Release 0.35.0 (#2783)

* bump u8darts 0.34.0 to 0.35.0

* update changelog

* update changelog

* update readme

* update docs

Author: dennisbader

CHANGELOG.md

@@ -5,37 +5,52 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 ## [Unreleased](https://github.com/unit8co/darts/tree/master)
 
-[Full Changelog](https://github.com/unit8co/darts/compare/0.34.0...master)
+[Full Changelog](https://github.com/unit8co/darts/compare/0.35.0...master)
 
 ### For users of the library:
 
 **Improved**
 
-- Added support for `numpy>=2.0.0`. [#2771](https://github.com/unit8co/darts/pull/2771) by [Dennis Bader](https://github.com/dennisbader).
-- Added support for categorical covariates to  `CatBoostModel`. You can now define categorical components at model creation with parameters `categorical_*_covariates: List[str]` for past, future, and static covariates. [#2733](https://github.com/unit8co/darts/pull/2750) by [Jonas Blanc](https://github.com/jonasblanc).
-- Added new forecasting model: `AutoMFLES`, a simple time series method based on gradient boosting time series decomposition as proposed in [this repository](https://github.com/tblume1992/MFLES). This implementation is based on [AutoMFLES](https://nixtlaverse.nixtla.io/statsforecast/docs/models/mfles.html) from Nixtla's `statsforecasts` library. [#2747](https://github.com/unit8co/darts/pull/2747) by [Che Hang Ng](https://github.com/CheHangNg).
-- Added new forecasting model: `StatsForecastModel`, that allows to use any forecasting model from StatsForecast (see [here](https://nixtlaverse.nixtla.io/statsforecast/index.html#models)) in Darts with support for future covariates, probabilistic forecasting, and transferable series forecasting. [#2770](https://github.com/unit8co/darts/pull/2770) by [Dennis Bader](https://github.com/dennisbader).
-- 🔴 Simplified all `StatsForecast*` model names by removing the `StatsForecast` part. [#2762](https://github.com/unit8co/darts/pull/2762) by [Dennis Bader](https://github.com/dennisbader).
-  - Renamed `StatsForecastAutoARIMA` to `AutoARIMA`
-  - Renamed `StatsForecastAutoCES` to `AutoCES`
-  - Renamed `StatsForecastAutoETS` to `AutoETS`
-  - Renamed `StatsForecastAutoTBATS` to `AutoTBATS`
-  - Renamed `StatsForecastAutoTheta` to `AutoTheta`
-- 🔴 Changed `AutoARIMA` model backend from `pmdarima` to `statsforecast` to support `numpy>=2.0.0`. The model creation parameters are almost identical. For reference, see the [pmdarima docs](https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html) and [statsforecast docs](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoarima). Additionally, the new implementation is more efficient and has improved probabilistic forecasting support, including conformal prediction. [#2734](https://github.com/unit8co/darts/pull/2734) by [Dennis Bader](https://github.com/dennisbader).
-- 🔴 Changed `TBATS` model backend from `tbats` to `statsforecast` to support `numpy>=2.0.0`. The model creation parameters are almost identical. For reference, see the [tbats docs](https://github.com/intive-DataScience/tbats/blob/master/tbats/tbats/TBATS.py) and [statsforecast docs](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#tbats). Additionally, the new implementation is more efficient, comes with future covariates support and has improved probabilistic forecasting support. [#2781](https://github.com/unit8co/darts/pull/2781) by [Dennis Bader](https://github.com/dennisbader).
-
-**Removed**
-
-- 🔴 Removed `BATS` model to support `numpy>=2.0.0`. Use `TBATS` instead. [#2781](https://github.com/unit8co/darts/pull/2781) by [Dennis Bader](https://github.com/dennisbader).
-- 🔴 Removed deprecated method `TimeSeries.pd_dataframe()`. Use `TimeSeries.to_dataframe()` instead. [#2733](https://github.com/unit8co/darts/pull/2733) by [Dennis Bader](https://github.com/dennisbader).
-- 🔴 Removed deprecated method `TimeSeries.pd_serise()`. Use `TimeSeries.to_series()` instead. [#2733](https://github.com/unit8co/darts/pull/2733) by [Dennis Bader](https://github.com/dennisbader).
+**Fixed**
+
+**Dependencies**
+
+### For developers of the library:
+
+## [0.35.0](https://github.com/unit8co/darts/tree/0.35.0) (2025-04-18)
+
+### For users of the library:
+
+**Improved**
+
+- 🚀🚀 Added **new forecasting models**:
+  - `AutoMFLES` : a simple time series method based on gradient boosting time series decomposition as proposed by Tyler Blume in [this repository](https://github.com/tblume1992/MFLES). Our implementation is based on [AutoMFLES](https://nixtlaverse.nixtla.io/statsforecast/docs/models/mfles.html) from Nixtla's `statsforecast` library. [#2747](https://github.com/unit8co/darts/pull/2747) by [Che Hang Ng](https://github.com/CheHangNg).
+  - `StatsForecastModel` : allows to use **any forecasting model from StatsForecast** (see [here](https://nixtlaverse.nixtla.io/statsforecast/index.html#models)) in Darts with support for future covariates, probabilistic forecasting (sampled, direct quantile, and / or conformal predictions), and transferable series forecasting. [#2770](https://github.com/unit8co/darts/pull/2770) by [Dennis Bader](https://github.com/dennisbader).
+- Improvements to forecasting models:
+  - 🚀 Added **support for categorical covariates** to  `CatBoostModel`. You can now define categorical components at model creation with parameters `categorical_*_covariates: List[str]` for past, future, and static covariates. [#2733](https://github.com/unit8co/darts/pull/2750) by [Jonas Blanc](https://github.com/jonasblanc).
+  - 🚀🔴 Extended the functionalities of and renamed several existing models. All models below now support **future covariates**, **probabilistic forecasting** (sampled, direct quantile, and / or conformal predictions), and **transferable series forecasting**: [#2762](https://github.com/unit8co/darts/pull/2762) and [#2770](https://github.com/unit8co/darts/pull/2770) by [Dennis Bader](https://github.com/dennisbader).
+    - `AutoARIMA` (old name `StatsForecastAutoARIMA`)
+    - `AutoETS` (old name `StatsForecastAutoETS`)
+    - `AutoCES` (old name `StatsForecastAutoCES`)
+    - `AutoTBATS` (old name `StatsForecastAutoTBATS`)
+    - `AutoTheta` (old name `StatsForecastAutoTheta`)
+    - `TBATS`
+    - `Croston`
+- Changes to `TimeSeries` : [#2733](https://github.com/unit8co/darts/pull/2733) by [Dennis Bader](https://github.com/dennisbader).
+  - 🟠 Removed deprecated method `TimeSeries.pd_dataframe()`. Use `TimeSeries.to_dataframe()` instead.
+  - 🟠 Removed deprecated method `TimeSeries.pd_serise()`. Use `TimeSeries.to_series()` instead.
 
