { + "type": "number" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Weights X" + }, + "feature_contributions": { + "anyOf": [ + { + "items": { + "items": { + "type": "number" + }, + "type": "array" }, - "type": "object", - "required": [ - "columns", - "data" - ], - "title": "MultiSeriesInput" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Feature Contributions" + } + }, + "type": "object", + "required": [ + "input_tokens", + "output_tokens", + "finetune_tokens", + "mean" + ], + "title": "ForecastOutput" + }, + "HTTPValidationError": { + "properties": { + "detail": { + "items": { + "$ref": "#/components/schemas/ValidationError" }, - "MultiSeriesInsampleForecast": { - "properties": { - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", - "default": "D" - }, - "level": { - "anyOf": [ - { - "items": { - "anyOf": [ - { - "type": "integer" - }, - { - "type": "number" - } - ] - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Level", - "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." - }, - "y": { - "title": "Y", - "description": "The historical time series data provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"y\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.7], [\"ts_0\", \"2021-01-02\", 0.8]}.", - "default": { - "columns": [ - "unique_id", - "ds", - "y" - ], - "data": [ - [ - "PeytonManning", - "2015-12-02", - 4.390508031418598 - ], - [ - "PeytonManning", - "2015-12-03", - 5.721514930979356 - ], - [ - "PeytonManning", - "2015-12-04", - 4.822107008573151 - ], - [ - "PeytonManning", - "2015-12-05", - 4.359065463975175 - ], - [ - "PeytonManning", - "2015-12-06", - 3.3892383947112377 - ], - [ - "PeytonManning", - "2015-12-07", - 5.167152904533249 - ], - [ - "PeytonManning", - "2015-12-08", - 3.50069769010154 - ], - [ - "PeytonManning", - "2015-12-09", - 7.134184006256638 - ], - [ - "PeytonManning", - "2015-12-10", - 7.709302084008234 - ], - [ - "PeytonManning", - "2015-12-11", - 3.0675321506062216 - ], - [ - "PeytonManning", - "2015-12-12", - 6.333800304661317 - ], - [ - "PeytonManning", - "2015-12-13", - 4.231159358023236 - ], - [ - "PeytonManning", - "2015-12-14", - 4.5443564887514585 - ], - [ - "PeytonManning", - "2015-12-15", - 7.404773106341288 - ], - [ - "PeytonManning", - "2015-12-16", - 0.5682884655830955 - ], - [ - "PeytonManning", - "2015-12-17", - 0.6970343976123257 - ], - [ - "PeytonManning", - "2015-12-18", - 0.16174717952260576 - ], - [ - "PeytonManning", - "2015-12-19", - 6.660958764383504 - ], - [ - "PeytonManning", - "2015-12-20", - 6.225254007598804 - ], - [ - "PeytonManning", - "2015-12-21", - 6.960097185974553 - ], - [ - "PeytonManning", - "2015-12-22", - 7.828946737862112 - ], - [ - "PeytonManning", - "2015-12-23", - 6.393268513733789 - ], - [ - "PeytonManning", - "2015-12-24", - 3.6918348980234548 - ], - [ - "PeytonManning", - "2015-12-25", - 6.244233410291644 - ], - [ - "PeytonManning", - "2015-12-26", - 0.9461954069514658 - ], - [ - "PeytonManning", - "2015-12-27", - 5.119368170620191 - ], - [ - "PeytonManning", - "2015-12-28", - 1.1468262992723712 - ], - [ - "PeytonManning", - "2015-12-29", - 7.557351336396671 - ], - [ - "PeytonManning", - "2015-12-30", - 4.174786574000573 - ], - [ - "PeytonManning", - "2015-12-31", - 3.3172955199241887 - ], - [ - "PeytonManning", - "2016-01-01", - 2.1164448968370158 - ], - [ - "PeytonManning", - "2016-01-02", - 6.193869515473733 - ], - [ - "PeytonManning", - "2016-01-03", - 3.6492026577323884 - ], - [ - "PeytonManning", - "2016-01-04", - 4.547471590949188 - ], - [ - "PeytonManning", - "2016-01-05", - 0.15031840349084113 - ], - [ - "PeytonManning", - "2016-01-06", - 4.9410839766070165 - ], - [ - "PeytonManning", - "2016-01-07", - 4.896765781779371 - ], - [ - "PeytonManning", - "2016-01-08", - 4.935471974998055 - ], - [ - "PeytonManning", - "2016-01-09", - 7.549984628116993 - ], - [ - "PeytonManning", - "2016-01-10", - 5.454562392827867 - ], - [ - "PeytonManning", - "2016-01-11", - 2.876063204590288 - ], - [ - "PeytonManning", - "2016-01-12", - 3.4962556303947316 - ], - [ - "PeytonManning", - "2016-01-13", - 5.581049567418119 - ], - [ - "TomBrady", - "2015-12-02", - 4.390508031418598 - ], - [ - "TomBrady", - "2015-12-03", - 5.721514930979356 - ], - [ - "TomBrady", - "2015-12-04", - 4.822107008573151 - ], - [ - "TomBrady", - "2015-12-05", - 4.359065463975175 - ], - [ - "TomBrady", - "2015-12-06", - 3.3892383947112377 - ], - [ - "TomBrady", - "2015-12-07", - 5.167152904533249 - ], - [ - "TomBrady", - "2015-12-08", - 3.50069769010154 - ], - [ - "TomBrady", - "2015-12-09", - 7.134184006256638 - ], - [ - "TomBrady", - "2015-12-10", - 7.709302084008234 - ], - [ - "TomBrady", - "2015-12-11", - 3.0675321506062216 - ], - [ - "TomBrady", - "2015-12-12", - 6.333800304661317 - ], - [ - "TomBrady", - "2015-12-13", - 4.231159358023236 - ], - [ - "TomBrady", - "2015-12-14", - 4.5443564887514585 - ], - [ - "TomBrady", - "2015-12-15", - 7.404773106341288 - ], - [ - "TomBrady", - "2015-12-16", - 0.5682884655830955 - ], - [ - "TomBrady", - "2015-12-17", - 0.6970343976123257 - ], - [ - "TomBrady", - "2015-12-18", - 0.16174717952260576 - ], - [ - "TomBrady", - "2015-12-19", - 6.660958764383504 - ], - [ - "TomBrady", - "2015-12-20", - 6.225254007598804 - ], - [ - "TomBrady", - "2015-12-21", - 6.960097185974553 - ], - [ - "TomBrady", - "2015-12-22", - 7.828946737862112 - ], - [ - "TomBrady", - "2015-12-23", - 6.393268513733789 - ], - [ - "TomBrady", - "2015-12-24", - 3.6918348980234548 - ], - [ - "TomBrady", - "2015-12-25", - 6.244233410291644 - ], - [ - "TomBrady", - "2015-12-26", - 0.9461954069514658 - ], - [ - "TomBrady", - "2015-12-27", - 5.119368170620191 - ], - [ - "TomBrady", - "2015-12-28", - 1.1468262992723712 - ], - [ - "TomBrady", - "2015-12-29", - 7.557351336396671 - ], - [ - "TomBrady", - "2015-12-30", - 4.174786574000573 - ], - [ - "TomBrady", - "2015-12-31", - 3.3172955199241887 - ], - [ - "TomBrady", - "2016-01-01", - 2.1164448968370158 - ], - [ - "TomBrady", - "2016-01-02", - 6.193869515473733 - ], - [ - "TomBrady", - "2016-01-03", - 3.6492026577323884 - ], - [ - "TomBrady", - "2016-01-04", - 4.547471590949188 - ], - [ - "TomBrady", - "2016-01-05", - 0.15031840349084113 - ], - [ - "TomBrady", - "2016-01-06", - 4.9410839766070165 - ], - [ - "TomBrady", - "2016-01-07", - 4.896765781779371 - ], - [ - "TomBrady", - "2016-01-08", - 4.935471974998055 - ], - [ - "TomBrady", - "2016-01-09", - 7.549984628116993 - ], - [ - "TomBrady", - "2016-01-10", - 5.454562392827867 - ], - [ - "TomBrady", - "2016-01-11", - 2.876063204590288 - ], - [ - "TomBrady", - "2016-01-12", - 3.4962556303947316 - ], - [ - "TomBrady", - "2016-01-13", - 5.581049567418119 - ] - ] - } - }, - "x": { - "anyOf": [ - { - "$ref": "#/components/schemas/MultiSeriesInput" - }, - { - "type": "null" - } - ], - "description": "The exogenous variables provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"ex_1\", \"ex_2\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.2, 0.67], [\"ts_0\", \"2021-01-02\", 0.4, 0.7]}. This should also include forecasting horizon (fh) additional timestamps for each unique_id to calculate the future values." + "type": "array", + "title": "Detail" + } + }, + "type": "object", + "title": "HTTPValidationError" + }, + "InSampleInput": { + "properties": { + "series": { + "$ref": "#/components/schemas/SeriesWithExogenous" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." + }, + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + }, + "finetuned_model_id": { + "anyOf": [ + { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" + }, + { + "type": "null" + } + ], + "title": "Finetuned Model Id", + "description": "ID of previously finetuned model" + }, + "level": { + "anyOf": [ + { + "items": { + "anyOf": [ + { + "type": "integer", + "exclusiveMaximum": 100.0, + "minimum": 0.0 }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true + { + "type": "number", + "exclusiveMaximum": 100.0, + "minimum": 0.0 } + ] }, - "type": "object", - "title": "MultiSeriesInsampleForecast" + "type": "array", + "minItems": 1 + }, + { + "type": "null" + } + ], + "title": "Level", + "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." + }, + "feature_contributions": { + "type": "boolean", + "title": "Feature Contributions", + "description": "Compute the exogenous features contributions to the forecast.", + "default": false + } + }, + "type": "object", + "required": ["series", "freq"], + "title": "InSampleInput" + }, + "InSampleOutput": { + "properties": { + "input_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Input Tokens" + }, + "output_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Output Tokens" + }, + "finetune_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Tokens" + }, + "mean": { + "items": { + "type": "number" }, - "OnlineAnomalyInput": { - "properties": { - "series": { - "$ref": "#/components/schemas/SeriesWithExogenous" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." - }, - "detection_size": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "Detection Size", - "description": "Window over which to detect anomalies starting from the end of the series. This window is not considered when calculating the anomaly threshold to avoid bias from abnormal samples, unless there are less than 6 * detection_size forecasted samples." - }, - "threshold_method": { - "type": "string", - "enum": [ - "univariate", - "multivariate" - ], - "title": "Threshold Method", - "description": "The thresholding method to detect anomalies", - "default": "univariate" - }, - "h": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "H", - "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict." - }, - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true - }, - "level": { - "anyOf": [ - { - "type": "integer", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - }, - { - "type": "number", - "exclusiveMaximum": 100.0, - "minimum": 0.0 - } - ], - "title": "Level", - "description": "Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.", - "default": 99 - }, - "finetune_steps": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Steps", - "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", - "default": 0 - }, - "finetune_loss": { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape", - "poisson" - ], - "title": "Finetune Loss", - "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", - "default": "default" - }, - "finetune_depth": { - "type": "integer", - "enum": [ - 1, - 2, - 3, - 4, - 5 - ], - "title": "Finetune Depth", - "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. By default, the value is set to 1.", - "default": 1 - }, - "finetuned_model_id": { - "anyOf": [ - { - "type": "string", - "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" - }, - { - "type": "null" - } - ], - "title": "Finetuned Model Id", - "description": "ID of previously finetuned model" + "type": "array", + "title": "Mean" + }, + "sizes": { + "items": { + "type": "integer" + }, + "type": "array", + "title": "Sizes" + }, + "intervals": { + "anyOf": [ + { + "additionalProperties": { + "items": { + "type": "number" + }, + "type": "array" + }, + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Intervals" + }, + "weights_x": { + "anyOf": [ + { + "items": { + "type": "number" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Weights X" + }, + "feature_contributions": { + "anyOf": [ + { + "items": { + "items": { + "type": "number" + }, + "type": "array" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Feature Contributions" + } + }, + "type": "object", + "required": [ + "input_tokens", + "output_tokens", + "finetune_tokens", + "mean", + "sizes" + ], + "title": "InSampleOutput" + }, + "MultiSeriesAnomaly": { + "properties": { + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", + "default": "D" + }, + "level": { + "items": {}, + "type": "array", + "title": "Level", + "description": "Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.", + "default": [99] + }, + "y": { + "title": "Y", + "description": "The historical time series data provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"y\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.7], [\"ts_0\", \"2021-01-02\", 0.8]}.", + "default": { + "columns": ["unique_id", "ds", "y"], + "data": [ + ["PeytonManning", "2015-12-02", 4.390508031418598], + ["PeytonManning", "2015-12-03", 5.721514930979356], + ["PeytonManning", "2015-12-04", 4.822107008573151], + ["PeytonManning", "2015-12-05", 4.359065463975175], + ["PeytonManning", "2015-12-06", 3.3892383947112377], + ["PeytonManning", "2015-12-07", 5.167152904533249], + ["PeytonManning", "2015-12-08", 3.50069769010154], + ["PeytonManning", "2015-12-09", 7.134184006256638], + ["PeytonManning", "2015-12-10", 7.709302084008234], + ["PeytonManning", "2015-12-11", 3.0675321506062216], + ["PeytonManning", "2015-12-12", 6.333800304661317], + ["PeytonManning", "2015-12-13", 4.231159358023236], + ["PeytonManning", "2015-12-14", 4.5443564887514585], + ["PeytonManning", "2015-12-15", 7.404773106341288], + ["PeytonManning", "2015-12-16", 0.5682884655830955], + ["PeytonManning", "2015-12-17", 0.6970343976123257], + ["PeytonManning", "2015-12-18", 0.16174717952260576], + ["PeytonManning", "2015-12-19", 6.660958764383504], + ["PeytonManning", "2015-12-20", 6.225254007598804], + ["PeytonManning", "2015-12-21", 6.960097185974553], + ["PeytonManning", "2015-12-22", 7.828946737862112], + ["PeytonManning", "2015-12-23", 6.393268513733789], + ["PeytonManning", "2015-12-24", 3.6918348980234548], + ["PeytonManning", "2015-12-25", 6.244233410291644], + ["PeytonManning", "2015-12-26", 0.9461954069514658], + ["PeytonManning", "2015-12-27", 5.119368170620191], + ["PeytonManning", "2015-12-28", 1.1468262992723712], + ["PeytonManning", "2015-12-29", 7.557351336396671], + ["PeytonManning", "2015-12-30", 4.174786574000573], + ["PeytonManning", "2015-12-31", 3.3172955199241887], + ["PeytonManning", "2016-01-01", 2.1164448968370158], + ["PeytonManning", "2016-01-02", 6.193869515473733], + ["PeytonManning", "2016-01-03", 3.6492026577323884], + ["PeytonManning", "2016-01-04", 4.547471590949188], + ["PeytonManning", "2016-01-05", 0.15031840349084113], + ["PeytonManning", "2016-01-06", 4.9410839766070165], + ["PeytonManning", "2016-01-07", 4.896765781779371], + ["PeytonManning", "2016-01-08", 4.935471974998055], + ["PeytonManning", "2016-01-09", 7.549984628116993], + ["PeytonManning", "2016-01-10", 5.454562392827867], + ["PeytonManning", "2016-01-11", 2.876063204590288], + ["PeytonManning", "2016-01-12", 3.4962556303947316], + ["PeytonManning", "2016-01-13", 5.581049567418119], + ["TomBrady", "2015-12-02", 4.390508031418598], + ["TomBrady", "2015-12-03", 5.721514930979356], + ["TomBrady", "2015-12-04", 4.822107008573151], + ["TomBrady", "2015-12-05", 4.359065463975175], + ["TomBrady", "2015-12-06", 3.3892383947112377], + ["TomBrady", "2015-12-07", 5.167152904533249], + ["TomBrady", "2015-12-08", 3.50069769010154], + ["TomBrady", "2015-12-09", 7.134184006256638], + ["TomBrady", "2015-12-10", 7.709302084008234], + ["TomBrady", "2015-12-11", 3.0675321506062216], + ["TomBrady", "2015-12-12", 6.333800304661317], + ["TomBrady", "2015-12-13", 4.231159358023236], + ["TomBrady", "2015-12-14", 4.5443564887514585], + ["TomBrady", "2015-12-15", 7.404773106341288], + ["TomBrady", "2015-12-16", 0.5682884655830955], + ["TomBrady", "2015-12-17", 0.6970343976123257], + ["TomBrady", "2015-12-18", 0.16174717952260576], + ["TomBrady", "2015-12-19", 