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fix links in models
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‎nbs/docs/models/ADIDA.ipynb‎

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"\n",
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":::{.callout-tip}\n",
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"\n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation).\n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html).\n",
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"\n",
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":::\n",
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"\n",
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"source": [
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"## Implementation of `ADIDA Model` with StatsForecast <a class=\"anchor\" id=\"implementation\"></a>\n",
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"\n",
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"To also know more about the parameters of the functions of the `ADIDA Model`, they are listed below. For more information, visit the [documentation](../../models#class-adida)\n",
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"To also know more about the parameters of the functions of the `ADIDA Model`, they are listed below. For more information, visit the [documentation](../../src/core/models.html#adida)\n",
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"\n",
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"```\n",
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"alias : str\n",
@@ -8681,7 +8681,7 @@
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"1. [Changquan Huang • Alla Petukhina. Springer series (2022). Applied Time Series Analysis and Forecasting with Python.](https://link.springer.com/book/10.1007/978-3-031-13584-2) \n",
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"2. Ivan Svetunkov. [Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)](https://openforecast.org/adam/)\n",
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"3. [James D. Hamilton. Time Series Analysis Princeton University Press, Princeton, New Jersey, 1st Edition, 1994.](https://press.princeton.edu/books/hardcover/9780691042893/time-series-analysis)\n",
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"4. [Nixtla ADIDA API](../../models#class-adida)\n",
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"4. [Nixtla ADIDA API](../../src/core/models.html#adida)\n",
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"5. [Pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).\n",
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"6. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html).\n",
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"7. [Seasonal periods- Rob J Hyndman](https://robjhyndman.com/hyndsight/seasonal-periods/)."

‎nbs/docs/models/ARCH.ipynb‎

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"## Loading libraries and data <a class=\"anchor\" id=\"loading\"></a>\n",
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"\n",
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":::{.callout-tip}\n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation../getting-started/0_Installation).\n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html).\n",
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":::\n",
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"\n",
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"Next, we import plotting libraries and configure the plotting style."
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"source": [
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"## Implementation of ARCH with StatsForecast <a class=\"anchor\" id=\"implementation\"></a>\n",
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"\n",
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"To also know more about the parameters of the functions of the `ARCH Model`, they are listed below. For more information, visit the [documentation](../../models#class-arch)\n",
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"To also know more about the parameters of the functions of the `ARCH Model`, they are listed below. For more information, visit the [documentation](../../src/core/models.html#arch)\n",
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"\n",
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"```\n",
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"p : int\n",
@@ -17790,7 +17790,7 @@
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"1. [Changquan Huang • Alla Petukhina. Springer series (2022). Applied Time Series Analysis and Forecasting with Python.](https://link.springer.com/book/10.1007/978-3-031-13584-2) \n",
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"2. [Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.](http://www.econ.uiuc.edu/~econ508/Papers/engle82.pdf).\n",
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"3. [James D. Hamilton. Time Series Analysis Princeton University Press, Princeton, New Jersey, 1st Edition, 1994.](https://press.princeton.edu/books/hardcover/9780691042893/time-series-analysis)\n",
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"4. [Nixtla ARCH API](../../models#class-arch)\n",
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"4. [Nixtla ARCH API](../../src/core/models.html#arch)\n",
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"5. [Pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).\n",
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"6. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html).\n",
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"7. [Seasonal periods- Rob J Hyndman](https://robjhyndman.com/hyndsight/seasonal-periods/)."

‎nbs/docs/models/ARIMA.ipynb‎

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"## Loading libraries and data <a class=\"anchor\" id=\"loading\"></a>\n",
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"\n",
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":::{.callout-tip}\n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation../getting-started/0_Installation). \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html). \n",
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":::\n",
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"\n",
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"Next, we import plotting libraries and configure the plotting style."
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"source": [
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"## References <a class=\"anchor\" id=\"references\"></a>\n",
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"\n",
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"1. [Nixtla ARIMA API](../../models#class-arima)\n",
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"1. [Nixtla ARIMA API](../../src/core/models.html#arima)\n",
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"2. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html)."
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},

‎nbs/docs/models/AutoARIMA.ipynb‎

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"## Loading libraries and data <a class=\"anchor\" id=\"loading\"></a>\n",
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"\n",
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":::{.callout-tip} \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation../getting-started/0_Installation). \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html). \n",
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":::\n",
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"\n",
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"Next, we import plotting libraries and configure the plotting style."
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"## References <a class=\"anchor\" id=\"references\"></a>\n",
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"\n",
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"\n",
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"1. [Nixtla AutoARIMA API](../../models#class-autoarima)\n",
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"1. [Nixtla AutoARIMA API](../../src/core/models.html#autoarima)\n",
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"2. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html).\n"
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]
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},

‎nbs/docs/models/AutoCES.ipynb‎

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"## Loading libraries and data <a class=\"anchor\" id=\"loading\"></a>\n",
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"\n",
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":::{.callout-tip}\n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation../getting-started/0_Installation).\n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html).\n",
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":::\n",
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"\n",
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"Next, we import plotting libraries and configure the plotting style."
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"source": [
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"## References <a class=\"anchor\" id=\"references\"></a>\n",
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"\n",
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"1. [Nixtla AutoCES API](../../models#class-autoces)\n",
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"1. [Nixtla AutoCES API](../../src/core/models.html#autoces)\n",
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"2. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html).\n",
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"3. [Ivan Svetunkov, Nikolaos Kourentzes, John Keith Ord, \"Complex exponential smoothing\"](https://onlinelibrary.wiley.com/doi/full/10.1002/nav.22074)"
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]

