Merge branch 'master' into feat/tz_aware_dta

CHANGELOG.md

@@ -16,6 +16,8 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
   - Added callback `darts.utils.callbacks.TFMProgressBar` to customize at which model stages to display the progress bar. [#2020](https://github.com/unit8co/darts/pull/2020) by [Dennis Bader](https://github.com/dennisbader).
 - Improvements to documentation:
   - Adapted the example notebooks to properly apply data transformers and avoid look-ahead bias. [#2020](https://github.com/unit8co/darts/pull/2020) by [Samriddhi Singh](https://github.com/SimTheGreat).
+- 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).
 

darts/models/forecasting/xgboost.py

@@ -25,6 +25,10 @@
 
 logger = get_logger(__name__)
 
+# Check whether we are running xgboost >= 2.0.0 for quantile regression
+tokens = xgb.__version__.split(".")
+xgb_200_or_above = int(tokens[0]) >= 2
+
 
 def xgb_quantile_loss(labels: np.ndarray, preds: np.ndarray, quantile: float):
     """Custom loss function for XGBoost to compute quantile loss gradient.
@@ -184,8 +188,12 @@ def encode_year(idx):
             if likelihood in {"poisson"}:
                 self.kwargs["objective"] = f"count:{likelihood}"
             elif likelihood == "quantile":
+                if xgb_200_or_above:
+                    # leverage built-in Quantile Regression
+                    self.kwargs["objective"] = "reg:quantileerror"
                 self.quantiles, self._median_idx = self._prepare_quantiles(quantiles)
                 self._model_container = self._get_model_container()
+
             self._rng = np.random.default_rng(seed=random_state)  # seed for sampling
 
         super().__init__(
@@ -250,12 +258,17 @@ def fit(
                 )
             ]
 
+        # TODO: XGBRegressor supports multi quantile reqression which we could leverage in the future
+        #  see https://xgboost.readthedocs.io/en/latest/python/examples/quantile_regression.html
         if self.likelihood == "quantile":
             # empty model container in case of multiple calls to fit, e.g. when backtesting
             self._model_container.clear()
             for quantile in self.quantiles:
-                obj_func = partial(xgb_quantile_loss, quantile=quantile)
-                self.kwargs["objective"] = obj_func
+                if xgb_200_or_above:
+                    self.kwargs["quantile_alpha"] = quantile
+                else:
+                    objective = partial(xgb_quantile_loss, quantile=quantile)
+                    self.kwargs["objective"] = objective
                 self.model = xgb.XGBRegressor(**self.kwargs)
 
                 super().fit(