6.660958764383504], + ["TomBrady", "2015-12-20", 6.225254007598804], + ["TomBrady", "2015-12-21", 6.960097185974553], + ["TomBrady", "2015-12-22", 7.828946737862112], + ["TomBrady", "2015-12-23", 6.393268513733789], + ["TomBrady", "2015-12-24", 3.6918348980234548], + ["TomBrady", "2015-12-25", 6.244233410291644], + ["TomBrady", "2015-12-26", 0.9461954069514658], + ["TomBrady", "2015-12-27", 5.119368170620191], + ["TomBrady", "2015-12-28", 1.1468262992723712], + ["TomBrady", "2015-12-29", 7.557351336396671], + ["TomBrady", "2015-12-30", 4.174786574000573], + ["TomBrady", "2015-12-31", 3.3172955199241887], + ["TomBrady", "2016-01-01", 2.1164448968370158], + ["TomBrady", "2016-01-02", 6.193869515473733], + ["TomBrady", "2016-01-03", 3.6492026577323884], + ["TomBrady", "2016-01-04", 4.547471590949188], + ["TomBrady", "2016-01-05", 0.15031840349084113], + ["TomBrady", "2016-01-06", 4.9410839766070165], + ["TomBrady", "2016-01-07", 4.896765781779371], + ["TomBrady", "2016-01-08", 4.935471974998055], + ["TomBrady", "2016-01-09", 7.549984628116993], + ["TomBrady", "2016-01-10", 5.454562392827867], + ["TomBrady", "2016-01-11", 2.876063204590288], + ["TomBrady", "2016-01-12", 3.4962556303947316], + ["TomBrady", "2016-01-13", 5.581049567418119] + ] + } + }, + "x": { + "anyOf": [ + { + "$ref": "#/components/schemas/MultiSeriesInput" + }, + { + "type": "null" + } + ], + "description": "The exogenous variables provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"ex_1\", \"ex_2\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.2, 0.67], [\"ts_0\", \"2021-01-02\", 0.4, 0.7]}. This should also include forecasting horizon (fh) additional timestamps for each unique_id to calculate the future values." + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + } + }, + "type": "object", + "title": "MultiSeriesAnomaly" + }, + "MultiSeriesCrossValidation": { + "properties": { + "fewshot_steps": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "title": "Fewshot Steps", + "description": "Deprecated. Please use finetune_steps instead.", + "deprecated": true + }, + "fewshot_loss": { + "anyOf": [ + { + "type": "string", + "enum": ["default", "mae", "mse", "rmse", "mape", "smape"] + }, + { + "type": "null" + } + ], + "title": "Fewshot Loss", + "description": "Deprecated. Please use finetune_loss instead.", + "deprecated": true + }, + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", + "default": "D" + }, + "level": { + "anyOf": [ + { + "items": { + "anyOf": [ + { + "type": "integer" }, - "step_size": { - "anyOf": [ - { - "type": "integer", - "exclusiveMinimum": 0.0 - }, - { - "type": "null" - } - ], - "title": "Step Size", - "description": "Step size between each cross validation window. If None it will be equal to the forecasting horizon." + { + "type": "number" } + ] }, - "type": "object", - "required": [ - "series", - "freq", - "detection_size", - "h" - ], - "title": "OnlineAnomalyInput" - }, - "OnlineAnomalyOutput": { - "properties": { - "input_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Input Tokens" - }, - "output_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Output Tokens" - }, - "finetune_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Tokens" - }, - "mean": { - "items": { - "type": "number" - }, - "type": "array", - "title": "Mean" - }, - "sizes": { - "items": { - "type": "integer" - }, - "type": "array", - "title": "Sizes" - }, - "idxs": { - "items": { - "type": "integer" - }, - "type": "array", - "title": "Idxs" - }, - "anomaly": { - "items": { - "type": "boolean" - }, - "type": "array", - "title": "Anomaly" - }, - "anomaly_score": { - "items": { - "type": "number" - }, - "type": "array", - "title": "Anomaly Score" - }, - "accumulated_anomaly_score": { - "anyOf": [ - { - "items": { - "type": "number" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Accumulated Anomaly Score" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Level", + "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." + }, + "fh": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "Fh", + "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict.", + "default": 7 + }, + "y": { + "title": "Y", + "description": "The historical time series data provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"y\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.7], [\"ts_0\", \"2021-01-02\", 0.8]}.", + "default": { + "columns": ["unique_id", "ds", "y"], + "data": [ + ["PeytonManning", "2015-12-02", 4.390508031418598], + ["PeytonManning", "2015-12-03", 5.721514930979356], + ["PeytonManning", "2015-12-04", 4.822107008573151], + ["PeytonManning", "2015-12-05", 4.359065463975175], + ["PeytonManning", "2015-12-06", 3.3892383947112377], + ["PeytonManning", "2015-12-07", 5.167152904533249], + ["PeytonManning", "2015-12-08", 3.50069769010154], + ["PeytonManning", "2015-12-09", 7.134184006256638], + ["PeytonManning", "2015-12-10", 7.709302084008234], + ["PeytonManning", "2015-12-11", 3.0675321506062216], + ["PeytonManning", "2015-12-12", 6.333800304661317], + ["PeytonManning", "2015-12-13", 4.231159358023236], + ["PeytonManning", "2015-12-14", 4.5443564887514585], + ["PeytonManning", "2015-12-15", 7.404773106341288], + ["PeytonManning", "2015-12-16", 0.5682884655830955], + ["PeytonManning", "2015-12-17", 0.6970343976123257], + ["PeytonManning", "2015-12-18", 0.16174717952260576], + ["PeytonManning", "2015-12-19", 6.660958764383504], + ["PeytonManning", "2015-12-20", 6.225254007598804], + ["PeytonManning", "2015-12-21", 6.960097185974553], + ["PeytonManning", "2015-12-22", 7.828946737862112], + ["PeytonManning", "2015-12-23", 6.393268513733789], + ["PeytonManning", "2015-12-24", 3.6918348980234548], + ["PeytonManning", "2015-12-25", 6.244233410291644], + ["PeytonManning", "2015-12-26", 0.9461954069514658], + ["PeytonManning", "2015-12-27", 5.119368170620191], + ["PeytonManning", "2015-12-28", 1.1468262992723712], + ["PeytonManning", "2015-12-29", 7.557351336396671], + ["PeytonManning", "2015-12-30", 4.174786574000573], + ["PeytonManning", "2015-12-31", 3.3172955199241887], + ["PeytonManning", "2016-01-01", 2.1164448968370158], + ["PeytonManning", "2016-01-02", 6.193869515473733], + ["PeytonManning", "2016-01-03", 3.6492026577323884], + ["PeytonManning", "2016-01-04", 4.547471590949188], + ["PeytonManning", "2016-01-05", 0.15031840349084113], + ["PeytonManning", "2016-01-06", 4.9410839766070165], + ["PeytonManning", "2016-01-07", 4.896765781779371], + ["PeytonManning", "2016-01-08", 4.935471974998055], + ["PeytonManning", "2016-01-09", 7.549984628116993], + ["PeytonManning", "2016-01-10", 5.454562392827867], + ["PeytonManning", "2016-01-11", 2.876063204590288], + ["PeytonManning", "2016-01-12", 3.4962556303947316], + ["PeytonManning", "2016-01-13", 5.581049567418119], + ["TomBrady", "2015-12-02", 4.390508031418598], + ["TomBrady", "2015-12-03", 5.721514930979356], + ["TomBrady", "2015-12-04", 4.822107008573151], + ["TomBrady", "2015-12-05", 4.359065463975175], + ["TomBrady", "2015-12-06", 3.3892383947112377], + ["TomBrady", "2015-12-07", 5.167152904533249], + ["TomBrady", "2015-12-08", 3.50069769010154], + ["TomBrady", "2015-12-09", 7.134184006256638], + ["TomBrady", "2015-12-10", 7.709302084008234], + ["TomBrady", "2015-12-11", 3.0675321506062216], + ["TomBrady", "2015-12-12", 6.333800304661317], + ["TomBrady", "2015-12-13", 4.231159358023236], + ["TomBrady", "2015-12-14", 4.5443564887514585], + ["TomBrady", "2015-12-15", 7.404773106341288], + ["TomBrady", "2015-12-16", 0.5682884655830955], + ["TomBrady", "2015-12-17", 0.6970343976123257], + ["TomBrady", "2015-12-18", 0.16174717952260576], + ["TomBrady", "2015-12-19", 6.660958764383504], + ["TomBrady", "2015-12-20", 