 **Fixed**
 
 - Fixed a bug in `CatBoostModel` with `likelihood="gaussian"`, where predicting with `predict_likelihood_parameters=True` resulted in wrong ordering of the predicted parameters. [#2742](https://github.com/unit8co/darts/pull/2742) by [Dennis Bader](https://github.com/dennisbader).
 
 **Dependencies**
 
+- 🚀🚀 Added **support for NumPy 2.0 and higher**. To achieve this, we had to make the following adjustments to our dependencies. [#2771](https://github.com/unit8co/darts/pull/2771) by [Dennis Bader](https://github.com/dennisbader).
+  - 🔴 Changed `AutoARIMA` model backend from `pmdarima` to `statsforecast`. The model creation parameters are almost identical. For reference, see the [pmdarima docs](https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html) and [statsforecast docs](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoarima). Additionally, the new implementation is more efficient and has improved probabilistic forecasting support, including conformal prediction. [#2734](https://github.com/unit8co/darts/pull/2734) by [Dennis Bader](https://github.com/dennisbader).
+  - 🔴 Changed `TBATS` model backend from `tbats` to `statsforecast`. The model creation parameters are almost identical. For reference, see the [tbats docs](https://github.com/intive-DataScience/tbats/blob/master/tbats/tbats/TBATS.py) and [statsforecast docs](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#tbats). Additionally, the new implementation is more efficient, comes with future covariates support and has improved probabilistic forecasting support. [#2781](https://github.com/unit8co/darts/pull/2781) by [Dennis Bader](https://github.com/dennisbader).
+  - 🔴 Removed `BATS` model. Use `TBATS` instead. [#2781](https://github.com/unit8co/darts/pull/2781) by [Dennis Bader](https://github.com/dennisbader).
+
 ### For developers of the library:
 
 - Refactored likelihoods: [#2742](https://github.com/unit8co/darts/pull/2742) by [Dennis Bader](https://github.com/dennisbader).