udpate changelog (#2834)

Author: dennisbader

CHANGELOG.md

@@ -23,7 +23,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Improved**
 
-- Improvements to `TimeSeries`:
+- Improvements to `TimeSeries` :
   - 🚀🚀 Migrated the `TimeSeries` backend from `xarray` to `numpy`, resulting in **drastic performance improvements** throughout Darts without any changes to the user experience! We're talking about free speed boosts for creating and manipulating series of **up to multiple orders of magnitude**, which will positively affect any downstream task. You can find some benchmarks in the images [here](https://github.com/unit8co/darts/pull/2807). [#2807](https://github.com/unit8co/darts/pull/2807) by [Dennis Bader](https://github.com/dennisbader).
     - Added parameter `copy: bool = True` to all TimeSeries constructor and factory methods (`TimeSeries.from_*`). This allows to create your time series without copying the data. Defaults to `True` to maintain the existing behavior.
     - Method `__init__()` can now also be used to create new series in a similar way as `from_times_and_values()`.
@@ -36,14 +36,14 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
     - 🔴 Removed method `quantiles_df()`. Use `quantile().to_dataframe()` instead.
   - Added method `schema()` to `TimeSeries` to extract the schema from a series. It contains information about the time index, columns, static covariates, hierarchy, and metadata. [#2802](https://github.com/unit8co/darts/pull/2802) by [Dennis Bader](https://github.com/dennisbader).
 - Improvements to forecasting models:
-  - 🚀🚀 Added support for training any `SKLearnModel` (`RegressionModel`) and `TorchForecastingModel` with **stridden training samples** by passing parameter `stride` to `fit()`. This allows to reduce the size of the training set or apply elaborate training scenarios. [#2624](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou)
+  - 🚀🚀 Added support for training any `SKLearnModel` (`RegressionModel`) and `TorchForecastingModel` with **stridden training samples** by passing parameter `stride` to `fit()`. This allows to reduce the size of the training set or apply elaborate training scenarios. [#2624](https://github.com/unit8co/darts/pull/2624) by [Antoine Madrona](https://github.com/madtoinou)
   - Added parameter `random_state` to all `ForecastingModel` forecast methods (predict, historical forecasts, ...) to control the randomness of probabilistic forecasts. [#2808](https://github.com/unit8co/darts/pull/2808) and [#2816](https://github.com/unit8co/darts/pull/2816) by [Gabriel Margaria](https://github.com/Jaco-Pastorius).
   - Improvements `NLinearModel`: Default value for `normalize` changed from `False` to `True` to reflect the source paper. [#2757](https://github.com/unit8co/darts/pull/2757) by [Timon Erhart](https://github.com/turbotimon).
   - Renamed some regression models for consistency and clarity reasons. [#2774](https://github.com/unit8co/darts/pull/2774) by [Jonas Blanc](https://github.com/jonasblanc).
     - 🟠 Renamed `RegressionModel` to `SKLearnModel`. Using `RegressionModel` will raise a deprecation warning.
     - 🟠 Renamed `RandomForest` to `RandomForestModel`. Using `RandomForest` will raise a deprecation warning.
     - 🔴 Renamed `RegressionModelWithCategoricalCovariates` to `SKLearnModelWithCategoricalCovariates`. Removed `RegressionModelWithCategoricalCovariates`
-- Improvements to `TorchForecastingModel`: [#2802](https://github.com/unit8co/darts/pull/2802) by [Dennis Bader](https://github.com/dennisbader).
+- Improvements to `TorchForecastingModel` : [#2802](https://github.com/unit8co/darts/pull/2802) by [Dennis Bader](https://github.com/dennisbader).
   - 🚀🚀 Drastically improved prediction speed which is now up to 5.4 times as fast before. This affects `predict()`, `historical_forecasts()`, `backtest()`, `gridsearch()` and `residuals()`.
   - Added parameter `values_only` to method `predict_from_dataset()` which will return a tuple of (prediction `np.ndarray`, target series schema, prediction start time) instead of `TimeSeries` objects. This allows to completely bypass `TimeSeries` for model inference with custom datasets.
 - 🔴 Improvements to `TorchForecastingModel` datasets: [#2798](https://github.com/unit8co/darts/pull/2798) by [Dennis Bader](https://github.com/dennisbader).
@@ -56,15 +56,15 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
     - Training datasets: Tuple[past target, past cov, historic future cov, future cov, static cov, sample weight, future target].
     - Inference datasets: Tuple[past target, past cov, future past cov, historic future cov, future cov, static cov, target series schema, pred start time]
   - `HorizonBasedTorchTrainingDataset` now also supports future covariates.
-  - Added parameter `stride` to `*TorchTrainingDatset` to apply a stride between two consecutive training samples.  [#2624](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou)
+  - Added parameter `stride` to `*TorchTrainingDatset` to apply a stride between two consecutive training samples.  [#2624](https://github.com/unit8co/darts/pull/2624) by [Antoine Madrona](https://github.com/madtoinou)
 
 **Fixed**
 
 - Fixed some issues in `NLinearModel` with `normalize=True` that resulted in decreased predictive accuracy. Using `shared_weights=True` and auto-regressive forecasting now work properly. [#2757](https://github.com/unit8co/darts/pull/2757) by [Timon Erhart](https://github.com/turbotimon).
-- Fixed a bug when training a `TorchForecastingModel`, where using certain `torchmetrics` that require a 2D model output (e.g. R2Score) raised an error. [He Weilin](https://github.com/cnhwl).
+- Fixed a bug when training a `TorchForecastingModel`, where using certain `torchmetrics` that require a 2D model output (e.g. R2Score) raised an error. [#2786](https://github.com/unit8co/darts/pull/2786) by [He Weilin](https://github.com/cnhwl).
 - Fixed an issue with `TorchForecastingModel.predict()` on Windows machines, where tensor movement from GPU to CPU was not synchronized. [#2829](https://github.com/unit8co/darts/pull/2829) by [Dennis Bader](https://github.com/dennisbader).
 - Fixed a bug with `SKLearnModel.__str__()` which raised an error when the model was wrapped by Darts' MultioutputRegressor. [#2811](https://github.com/unit8co/darts/pull/2811) by [Dennis Bader](https://github.com/dennisbader).
-- Fixed the default `_ShapMethod` for three tree based regression models (HistGradientBoostingRegressor, ExtraTreesRegressor and RandomForestRegressor). [#2821](https://https://github.com/unit8co/darts/pull/2821) by [Rijk van der Meulen](https://github.com/rijkvandermeulen).
+- Fixed the default `_ShapMethod` for three tree based regression models (HistGradientBoostingRegressor, ExtraTreesRegressor and RandomForestRegressor). [#2821](https://github.com/unit8co/darts/pull/2821) by [Rijk van der Meulen](https://github.com/rijkvandermeulen).
 - Fixed a bug in `StatsForecastModel` where custom future covariates support (OLS) resulted in a feature error with target series that contain static covariates. [#2824](https://github.com/unit8co/darts/pull/2824) by [Dennis Bader](https://github.com/dennisbader).
 
 **Dependencies**