6.225254007598804], + ["TomBrady", "2015-12-21", 6.960097185974553], + ["TomBrady", "2015-12-22", 7.828946737862112], + ["TomBrady", "2015-12-23", 6.393268513733789], + ["TomBrady", "2015-12-24", 3.6918348980234548], + ["TomBrady", "2015-12-25", 6.244233410291644], + ["TomBrady", "2015-12-26", 0.9461954069514658], + ["TomBrady", "2015-12-27", 5.119368170620191], + ["TomBrady", "2015-12-28", 1.1468262992723712], + ["TomBrady", "2015-12-29", 7.557351336396671], + ["TomBrady", "2015-12-30", 4.174786574000573], + ["TomBrady", "2015-12-31", 3.3172955199241887], + ["TomBrady", "2016-01-01", 2.1164448968370158], + ["TomBrady", "2016-01-02", 6.193869515473733], + ["TomBrady", "2016-01-03", 3.6492026577323884], + ["TomBrady", "2016-01-04", 4.547471590949188], + ["TomBrady", "2016-01-05", 0.15031840349084113], + ["TomBrady", "2016-01-06", 4.9410839766070165], + ["TomBrady", "2016-01-07", 4.896765781779371], + ["TomBrady", "2016-01-08", 4.935471974998055], + ["TomBrady", "2016-01-09", 7.549984628116993], + ["TomBrady", "2016-01-10", 5.454562392827867], + ["TomBrady", "2016-01-11", 2.876063204590288], + ["TomBrady", "2016-01-12", 3.4962556303947316], + ["TomBrady", "2016-01-13", 5.581049567418119] + ] + } + }, + "x": { + "anyOf": [ + { + "$ref": "#/components/schemas/MultiSeriesInput" + }, + { + "type": "null" + } + ], + "description": "The exogenous variables provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"ex_1\", \"ex_2\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.2, 0.67], [\"ts_0\", \"2021-01-02\", 0.4, 0.7]}. This should also include forecasting horizon (fh) additional timestamps for each unique_id to calculate the future values." + }, + "n_windows": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "N Windows", + "description": "Number of windows to evaluate.", + "default": 1 + }, + "step_size": { + "anyOf": [ + { + "type": "integer", + "exclusiveMinimum": 0.0 + }, + { + "type": "null" + } + ], + "title": "Step Size", + "description": "Step size between each cross validation window. If None it will be equal to the forecasting horizon." + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + }, + "finetune_steps": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Steps", + "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", + "default": 0 + }, + "finetune_loss": { + "type": "string", + "enum": ["default", "mae", "mse", "rmse", "mape", "smape"], + "title": "Finetune Loss", + "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", + "default": "default" + } + }, + "type": "object", + "title": "MultiSeriesCrossValidation" + }, + "MultiSeriesForecast": { + "properties": { + "fewshot_steps": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "title": "Fewshot Steps", + "description": "Deprecated. Please use finetune_steps instead.", + "deprecated": true + }, + "fewshot_loss": { + "anyOf": [ + { + "type": "string", + "enum": ["default", "mae", "mse", "rmse", "mape", "smape"] + }, + { + "type": "null" + } + ], + "title": "Fewshot Loss", + "description": "Deprecated. Please use finetune_loss instead.", + "deprecated": true + }, + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", + "default": "D" + }, + "level": { + "anyOf": [ + { + "items": { + "anyOf": [ + { + "type": "integer" }, - "intervals": { - "anyOf": [ - { - "additionalProperties": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "object" - }, - { - "type": "null" - } - ], - "title": "Intervals" + { + "type": "number" } + ] }, - "type": "object", - "required": [ - "input_tokens", - "output_tokens", - "finetune_tokens", - "mean", - "sizes", - "idxs", - "anomaly", - "anomaly_score" - ], - "title": "OnlineAnomalyOutput" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Level", + "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." + }, + "fh": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "Fh", + "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict.", + "default": 7 + }, + "y": { + "title": "Y", + "description": "The historical time series data provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"y\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.7], [\"ts_0\", \"2021-01-02\", 0.8]}.", + "default": { + "columns": ["unique_id", "ds", "y"], + "data": [ + ["PeytonManning", "2015-12-02", 4.390508031418598], + ["PeytonManning", "2015-12-03", 5.721514930979356], + ["PeytonManning", "2015-12-04", 4.822107008573151], + ["PeytonManning", "2015-12-05", 4.359065463975175], + ["PeytonManning", "2015-12-06", 3.3892383947112377], + ["PeytonManning", "2015-12-07", 5.167152904533249], + ["PeytonManning", "2015-12-08", 3.50069769010154], + ["PeytonManning", "2015-12-09", 7.134184006256638], + ["PeytonManning", "2015-12-10", 7.709302084008234], + ["PeytonManning", "2015-12-11", 3.0675321506062216], + ["PeytonManning", "2015-12-12", 6.333800304661317], + ["PeytonManning", "2015-12-13", 4.231159358023236], + ["PeytonManning", "2015-12-14", 4.5443564887514585], + ["PeytonManning", "2015-12-15", 7.404773106341288], + ["PeytonManning", "2015-12-16", 0.5682884655830955], + ["PeytonManning", "2015-12-17", 0.6970343976123257], + ["PeytonManning", "2015-12-18", 0.16174717952260576], + ["PeytonManning", "2015-12-19", 6.660958764383504], + ["PeytonManning", "2015-12-20", 6.225254007598804], + ["PeytonManning", "2015-12-21", 6.960097185974553], + ["PeytonManning", "2015-12-22", 7.828946737862112], + ["PeytonManning", "2015-12-23", 6.393268513733789], + ["PeytonManning", "2015-12-24", 3.6918348980234548], + ["PeytonManning", "2015-12-25", 6.244233410291644], + ["PeytonManning", "2015-12-26", 0.9461954069514658], + ["PeytonManning", "2015-12-27", 5.119368170620191], + ["PeytonManning", "2015-12-28", 1.1468262992723712], + ["PeytonManning", "2015-12-29", 7.557351336396671], + ["PeytonManning", "2015-12-30", 4.174786574000573], + ["PeytonManning", "2015-12-31", 3.3172955199241887], + ["PeytonManning", "2016-01-01", 2.1164448968370158], + ["PeytonManning", "2016-01-02", 6.193869515473733], + ["PeytonManning", "2016-01-03", 3.6492026577323884], + ["PeytonManning", "2016-01-04", 4.547471590949188], + ["PeytonManning", "2016-01-05", 0.15031840349084113], + ["PeytonManning", "2016-01-06", 4.9410839766070165], + ["PeytonManning", "2016-01-07", 4.896765781779371], + ["PeytonManning", "2016-01-08", 4.935471974998055], + ["PeytonManning", "2016-01-09", 7.549984628116993], + ["PeytonManning", "2016-01-10", 5.454562392827867], + ["PeytonManning", "2016-01-11", 2.876063204590288], + ["PeytonManning", "2016-01-12", 3.4962556303947316], + ["PeytonManning", "2016-01-13", 5.581049567418119], + ["TomBrady", "2015-12-02", 4.390508031418598], + ["TomBrady", "2015-12-03", 5.721514930979356], + ["TomBrady", "2015-12-04", 4.822107008573151], + ["TomBrady", "2015-12-05", 4.359065463975175], + ["TomBrady", "2015-12-06", 3.3892383947112377], + ["TomBrady", "2015-12-07", 5.167152904533249], + ["TomBrady", "2015-12-08", 3.50069769010154], + ["TomBrady", "2015-12-09", 7.134184006256638], + ["TomBrady", "2015-12-10", 7.709302084008234], + ["TomBrady", "2015-12-11", 3.0675321506062216], + ["TomBrady", "2015-12-12", 6.333800304661317], + ["TomBrady", "2015-12-13", 4.231159358023236], + ["TomBrady", "2015-12-14", 4.5443564887514585], + ["TomBrady", "2015-12-15", 7.404773106341288], + ["TomBrady", "2015-12-16", 0.5682884655830955], + ["TomBrady", "2015-12-17", 0.6970343976123257], + ["TomBrady", "2015-12-18", 0.16174717952260576], + ["TomBrady", "2015-12-19", 6.660958764383504], + ["TomBrady", "2015-12-20", 6.225254007598804], + ["TomBrady", "2015-12-21", 6.960097185974553], + ["TomBrady", "2015-12-22", 7.828946737862112], + ["TomBrady", "2015-12-23", 6.393268513733789], + ["TomBrady", "2015-12-24", 3.6918348980234548], + ["TomBrady", "2015-12-25", 