fix links in getting-started

Author: deven367

nbs/docs/getting-started/getting_Started_complete.ipynb

@@ -40,7 +40,7 @@
    "source": [
     ":::{.callout-warning collapse=\"true\"}\n",
     "## Prerequisites\n",
-    "This Guide assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](./1_Getting_Started_short).\n",
+    "This Guide assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](./getting_started_short.html).\n",
     ":::\n",
     "\n",
     "Follow this article for a step-by-step guide on building a production-ready forecasting pipeline for multiple time series. \n",
@@ -65,15 +65,15 @@
     "## Not Covered in this guide\n",
     "\n",
     "* Forecasting at scale using clusters on the cloud. \n",
-    "    * [Forecast the M5 Dataset in 5min](../experiments/ETS_ray_m5) using Ray clusters.\n",
-    "    * [Forecast the M5 Dataset in 5min](../experiments/Prophet_spark_m5) using Spark clusters.\n",
+    "    * [Forecast the M5 Dataset in 5min](../experiments/ets_ray_m5.html) using Ray clusters.\n",
+    "    * [Forecast the M5 Dataset in 5min](../experiments/prophet_spark_m5.html) using Spark clusters.\n",
     "    * Learn how to predict [1M series in less than 30min](https://www.anyscale.com/blog/how-nixtla-uses-ray-to-accurately-predict-more-than-a-million-time-series).\n",
     "\n",
     "* Training models on Multiple Seasonalities. \n",
-    "    * Learn to use multiple seasonality in this [Electricity Load forecasting](../tutorials/ElectricityLoadForecasting) tutorial.\n",
+    "    * Learn to use multiple seasonality in this [Electricity Load forecasting](../tutorials/electricityloadforecasting.html) tutorial.\n",
     "\n",
     "* Using external regressors or exogenous variables\n",
-    "    * Follow this tutorial to [include exogenous variables](../how-to-guides/Exogenous) like weather or holidays or static variables like category or family. \n",
+    "    * Follow this tutorial to [include exogenous variables](../how-to-guides/exogenous.html) like weather or holidays or static variables like category or family. \n",
     "\n",
     "* Comparing StatsForecast with other popular libraries.\n",
     "    * You can reproduce our benchmarks [here](https://github.com/Nixtla/statsforecast/tree/main/experiments).\n",
@@ -86,7 +86,7 @@
    "source": [
     "## Install libraries\n",
     "\n",
-    "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](./0_Installation)."
+    "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](./installation.html)."
    ]
   },
   {
@@ -306,7 +306,7 @@
     "\n",
     "* **Theta Models:**  fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts. Examples: Theta, DynamicTheta\n",
     "\n",
-    "Here you can check the complete list of [models](../../src/core/models_intro) .\n",
+    "Here you can check the complete list of [models](../../src/core/models_intro.html) .\n",
     "\n",
     "For this example we will use:\n",
     "\n",
@@ -365,7 +365,7 @@
    "source": [
     "We fit the models by instantiating a new `StatsForecast` object with the following parameters:\n",
     "\n",
-    "* `models`: a list of models. Select the models you want from [models](../../src/core/models_intro)  and import them.\n",
+    "* `models`: a list of models. Select the models you want from [models](../../src/core/models_intro.html)  and import them.\n",
     "\n",
     "* `freq`: a string indicating the frequency of the data. (See [pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n",
     "\n",