‎nbs/docs/models/AutoETS.ipynb‎

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"## Loading libraries and data <a class=\"anchor\" id=\"loading\"></a>\n",
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"\n",
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":::{.callout-tip} \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation../getting-started/0_Installation). \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html). \n",
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":::\n",
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"\n",
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"Next, we import plotting libraries and configure the plotting style."
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"source": [
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"## References <a class=\"anchor\" id=\"references\"></a>\n",
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"\n",
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"1. [Nixtla AutoETS API](../../models#class-autoets)\n",
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"1. [Nixtla AutoETS API](../../src/core/models.html#autoets)\n",
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"2. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html).\n"
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‎nbs/docs/models/AutoRegressive.ipynb‎

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"## Loading libraries and data <a class=\"anchor\" id=\"loading\"></a>\n",
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"\n",
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":::{.callout-tip} \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation../getting-started/0_Installation). \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html). \n",
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":::\n",
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"\n",
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"Next, we import plotting libraries and configure the plotting style."
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"\n",
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"1. [Changquan Huang • Alla Petukhina. Springer series (2022). Applied Time Series Analysis and Forecasting with Python.](https://link.springer.com/book/10.1007/978-3-031-13584-2) \n",
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"2. [Jose A. Fiorucci, Tiago R. Pellegrini, Francisco Louzada, Fotios Petropoulos, Anne B. Koehler (2016). “Models for optimising the theta method and their relationship to state space models”. International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207016300243).\n",
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"3. [Nixtla AutoRegressive API](../../models#class-autoregressive)\n",
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"3. [Nixtla AutoRegressive API](../../src/core/models.html#autoregressive)\n",
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"4. [Pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).\n",
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"5. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html)\n",
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"6. [Seasonal periods- Rob J Hyndman](https://robjhyndman.com/hyndsight/seasonal-periods/)."

‎nbs/docs/models/AutoTheta.ipynb‎

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"## Loading libraries and data <a class=\"anchor\" id=\"loading\"></a>\n",
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":::{.callout-tip} \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation). \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html). \n",
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":::\n",
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"## References <a class=\"anchor\" id=\"references\"></a>\n",
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"\n",
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"1. [Jose A. Fiorucci, Tiago R. Pellegrini, Francisco Louzada, Fotios Petropoulos, Anne B. Koehler (2016). “Models for optimising the theta method and their relationship to state space models”. International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207016300243).\n",
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"2. [Nixtla AutoTheta API](../../models#class-autotheta)\n",
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"2. [Nixtla AutoTheta API](../../src/core/models.html#autotheta)\n",
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"3. [Pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).\n",
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"4. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html)\n",
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"5. [Seasonal periods- Rob J Hyndman](https://robjhyndman.com/hyndsight/seasonal-periods/)."

‎nbs/docs/models/CrostonClassic.ipynb‎

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"## Loading libraries and data <a class=\"anchor\" id=\"loading\"></a>\n",
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":::{.callout-tip} \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation../getting-started/0_Installation). \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html). \n",
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"## Implementation of CrostonClassic with StatsForecast <a class=\"anchor\" id=\"implementation\"></a>\n",
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"\n",
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"To also know more about the parameters of the functions of the `CrostonClassic Model`, they are listed below. For more information, visit the [documentation](../../models#class-crostonclassic)\n",
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"To also know more about the parameters of the functions of the `CrostonClassic Model`, they are listed below. For more information, visit the [documentation](../../src/core/models.html#crostonclassic)\n",
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"\n",
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"```\n",
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"alias : str\n",
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"1. [Changquan Huang • Alla Petukhina. Springer series (2022). Applied Time Series Analysis and Forecasting with Python.](https://link.springer.com/book/10.1007/978-3-031-13584-2) \n",
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"2. Ivan Svetunkov. [Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)](https://openforecast.org/adam/)\n",
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"3. [James D. Hamilton. Time Series Analysis Princeton University Press, Princeton, New Jersey, 1st Edition, 1994.](https://press.princeton.edu/books/hardcover/9780691042893/time-series-analysis)\n",
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"4. [Nixtla CrostonClassic API](../../models#class-crostonclassic)\n",
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"4. [Nixtla CrostonClassic API](../../src/core/models.html#crostonclassic)\n",
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"5. [Pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).\n",
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"6. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html).\n",
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"7. [Seasonal periods- Rob J Hyndman](https://robjhyndman.com/hyndsight/seasonal-periods/)."

‎nbs/docs/models/CrostonOptimized.ipynb‎

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"## Loading libraries and data <a class=\"anchor\" id=\"loading\"></a>\n",
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"\n",
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":::{.callout-tip} \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation../getting-started/0_Installation). \n",
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"Statsforecast will be needed. To install, see [instructions](../getting-started/installation.html). \n",
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":::\n",
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"\n",
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"Next, we import plotting libraries and configure the plotting style."
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"1. [Changquan Huang • Alla Petukhina. Springer series (2022). Applied Time Series Analysis and Forecasting with Python.](https://link.springer.com/book/10.1007/978-3-031-13584-2) \n",
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"2. Ivan Svetunkov. [Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)](https://openforecast.org/adam/)\n",
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"3. [James D. Hamilton. Time Series Analysis Princeton University Press, Princeton, New Jersey, 1st Edition, 1994.](https://press.princeton.edu/books/hardcover/9780691042893/time-series-analysis)\n",
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"4. [Nixtla CrostonOptimized API](../../models#class-crostonoptimized)\n",
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"4. [Nixtla CrostonOptimized API](../../src/core/models.html#crostonoptimized)\n",
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"5. [Pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).\n",
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"6. [Rob J. Hyndman and George Athanasopoulos (2018). “Forecasting Principles and Practice (3rd ed)”](https://otexts.com/fpp3/tscv.html).\n",
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"7. [Seasonal periods- Rob J Hyndman](https://robjhyndman.com/hyndsight/seasonal-periods/)."

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