6.244233410291644], + ["TomBrady", "2015-12-26", 0.9461954069514658], + ["TomBrady", "2015-12-27", 5.119368170620191], + ["TomBrady", "2015-12-28", 1.1468262992723712], + ["TomBrady", "2015-12-29", 7.557351336396671], + ["TomBrady", "2015-12-30", 4.174786574000573], + ["TomBrady", "2015-12-31", 3.3172955199241887], + ["TomBrady", "2016-01-01", 2.1164448968370158], + ["TomBrady", "2016-01-02", 6.193869515473733], + ["TomBrady", "2016-01-03", 3.6492026577323884], + ["TomBrady", "2016-01-04", 4.547471590949188], + ["TomBrady", "2016-01-05", 0.15031840349084113], + ["TomBrady", "2016-01-06", 4.9410839766070165], + ["TomBrady", "2016-01-07", 4.896765781779371], + ["TomBrady", "2016-01-08", 4.935471974998055], + ["TomBrady", "2016-01-09", 7.549984628116993], + ["TomBrady", "2016-01-10", 5.454562392827867], + ["TomBrady", "2016-01-11", 2.876063204590288], + ["TomBrady", "2016-01-12", 3.4962556303947316], + ["TomBrady", "2016-01-13", 5.581049567418119] + ] + } + }, + "x": { + "anyOf": [ + { + "$ref": "#/components/schemas/MultiSeriesInput" + }, + { + "type": "null" + } + ], + "description": "The exogenous variables provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"ex_1\", \"ex_2\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.2, 0.67], [\"ts_0\", \"2021-01-02\", 0.4, 0.7]}. This should also include forecasting horizon (fh) additional timestamps for each unique_id to calculate the future values." + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + }, + "finetune_steps": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Steps", + "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", + "default": 0 + }, + "finetune_loss": { + "type": "string", + "enum": ["default", "mae", "mse", "rmse", "mape", "smape"], + "title": "Finetune Loss", + "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", + "default": "default" + } + }, + "type": "object", + "title": "MultiSeriesForecast" + }, + "MultiSeriesInput": { + "properties": { + "columns": { + "items": { + "type": "string" }, - "Series": { - "properties": { - "y": { - "items": { - "type": "number" - }, - "type": "array", - "title": "Y", - "description": "Historic values of the target." + "type": "array", + "title": "Columns" + }, + "data": { + "items": {}, + "type": "array", + "title": "Data" + } + }, + "type": "object", + "required": ["columns", "data"], + "title": "MultiSeriesInput" + }, + "MultiSeriesInsampleForecast": { + "properties": { + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", + "default": "D" + }, + "level": { + "anyOf": [ + { + "items": { + "anyOf": [ + { + "type": "integer" }, - "sizes": { - "items": { - "type": "integer" - }, - "type": "array", - "title": "Sizes", - "description": "Sizes of the individual series." + { + "type": "number" } + ] }, - "type": "object", - "required": [ - "y", - "sizes" - ], - "title": "Series" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Level", + "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." + }, + "y": { + "title": "Y", + "description": "The historical time series data provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"y\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.7], [\"ts_0\", \"2021-01-02\", 0.8]}.", + "default": { + "columns": ["unique_id", "ds", "y"], + "data": [ + ["PeytonManning", "2015-12-02", 4.390508031418598], + ["PeytonManning", "2015-12-03", 5.721514930979356], + ["PeytonManning", "2015-12-04", 4.822107008573151], + ["PeytonManning", "2015-12-05", 4.359065463975175], + ["PeytonManning", "2015-12-06", 3.3892383947112377], + ["PeytonManning", "2015-12-07", 5.167152904533249], + ["PeytonManning", "2015-12-08", 3.50069769010154], + ["PeytonManning", "2015-12-09", 7.134184006256638], + ["PeytonManning", "2015-12-10", 7.709302084008234], + ["PeytonManning", "2015-12-11", 3.0675321506062216], + ["PeytonManning", "2015-12-12", 6.333800304661317], + ["PeytonManning", "2015-12-13", 4.231159358023236], + ["PeytonManning", "2015-12-14", 4.5443564887514585], + ["PeytonManning", "2015-12-15", 7.404773106341288], + ["PeytonManning", "2015-12-16", 0.5682884655830955], + ["PeytonManning", "2015-12-17", 0.6970343976123257], + ["PeytonManning", "2015-12-18", 0.16174717952260576], + ["PeytonManning", "2015-12-19", 6.660958764383504], + ["PeytonManning", "2015-12-20", 6.225254007598804], + ["PeytonManning", "2015-12-21", 6.960097185974553], + ["PeytonManning", "2015-12-22", 7.828946737862112], + ["PeytonManning", "2015-12-23", 6.393268513733789], + ["PeytonManning", "2015-12-24", 3.6918348980234548], + ["PeytonManning", "2015-12-25", 6.244233410291644], + ["PeytonManning", "2015-12-26", 0.9461954069514658], + ["PeytonManning", "2015-12-27", 5.119368170620191], + ["PeytonManning", "2015-12-28", 1.1468262992723712], + ["PeytonManning", "2015-12-29", 7.557351336396671], + ["PeytonManning", "2015-12-30", 4.174786574000573], + ["PeytonManning", "2015-12-31", 3.3172955199241887], + ["PeytonManning", "2016-01-01", 2.1164448968370158], + ["PeytonManning", "2016-01-02", 6.193869515473733], + ["PeytonManning", "2016-01-03", 3.6492026577323884], + ["PeytonManning", "2016-01-04", 4.547471590949188], + ["PeytonManning", "2016-01-05", 0.15031840349084113], + ["PeytonManning", "2016-01-06", 4.9410839766070165], + ["PeytonManning", "2016-01-07", 4.896765781779371], + ["PeytonManning", "2016-01-08", 4.935471974998055], + ["PeytonManning", "2016-01-09", 7.549984628116993], + ["PeytonManning", "2016-01-10", 5.454562392827867], + ["PeytonManning", "2016-01-11", 2.876063204590288], + ["PeytonManning", "2016-01-12", 3.4962556303947316], + ["PeytonManning", "2016-01-13", 5.581049567418119], + ["TomBrady", "2015-12-02", 4.390508031418598], + ["TomBrady", "2015-12-03", 5.721514930979356], + ["TomBrady", "2015-12-04", 4.822107008573151], + ["TomBrady", "2015-12-05", 4.359065463975175], + ["TomBrady", "2015-12-06", 3.3892383947112377], + ["TomBrady", "2015-12-07", 5.167152904533249], + ["TomBrady", "2015-12-08", 3.50069769010154], + ["TomBrady", "2015-12-09", 7.134184006256638], + ["TomBrady", "2015-12-10", 7.709302084008234], + ["TomBrady", "2015-12-11", 3.0675321506062216], + ["TomBrady", "2015-12-12", 6.333800304661317], + ["TomBrady", "2015-12-13", 4.231159358023236], + ["TomBrady", "2015-12-14", 4.5443564887514585], + ["TomBrady", "2015-12-15", 7.404773106341288], + ["TomBrady", "2015-12-16", 0.5682884655830955], + ["TomBrady", "2015-12-17", 0.6970343976123257], + ["TomBrady", "2015-12-18", 0.16174717952260576], + ["TomBrady", "2015-12-19", 6.660958764383504], + ["TomBrady", "2015-12-20", 6.225254007598804], + ["TomBrady", "2015-12-21", 6.960097185974553], + ["TomBrady", "2015-12-22", 7.828946737862112], + ["TomBrady", "2015-12-23", 6.393268513733789], + ["TomBrady", "2015-12-24", 3.6918348980234548], + ["TomBrady", "2015-12-25", 6.244233410291644], + ["TomBrady", "2015-12-26", 0.9461954069514658], + ["TomBrady", "2015-12-27", 5.119368170620191], + ["TomBrady", "2015-12-28", 1.1468262992723712], + ["TomBrady", "2015-12-29", 7.557351336396671], + ["TomBrady", "2015-12-30", 4.174786574000573], + ["TomBrady", "2015-12-31", 3.3172955199241887], + ["TomBrady", "2016-01-01", 2.1164448968370158], + ["TomBrady", "2016-01-02", 6.193869515473733], + ["TomBrady", "2016-01-03", 3.6492026577323884], + ["TomBrady", "2016-01-04", 4.547471590949188], + ["TomBrady", "2016-01-05", 0.15031840349084113], + ["TomBrady", "2016-01-06", 4.9410839766070165], + ["TomBrady", "2016-01-07", 4.896765781779371], + ["TomBrady", "2016-01-08", 4.935471974998055], + ["TomBrady", "2016-01-09", 7.549984628116993], + ["TomBrady", "2016-01-10", 5.454562392827867], + ["TomBrady", "2016-01-11", 2.876063204590288], + ["TomBrady", "2016-01-12", 3.4962556303947316], + ["TomBrady", "2016-01-13", 