nbs/docs/getting-started/getting_Started_complete_polars.ipynb

@@ -34,7 +34,7 @@
    "source": [
     ":::{.callout-warning collapse=\"true\"}\n",
     "## Prerequisites\n",
-    "This Guide assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](./Getting_Started_short)\n",
+    "This Guide assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](./getting_started_short.html)\n",
     ":::\n",
     "\n",
     "Follow this article for a step-by-step guide on building a production-ready forecasting pipeline for multiple time series. \n",
@@ -59,15 +59,15 @@
     "## Not Covered in this guide\n",
     "\n",
     "* Forecasting at scale using clusters on the cloud. \n",
-    "    * [Forecast the M5 Dataset in 5min](../experiments/ETS_ray_m5) using Ray clusters.\n",
-    "    * [Forecast the M5 Dataset in 5min](../experiments/Prophet_spark_m5) using Spark clusters.\n",
+    "    * [Forecast the M5 Dataset in 5min](../experiments/ets_ray_m5.html) using Ray clusters.\n",
+    "    * [Forecast the M5 Dataset in 5min](../experiments/prophet_spark_m5.html) using Spark clusters.\n",
     "    * Learn how to predict [1M series in less than 30min](https://www.anyscale.com/blog/how-nixtla-uses-ray-to-accurately-predict-more-than-a-million-time-series).\n",
     "\n",
     "* Training models on Multiple Seasonalities. \n",
-    "    * Learn to use multiple seasonality in this [Electricity Load forecasting](../tutorials/ElectricityLoadForecasting) tutorial.\n",
+    "    * Learn to use multiple seasonality in this [Electricity Load forecasting](../tutorials/electricityloadforecasting.html) tutorial.\n",
     "\n",
     "* Using external regressors or exogenous variables\n",
-    "    * Follow this tutorial to [include exogenous variables](../how-to-guides/Exogenous) like weather or holidays or static variables like category or family. \n",
+    "    * Follow this tutorial to [include exogenous variables](../how-to-guides/exogenous.html) like weather or holidays or static variables like category or family. \n",
     "\n",
     "* Comparing StatsForecast with other popular libraries.\n",
     "    * You can reproduce our benchmarks [here](https://github.com/Nixtla/statsforecast/tree/main/experiments).\n",
@@ -80,7 +80,7 @@
    "source": [
     "## Install libraries\n",
     "\n",
-    "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](./0_Installation)."
+    "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](./installation.html)."
    ]
   },
   {
@@ -278,7 +278,7 @@
     "\n",
     "* **Theta Models:**  fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts. Examples: Theta, DynamicTheta\n",
     "\n",
-    "Here you can check the complete list of [models](../models_intro.qmd).\n",
+    "Here you can check the complete list of [models](../../src/core/models_intro.html).\n",
     "\n",
     "For this example we will use:\n",
     "\n",
@@ -337,7 +337,7 @@
    "source": [
     "We fit the models by instantiating a new `StatsForecast` object with the following parameters:\n",
     "\n",
-    "* `models`: a list of models. Select the models you want from [models](../../src/core/models_intro) and import them.\n",
+    "* `models`: a list of models. Select the models you want from [models](../../src/core/models_intro.html) and import them.\n",
     "\n",
     "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).) This is also available with Polars.\n",
     "\n",

nbs/docs/getting-started/getting_Started_short.ipynb

@@ -18,7 +18,7 @@
     "`StatsForecast` follows the sklearn model API. For this minimal example, you will create an instance of the StatsForecast class and then call its `fit` and `predict` methods. We recommend this option if speed is not paramount and you want to explore the fitted values and parameters. \n",
     "\n",
     ":::{.callout-tip}\n",
-    "If you want to forecast many series, we recommend using the `forecast` method. Check this [Getting Started with multiple time series](../../2_Getting_Started_complete) guide. \n",
+    "If you want to forecast many series, we recommend using the `forecast` method. Check this [Getting Started with multiple time series](./getting_started_complete.html) guide. \n",
     ":::\n",
     "\n",
     "The input to StatsForecast is always a data frame in [long format](https://www.theanalysisfactor.com/wide-and-long-data/) with three columns: `unique_id`, `ds` and `y`:\n",
@@ -32,7 +32,7 @@
     "\n",
     "As an example, let\u2019s look at the US Air Passengers dataset. This time series consists of monthly totals of a US airline passengers from 1949 to 1960. The CSV is available [here](https://www.kaggle.com/datasets/chirag19/air-passengers).\n",
     "\n",
-    "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](../../0_Installation).\n",
+    "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](./installation.html).\n",
     "\n",
     "First, we\u2019ll  import the data:"
    ]
@@ -146,8 +146,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We fit the model by instantiating a new `StatsForecast` object with its [two required parameters](../../models):\n",
-    "* `models`: a list of models. Select the models you want from [models](../../models) and import them. For this example, we will use a `AutoARIMA` model. We set `season_length` to 12 because we expect seasonal effects every 12 months. (See: [Seasonal periods](https://robjhyndman.com/hyndsight/seasonal-periods/))\n",
+    "We fit the model by instantiating a new `StatsForecast` object with its [two required parameters](../../src/core/models.html):\n",
+    "* `models`: a list of models. Select the models you want from [models](../../src/core/models.html) and import them. For this example, we will use a `AutoARIMA` model. We set `season_length` to 12 because we expect seasonal effects every 12 months. (See: [Seasonal periods](https://robjhyndman.com/hyndsight/seasonal-periods/))\n",
     "\n",
     "* `freq`: a string indicating the frequency of the data. (See [pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n",
     "\n",
@@ -337,9 +337,9 @@
     ":::{.callout-tip}\n",
     "## Next Steps\n",
     "\n",
-    "* Build and end-to-end forecasting pipeline following best practices in [End to End Walkthrough](./2_Getting_Started_complete)\n",
-    "* [Forecast millions of series](../experiments/Prophet_spark_m5) in a scalable cluster in the cloud using Spark and Nixtla\n",
-    "* [Detect anomalies](../tutorials/AnomalyDetection) in your past observations\n",
+    "* Build and end-to-end forecasting pipeline following best practices in [End to End Walkthrough](./getting_started_complete.html)\n",
+    "* [Forecast millions of series](../experiments/prophet_spark_m5.html) in a scalable cluster in the cloud using Spark and Nixtla\n",
+    "* [Detect anomalies](../tutorials/anomalydetection.html) in your past observations\n",
     ":::"
    ]
   },