Fix/add fit/predict_kwargs argument to historical_forecasts

CHANGELOG.md

@@ -20,9 +20,10 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 - Improvements to Regression Models:
   - `XGBModel` now leverages XGBoost's native Quantile Regression support that was released in version 2.0.0 for improved probabilistic forecasts. [#2051](https://github.com/unit8co/darts/pull/2051) by [Dennis Bader](https://github.com/dennisbader).
 - Other improvements:
-  - Added support for time index time zone conversion with parameter `tz` before generating/computing holidays and datetime attributes. Support was added to all Time Axis Encoders (standalone encoders and forecasting models' `add_encoders`, time series generation utils functions `holidays_timeseries()` and `datetime_attribute_timeseries()`, and `TimeSeries` methods `add_datetime_attribute()` and `add_holidays()`. [#2054](https://github.com/unit8co/darts/pull/2054) by [Dennis Bader](https://github.com/dennisbader).
+  - Added support for time index time zone conversion with parameter `tz` before generating/computing holidays and datetime attributes. Support was added to all Time Axis Encoders, standalone encoders and forecasting models' `add_encoders`, time series generation utils functions `holidays_timeseries()` and `datetime_attribute_timeseries()`, and `TimeSeries` methods `add_datetime_attribute()` and `add_holidays()`. [#2054](https://github.com/unit8co/darts/pull/2054) by [Dennis Bader](https://github.com/dennisbader).
   - Added optional keyword arguments dict `kwargs` to `ExponentialSmoothing` that will be passed to the constructor of the underlying `statsmodels.tsa.holtwinters.ExponentialSmoothing` model. [#2059](https://github.com/unit8co/darts/pull/2059) by [Antoine Madrona](https://github.com/madtoinou).
   - Added new dataset `ElectricityConsumptionZurichDataset`: The dataset contains the electricity consumption of households in Zurich, Switzerland from 2015-2022 on different grid levels. We also added weather measurements for Zurich which can be used as covariates for modelling. [#2039](https://github.com/unit8co/darts/pull/2039) by [Antoine Madrona](https://github.com/madtoinou) and [Dennis Bader](https://github.com/dennisbader).
+  - Added new arguments `fit_kwargs` and `predict_kwargs` to `historical_forecasts()`, `backtest()` and `gridsearch()` that will be passed to the model's `fit()` and / or `predict` methods. E.g., you can now set a batch size, static validation series, ... depending on the model support. [#2050](https://github.com/unit8co/darts/pull/2050) by [Antoine Madrona](https://github.com/madtoinou)
 
 **Fixed**
 - Fixed a bug when calling optimized `historical_forecasts()` for a `RegressionModel` trained with unequal component-specific lags. [#2040](https://github.com/unit8co/darts/pull/2040) by [Antoine Madrona](https://github.com/madtoinou).

darts/datasets/__init__.py

@@ -829,7 +829,8 @@ class ElectricityConsumptionZurichDataset(DatasetLoaderCSV):
     To simplify the dataset, the measurements from the Zch_Schimmelstrasse and Zch_Rosengartenstrasse weather
     stations are discarded to keep only the data recorded in the Zch_Stampfenbachstrasse station.
 
-    Both dataset sources are updated continuously, but this dataset only retrains values between 2015 and 2022.
+    Both dataset sources are updated continuously, but this dataset only retrains values between 2015-01-01 and
+    2022-08-31.
     The time index was converted from CET time zone to UTC.
 
     Components Descriptions:
@@ -864,7 +865,7 @@ def pre_process_dataset(dataset_path):
             # extract pre-determined period
             df = df.loc[
                 (pd.Timestamp("2015-01-01") <= df.index)
-                & (df.index <= pd.Timestamp("2022-12-31"))
+                & (df.index <= pd.Timestamp("2022-08-31"))
             ]
             # download and preprocess the weather information
             df_weather = self._download_weather_data()
@@ -894,7 +895,7 @@ def pre_process_dataset(dataset_path):
                     "ewz_stromabgabe_netzebenen_stadt_zuerich/"
                     "download/ewz_stromabgabe_netzebenen_stadt_zuerich.csv"
                 ),
-                hash="c2fea1a0974611ff1c276abcc1d34619",
+                hash="a019125b7f9c1afeacb0ae60ce7455ef",
                 header_time="Timestamp",
                 freq="15min",
                 pre_process_csv_fn=pre_process_dataset,
@@ -919,6 +920,6 @@ def _download_weather_data():
         )
         df = df.loc[
             (pd.Timestamp("2015-01-01") <= df.index)
-            & (df.index <= pd.Timestamp("2022-12-31"))
+            & (df.index <= pd.Timestamp("2022-08-31"))
         ]
         return df

darts/models/forecasting/baselines.py

@@ -332,8 +332,12 @@ def fit(
             for model in self.forecasting_models:
                 model._fit_wrapper(
                     series=series,
-                    past_covariates=past_covariates,
-                    future_covariates=future_covariates,
+                    past_covariates=past_covariates
+                    if model.supports_past_covariates
+                    else None,
+                    future_covariates=future_covariates
+                    if model.supports_future_covariates
+                    else None,
                 )
 
         return self