5.581049567418119] + ] + } + }, + "x": { + "anyOf": [ + { + "$ref": "#/components/schemas/MultiSeriesInput" + }, + { + "type": "null" + } + ], + "description": "The exogenous variables provided as a dictionary of two colums: columns and data. The columns contains the columns of the dataframe and data contains eaach data point. For example: {\"columns\": [\"unique_id\", \"ds\", \"ex_1\", \"ex_2\"], \"data\": [[\"ts_0\", \"2021-01-01\", 0.2, 0.67], [\"ts_0\", \"2021-01-02\", 0.4, 0.7]}. This should also include forecasting horizon (fh) additional timestamps for each unique_id to calculate the future values." + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + } + }, + "type": "object", + "title": "MultiSeriesInsampleForecast" + }, + "OnlineAnomalyInput": { + "properties": { + "series": { + "$ref": "#/components/schemas/SeriesWithExogenous" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available." + }, + "detection_size": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "Detection Size", + "description": "Window over which to detect anomalies starting from the end of the series. This window is not considered when calculating the anomaly threshold to avoid bias from abnormal samples, unless there are less than 6 * detection_size forecasted samples." + }, + "threshold_method": { + "type": "string", + "enum": ["univariate", "multivariate"], + "title": "Threshold Method", + "description": "The thresholding method to detect anomalies", + "default": "univariate" + }, + "h": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "H", + "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict." + }, + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + }, + "level": { + "anyOf": [ + { + "type": "integer", + "exclusiveMaximum": 100.0, + "minimum": 0.0 + }, + { + "type": "number", + "exclusiveMaximum": 100.0, + "minimum": 0.0 + } + ], + "title": "Level", + "description": "Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.", + "default": 99 + }, + "finetune_steps": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Steps", + "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", + "default": 0 + }, + "finetune_loss": { + "type": "string", + "enum": [ + "default", + "mae", + "mse", + "rmse", + "mape", + "smape", + "poisson" + ], + "title": "Finetune Loss", + "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", + "default": "default" + }, + "finetune_depth": { + "type": "integer", + "enum": [1, 2, 3, 4, 5], + "title": "Finetune Depth", + "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. By default, the value is set to 1.", + "default": 1 + }, + "finetuned_model_id": { + "anyOf": [ + { + "type": "string", + "pattern": "^[a-zA-Z0-9\\-_]{1,36}$" + }, + { + "type": "null" + } + ], + "title": "Finetuned Model Id", + "description": "ID of previously finetuned model" + }, + "step_size": { + "anyOf": [ + { + "type": "integer", + "exclusiveMinimum": 0.0 + }, + { + "type": "null" + } + ], + "title": "Step Size", + "description": "Step size between each cross validation window. If None it will be equal to the forecasting horizon." + } + }, + "type": "object", + "required": ["series", "freq", "detection_size", "h"], + "title": "OnlineAnomalyInput" + }, + "OnlineAnomalyOutput": { + "properties": { + "input_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Input Tokens" + }, + "output_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Output Tokens" + }, + "finetune_tokens": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Tokens" + }, + "mean": { + "items": { + "type": "number" }, - "SeriesWithExogenous": { - "properties": { - "X": { - "anyOf": [ - { - "items": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "X", - "description": "Historic values of the exogenous features. Each feature must be a list of the same size as the target (y)." - }, - "y": { - "items": { - "type": "number" - }, - "type": "array", - "title": "Y", - "description": "Historic values of the target." - }, - "sizes": { - "items": { - "type": "integer" - }, - "type": "array", - "title": "Sizes", - "description": "Sizes of the individual series." - } + "type": "array", + "title": "Mean" + }, + "sizes": { + "items": { + "type": "integer" + }, + "type": "array", + "title": "Sizes" + }, + "idxs": { + "items": { + "type": "integer" + }, + "type": "array", + "title": "Idxs" + }, + "anomaly": { + "items": { + "type": "boolean" + }, + "type": "array", + "title": "Anomaly" + }, + "anomaly_score": { + "items": { + "type": "number" + }, + "type": "array", + "title": "Anomaly Score" + }, + "accumulated_anomaly_score": { + "anyOf": [ + { + "items": { + "type": "number" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Accumulated Anomaly Score" + }, + "intervals": { + "anyOf": [ + { + "additionalProperties": { + "items": { + "type": "number" + }, + "type": "array" }, - "type": "object", - "required": [ - "y", - "sizes" - ], - "title": "SeriesWithExogenous" + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Intervals" + } + }, + "type": "object", + "required": [ + "input_tokens", + "output_tokens", + "finetune_tokens", + "mean", + "sizes", + "idxs", + "anomaly", + "anomaly_score" + ], + "title": "OnlineAnomalyOutput" + }, + "Series": { + "properties": { + "y": { + "items": { + "type": "number" }, - "SeriesWithFutureExogenous": { - "properties": { - "X_future": { - "anyOf": [ - { - "items": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "X Future", - "description": "Future values of the exogenous features. Each feature must be a list of size number of series times the forecast horizon (h)." - }, - "X": { - "anyOf": [ - { - "items": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "X", - "description": "Historic values of the exogenous features. Each feature must be a list of the same size as the target (y)." - }, - "y": { - "items": { - "type": "number" - }, - "type": "array", - "title": "Y", - "description": "Historic values of the target." - }, - "sizes": { - "items": { - "type": "integer" - }, - "type": "array", - "title": "Sizes", - "description": "Sizes of the individual series." - } + "type": "array", + "title": "Y", + "description": "Historic values of the target." + }, + "sizes": { + "items": { + "type": "integer" + }, + "type": "array", + "title": "Sizes", + "description": "Sizes of the individual series." + } + }, + "type": "object", + "required": ["y", "sizes"], + "title": "Series" + }, + "SeriesWithExogenous": { + "properties": { + "X": { + "anyOf": [ + { + "items": { + "items": { + "type": "number" + }, + "type": "array" }, - "type": "object", - "required": [ - "y", - "sizes" - ], - "title": "SeriesWithFutureExogenous" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "X", + "description": "Historic values of the exogenous features. Each feature must be a list of the same size as the target (y)." + }, + "y": { + "items": { + "type": "number" }, - "SingleSeriesForecast": { - "properties": { - "fewshot_steps": { - "anyOf": [ - { - "type": "integer" - }, - { - "type": "null" - } - ], - "title": "Fewshot Steps", - "description": "Deprecated. Please use finetune_steps instead.", - "deprecated": true - }, - "fewshot_loss": { - "anyOf": [ - { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape" - ] - }, - { - "type": "null" - } - ], - "title": "Fewshot Loss", - "description": "Deprecated. Please use finetune_loss instead.", - "deprecated": true - }, - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", - "default": "D" - }, - "level": { - "anyOf": [ - { - "items": { - "anyOf": [ - { - "type": "integer" - }, - { - "type": "number" - } - ] - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Level", - "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." - }, - "fh": { - "type": "integer", - "exclusiveMinimum": 