Fix/documentation url of statsforecast models (#2610)

* Update sf_auto_arima.py

* Update sf_auto_ets.py

* Update sf_auto_ces.py

* Update sf_auto_theta.py

* Update CHANGELOG.md

CHANGELOG.md

@@ -14,6 +14,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 - Improvements to `ForecastingModel`: Improved `start` handling for historical forecasts, backtest, residuals, and gridsearch. If `start` is not within the trainable / forecastable points, uses the closest valid start point that is a round multiple of `stride` ahead of start. Raises a ValueError, if no valid start point exists. This guarantees that all historical forecasts are `n * stride` points away from start, and will simplify many downstream tasks. [#2560](https://github.com/unit8co/darts/issues/2560) by [Dennis Bader](https://github.com/dennisbader).
 - Added `data_transformers` argument to `historical_forecasts`, `backtest`, `residuals`, and `gridsearch` that allow to automatically apply `DataTransformer` and/or `Pipeline` to the input series without data-leakage (fit on historic window of input series, transform the input series, and inverse transform the forecasts). [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou) and [Jan Fidor](https://github.com/JanFidor)
 - Added `series_idx` argument to `DataTransformer` that allows users to use only a subset of the transformers when `global_fit=False` and severals series are used. [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou)
+- Updated the Documentation URL of `Statsforecast` models. [#2610](https://github.com/unit8co/darts/pull/2610) by [He Weilin](https://github.com/cnhwl).
 
 **Fixed**
 

darts/models/forecasting/sf_auto_arima.py

@@ -32,7 +32,7 @@ def __init__(
         It is probabilistic, whereas :class:`AutoARIMA` is not.
 
         We refer to the `statsforecast AutoARIMA documentation
-        <https://nixtla.github.io/statsforecast/src/core/models.html#autoarima>`_
+        <https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoarima>`_
         for the exhaustive documentation of the arguments.
 
         Parameters

darts/models/forecasting/sf_auto_ces.py

@@ -18,7 +18,7 @@ def __init__(self, *autoces_args, **autoces_kwargs):
         <https://onlinelibrary.wiley.com/doi/full/10.1002/nav.22074>
 
         We refer to the `statsforecast AutoCES documentation
-        <https://nixtla.github.io/statsforecast/src/core/models.html#autoces>`_
+        <https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoces>`_
         for the exhaustive documentation of the arguments.
 
         Parameters

darts/models/forecasting/sf_auto_ets.py

@@ -31,7 +31,7 @@ def __init__(
         on Numba and jit compilation.
 
         We refer to the `statsforecast AutoETS documentation
-        <https://nixtla.github.io/statsforecast/src/core/models.html#autoets>`_
+        <https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoets>`_
         for the exhaustive documentation of the arguments.
 
         In addition to the StatsForecast implementation, this model can handle future covariates. It does so by first

darts/models/forecasting/sf_auto_theta.py

@@ -26,7 +26,7 @@ def __init__(self, *autotheta_args, **autotheta_kwargs):
         It is probabilistic, whereas :class:`FourTheta` is not.
 
         We refer to the `statsforecast AutoTheta documentation
-        <https://nixtla.github.io/statsforecast/src/core/models.html#autotheta>`_
+        <https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotheta>`_
         for the exhaustive documentation of the arguments.
 
         Parameters