0.0, - "title": "Fh", - "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict.", - "default": 7 - }, - "y": { - "title": "Y", - "description": "The historical time series data provided as a dictionary. Each key is a timestamp (string format: YYYY-MM-DD) and the corresponding value is the observation at that time point. For example: {\"2021-01-01\": 0.1, \"2021-01-02\": 0.4}.", - "default": { - "2015-12-02": 4.390508031418598, - "2015-12-03": 5.721514930979356, - "2015-12-04": 4.822107008573151, - "2015-12-05": 4.359065463975175, - "2015-12-06": 3.3892383947112377, - "2015-12-07": 5.167152904533249, - "2015-12-08": 3.50069769010154, - "2015-12-09": 7.134184006256638, - "2015-12-10": 7.709302084008234, - "2015-12-11": 3.0675321506062216, - "2015-12-12": 6.333800304661317, - "2015-12-13": 4.231159358023236, - "2015-12-14": 4.5443564887514585, - "2015-12-15": 7.404773106341288, - "2015-12-16": 0.5682884655830955, - "2015-12-17": 0.6970343976123257, - "2015-12-18": 0.16174717952260576, - "2015-12-19": 6.660958764383504, - "2015-12-20": 6.225254007598804, - "2015-12-21": 6.960097185974553, - "2015-12-22": 7.828946737862112, - "2015-12-23": 6.393268513733789, - "2015-12-24": 3.6918348980234548, - "2015-12-25": 6.244233410291644, - "2015-12-26": 0.9461954069514658, - "2015-12-27": 5.119368170620191, - "2015-12-28": 1.1468262992723712, - "2015-12-29": 7.557351336396671, - "2015-12-30": 4.174786574000573, - "2015-12-31": 3.3172955199241887, - "2016-01-01": 2.1164448968370158, - "2016-01-02": 6.193869515473733, - "2016-01-03": 3.6492026577323884, - "2016-01-04": 4.547471590949188, - "2016-01-05": 0.15031840349084113, - "2016-01-06": 4.9410839766070165, - "2016-01-07": 4.896765781779371, - "2016-01-08": 4.935471974998055, - "2016-01-09": 7.549984628116993, - "2016-01-10": 5.454562392827867, - "2016-01-11": 2.876063204590288, - "2016-01-12": 3.4962556303947316, - "2016-01-13": 5.581049567418119 - } - }, - "x": { - "anyOf": [ - { - "additionalProperties": { - "items": { - "type": "number" - }, - "type": "array" - }, - "type": "object" - }, - { - "type": "null" - } - ], - "title": "X", - "description": "The exogenous variables provided as a dictionary. Each key is a timestamp (string format: YYYY-MM-DD) and the corresponding value is a list of exogenous variable values at that time point. For example: {\"2021-01-01\": [0.1], \"2021-01-02\": [0.4]}. This should also include forecasting horizon (fh) additional timestamps to calculate the future values." - }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true - }, - "finetune_steps": { - "type": "integer", - "minimum": 0.0, - "title": "Finetune Steps", - "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", - "default": 0 - }, - "finetune_loss": { - "type": "string", - "enum": [ - "default", - "mae", - "mse", - "rmse", - "mape", - "smape" - ], - "title": "Finetune Loss", - "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", - "default": "default" - } + "type": "array", + "title": "Y", + "description": "Historic values of the target." + }, + "sizes": { + "items": { + "type": "integer" + }, + "type": "array", + "title": "Sizes", + "description": "Sizes of the individual series." + } + }, + "type": "object", + "required": ["y", "sizes"], + "title": "SeriesWithExogenous" + }, + "SeriesWithFutureExogenous": { + "properties": { + "X_future": { + "anyOf": [ + { + "items": { + "items": { + "type": "number" + }, + "type": "array" }, - "type": "object", - "title": "SingleSeriesForecast" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "X Future", + "description": "Future values of the exogenous features. Each feature must be a list of size number of series times the forecast horizon (h)." + }, + "X": { + "anyOf": [ + { + "items": { + "items": { + "type": "number" + }, + "type": "array" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "X", + "description": "Historic values of the exogenous features. Each feature must be a list of the same size as the target (y)." + }, + "y": { + "items": { + "type": "number" }, - "SingleSeriesInsampleForecast": { - "properties": { - "model": { - "title": "Model", - "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", - "default": "timegpt-1" - }, - "freq": { - "type": "string", - "title": "Freq", - "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", - "default": "D" - }, - "level": { - "anyOf": [ - { - "items": { - "anyOf": [ - { - "type": "integer" - }, - { - "type": "number" - } - ] - }, - "type": "array" - }, - { - "type": "null" - } - ], - "title": "Level", - "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." - }, - "y": { - "title": "Y", - "description": "The historical time series data provided as a dictionary. Each key is a timestamp (string format: YYYY-MM-DD) and the corresponding value is the observation at that time point. For example: {\"2021-01-01\": 0.1, \"2021-01-02\": 0.4}.", - "default": { - "2015-12-02": 4.390508031418598, - "2015-12-03": 5.721514930979356, - "2015-12-04": 4.822107008573151, - "2015-12-05": 4.359065463975175, - "2015-12-06": 3.3892383947112377, - "2015-12-07": 5.167152904533249, - "2015-12-08": 3.50069769010154, - "2015-12-09": 7.134184006256638, - "2015-12-10": 7.709302084008234, - "2015-12-11": 3.0675321506062216, - "2015-12-12": 6.333800304661317, - "2015-12-13": 4.231159358023236, - "2015-12-14": 4.5443564887514585, - "2015-12-15": 7.404773106341288, - "2015-12-16": 0.5682884655830955, - "2015-12-17": 0.6970343976123257, - "2015-12-18": 0.16174717952260576, - "2015-12-19": 6.660958764383504, - "2015-12-20": 6.225254007598804, - "2015-12-21": 6.960097185974553, - "2015-12-22": 7.828946737862112, - "2015-12-23": 6.393268513733789, - "2015-12-24": 3.6918348980234548, - "2015-12-25": 6.244233410291644, - "2015-12-26": 0.9461954069514658, - "2015-12-27": 5.119368170620191, - "2015-12-28": 1.1468262992723712, - "2015-12-29": 7.557351336396671, - "2015-12-30": 4.174786574000573, - "2015-12-31": 3.3172955199241887, - "2016-01-01": 2.1164448968370158, - "2016-01-02": 6.193869515473733, - "2016-01-03": 3.6492026577323884, - "2016-01-04": 4.547471590949188, - "2016-01-05": 0.15031840349084113, - "2016-01-06": 4.9410839766070165, - "2016-01-07": 4.896765781779371, - "2016-01-08": 4.935471974998055, - "2016-01-09": 7.549984628116993, - "2016-01-10": 5.454562392827867, - "2016-01-11": 2.876063204590288, - "2016-01-12": 3.4962556303947316, - "2016-01-13": 5.581049567418119 - } - }, - "x": { - "title": "X", - "description": "The exogenous variables provided as a dictionary. Each key is a timestamp (string format: YYYY-MM-DD) and the corresponding value is a list of exogenous variable values at that time point. For example: {\"2021-01-01\": [0.1], \"2021-01-02\": [0.4]}. This should also include forecasting horizon (fh) additional timestamps to calculate the future values." + "type": "array", + "title": "Y", + "description": "Historic values of the target." + }, + "sizes": { + "items": { + "type": "integer" + }, + "type": "array", + "title": "Sizes", + "description": "Sizes of the individual series." + } + }, + "type": "object", + "required": ["y", "sizes"], + "title": "SeriesWithFutureExogenous" + }, + "SingleSeriesForecast": { + "properties": { + "fewshot_steps": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "title": "Fewshot Steps", + "description": "Deprecated. Please use finetune_steps instead.", + "deprecated": true + }, + "fewshot_loss": { + "anyOf": [ + { + "type": "string", + "enum": ["default", "mae", "mse", "rmse", "mape", "smape"] + }, + { + "type": "null" + } + ], + "title": "Fewshot Loss", + "description": "Deprecated. Please use finetune_loss instead.", + "deprecated": true + }, + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", + "default": "D" + }, + "level": { + "anyOf": [ + { + "items": { + "anyOf": [ + { + "type": "integer" }, - "clean_ex_first": { - "type": "boolean", - "title": "Clean Ex First", - "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", - "default": true + { + "type": "number" } + ] }, - "type": "object", - "title": "SingleSeriesInsampleForecast" - }, - "ValidationError": { - "properties": { - "loc": { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "integer" - } - ] - }, - "type": "array", - "title": "Location" - }, - "msg": { - "type": "string", - "title": "Message" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Level", + "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." + }, + "fh": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "Fh", + "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict.", + "default": 7 + }, + "y": { + "title": "Y", + "description": "The historical time series data provided as a dictionary. Each key is a timestamp (string format: YYYY-MM-DD) and the corresponding value is the observation at that time point. For example: {\"2021-01-01\": 0.1, \"2021-01-02\": 0.4}.", + "default": { + "2015-12-02": 4.390508031418598, + "2015-12-03": 5.721514930979356, + "2015-12-04": 4.822107008573151, + "2015-12-05": 4.359065463975175, + "2015-12-06": 3.3892383947112377, + "2015-12-07": 5.167152904533249, + "2015-12-08": 3.50069769010154, + "2015-12-09": 7.134184006256638, + "2015-12-10": 7.709302084008234, + "2015-12-11": 3.0675321506062216, + "2015-12-12": 6.333800304661317, + "2015-12-13": 4.231159358023236, + "2015-12-14": 4.5443564887514585, + "2015-12-15": 7.404773106341288, + "2015-12-16": 0.5682884655830955, + "2015-12-17": 0.6970343976123257, + "2015-12-18": 0.16174717952260576, + "2015-12-19": 6.660958764383504, + "2015-12-20": 6.225254007598804, + "2015-12-21": 6.960097185974553, + "2015-12-22": 7.828946737862112, + "2015-12-23": 6.393268513733789, + "2015-12-24": 3.6918348980234548, + "2015-12-25": 6.244233410291644, + "2015-12-26": 0.9461954069514658, + "2015-12-27": 5.119368170620191, + "2015-12-28": 1.1468262992723712, + "2015-12-29": 7.557351336396671, + "2015-12-30": 4.174786574000573, + "2015-12-31": 3.3172955199241887, + "2016-01-01": 2.1164448968370158, + "2016-01-02": 6.193869515473733, + "2016-01-03": 3.6492026577323884, + "2016-01-04": 4.547471590949188, + "2016-01-05": 0.15031840349084113, + "2016-01-06": 4.9410839766070165, + "2016-01-07": 4.896765781779371, + "2016-01-08": 4.935471974998055, + "2016-01-09": 7.549984628116993, + "2016-01-10": 5.454562392827867, + "2016-01-11": 2.876063204590288, + "2016-01-12": 3.4962556303947316, + "2016-01-13": 5.581049567418119 + } + }, + "x": { + "anyOf": [ + { + "additionalProperties": { + "items": { + "type": "number" + }, + "type": "array" + }, + "type": "object" + }, + { + "type": "null" + } + ], + "title": "X", + "description": "The exogenous variables provided as a dictionary. Each key is a timestamp (string format: YYYY-MM-DD) and the corresponding value is a list of exogenous variable values at that time point. For example: {\"2021-01-01\": [0.1], \"2021-01-02\": [0.4]}. This should also include forecasting horizon (fh) additional timestamps to calculate the future values." + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + }, + "finetune_steps": { + "type": "integer", + "minimum": 0.0, + "title": "Finetune Steps", + "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.", + "default": 0 + }, + "finetune_loss": { + "type": "string", + "enum": ["default", "mae", "mse", "rmse", "mape", "smape"], + "title": "Finetune Loss", + "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.", + "default": "default" + } + }, + "type": "object", + "title": "SingleSeriesForecast" + }, + "SingleSeriesInsampleForecast": { + "properties": { + "model": { + "title": "Model", + "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.", + "default": "timegpt-1" + }, + "freq": { + "type": "string", + "title": "Freq", + "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.", + "default": "D" + }, + "level": { + "anyOf": [ + { + "items": { + "anyOf": [ + { + "type": "integer" }, - "type": { - "type": "string", - "title": "Error Type" + { + "type": "number" } + ] }, - "type": "object", - "required": [ - "loc", - "msg", - "type" - ], - "title": "ValidationError" + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Level", + "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals." + }, + "y": { + "title": "Y", + "description": "The historical time series data provided as a dictionary. Each key is a timestamp (string format: YYYY-MM-DD) and the corresponding value is the observation at that time point. For example: {\"2021-01-01\": 0.1, \"2021-01-02\": 0.4}.", + "default": { + "2015-12-02": 4.390508031418598, + "2015-12-03": 5.721514930979356, + "2015-12-04": 4.822107008573151, + "2015-12-05": 4.359065463975175, + "2015-12-06": 3.3892383947112377, + "2015-12-07": 5.167152904533249, + "2015-12-08": 3.50069769010154, + "2015-12-09": 7.134184006256638, + "2015-12-10": 7.709302084008234, + "2015-12-11": 3.0675321506062216, + "2015-12-12": 6.333800304661317, + "2015-12-13": 4.231159358023236, + "2015-12-14": 4.5443564887514585, + "2015-12-15": 7.404773106341288, + "2015-12-16": 0.5682884655830955, + "2015-12-17": 0.6970343976123257, + "2015-12-18": 0.16174717952260576, + "2015-12-19": 6.660958764383504, + "2015-12-20": 6.225254007598804, + "2015-12-21": 6.960097185974553, + "2015-12-22": 7.828946737862112, + "2015-12-23": 6.393268513733789, + "2015-12-24": 3.6918348980234548, + "2015-12-25": 6.244233410291644, + "2015-12-26": 0.9461954069514658, + "2015-12-27": 5.119368170620191, + "2015-12-28": 1.1468262992723712, + "2015-12-29": 7.557351336396671, + "2015-12-30": 4.174786574000573, + "2015-12-31": 3.3172955199241887, + "2016-01-01": 2.1164448968370158, + "2016-01-02": 6.193869515473733, + "2016-01-03": 3.6492026577323884, + "2016-01-04": 4.547471590949188, + "2016-01-05": 0.15031840349084113, + "2016-01-06": 4.9410839766070165, + "2016-01-07": 4.896765781779371, + "2016-01-08": 4.935471974998055, + "2016-01-09": 7.549984628116993, + "2016-01-10": 5.454562392827867, + "2016-01-11": 2.876063204590288, + "2016-01-12": 3.4962556303947316, + "2016-01-13": 5.581049567418119 } + }, + "x": { + "title": "X", + "description": "The exogenous variables provided as a dictionary. Each key is a timestamp (string format: YYYY-MM-DD) and the corresponding value is a list of exogenous variable values at that time point. For example: {\"2021-01-01\": [0.1], \"2021-01-02\": [0.4]}. This should also include forecasting horizon (fh) additional timestamps to calculate the future values." + }, + "clean_ex_first": { + "type": "boolean", + "title": "Clean Ex First", + "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.", + "default": true + } }, - "securitySchemes": { - "HTTPBearer": { - "type": "http", - "description": "HTTPBearer", - "scheme": "bearer" - } - } + "type": "object", + "title": "SingleSeriesInsampleForecast" + }, + "ValidationError": { + "properties": { + "loc": { + "items": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + } + ] + }, + "type": "array", + "title": "Location" + }, + "msg": { + "type": "string", + "title": "Message" + }, + "type": { + "type": "string", + "title": "Error Type" + } + }, + "type": "object", + "required": ["loc", "msg", "type"], + "title": "ValidationError" + } }, - "servers": [ - { - "url": "https://api.nixtla.io" - } - ] -} \ No newline at end of file + "securitySchemes": { + "HTTPBearer": { + "type": "http", + "description": "HTTPBearer", + "scheme": "bearer" + } + } + }, + "servers": [ + { + "url": "https://api.nixtla.io" + } + ] +}