Release 0.34.0 (#2726)

* bump u8darts 0.33.0 to 0.34.0

* update changelog

Author: dennisbader

CHANGELOG.md

@@ -1,38 +1,50 @@
 # Changelog
 
 We do our best to avoid the introduction of breaking changes,
-but cannot always guarantee backwards compatibility. Changes that may **break code which uses a previous release of Darts** are marked with a "🔴".
+but cannot always guarantee backwards compatibility. Changes that may **break code which uses a previous release of Darts** are marked with a "🔴". Changes that deprecate functionalities are marked with a "🟠".
 
 ## [Unreleased](https://github.com/unit8co/darts/tree/master)
 
-[Full Changelog](https://github.com/unit8co/darts/compare/0.33.0...master)
+[Full Changelog](https://github.com/unit8co/darts/compare/0.34.0...master)
 
 ### For users of the library:
 
 **Improved**
 
-- Added support for embedding metadata in `TimeSeries`. You can now attach any type of metadata as a dictionary to your time series, either at series creation using parameter `metadata` (or `metadata_cols` with `from_group_dataframe()`), or add it to an existing series via the `TimeSeries.with_metadata()` method. Unlike static covariates, metadata is not utilized by Darts' models during the forecasting process. However, the metadata is propagated through all downstream tasks, preserving the integrity and continuity of the information throughout the data pipeline. [#2656](https://github.com/unit8co/darts/pull/2656) by [Yoav Matzkevich](https://github.com/ymatzkevich), [Jules Authier](https://github.com/authierj) and [Dennis Bader](https://github.com/dennisbader).
-- Improved `CatBoostModel` documentation by describing how to use native multi-output regression. [#2659](https://github.com/unit8co/darts/pull/2659) by [Jonas Blanc](https://github.com/jonasblanc)
-- `TimeSeries.from_dataframe()` and `from_series()` now support creating `TimeSeries` from additional backends (Polars, PyArrow, ...). We leverage `narwhals` as the compatibility layer between dataframe libraries. See the `narwhals` [documentation](https://narwhals-dev.github.io/narwhals/) for all supported backends. [#2661](https://github.com/unit8co/darts/pull/2661) by [Jules Authier](https://github.com/authierj)
-- Added new export methods: `TimeSeries.to_dataframe()` and `TimeSeries.to_series()`. These methods support exporting `TimeSeries` to DataFrames and Series for all backends supported by `narwhals`. See the `narwhals` [documentation](https://narwhals-dev.github.io/narwhals/extending/) for all supported backends. [#2701](https://github.com/unit8co/darts/pull/2701) by [Jules Authier](https://github.com/authierj)
-- 🟠 `TimeSeries.pd_dataframe()` and `TimeSeries.pd_series()` are deprecated and will be removed in Darts version 0.35.0. Use `to_dataframe()` and `to_series()` instead. [#2701](https://github.com/unit8co/darts/pull/2701) by [Jules Authier](https://github.com/authierj)
-- Added ONNX support for torch-based models with method `TorchForecastingModel.to_onnx()`. Check out [this example](https://unit8co.github.io/darts/userguide/gpu_and_tpu_usage.html#exporting-model-to-onnx-format-for-inference) from the user guide on how to export and load a model for inference. [#2620](https://github.com/unit8co/darts/pull/2620) by [Antoine Madrona](https://github.com/madtoinou)
-- Made method `ForecastingModel.untrained_model()` public. Use this method to get a new (untrained) model instance created with the same parameters. [#2684](https://github.com/unit8co/darts/pull/2684) by [Timon Erhart](https://github.com/turbotimon)
-- `TimeSeries.plot()` now supports setting the color for each component in the series. Simply pass a list / sequence of colors with length matching the number of components as parameters "c" or "colors". [#2680](https://github.com/unit8co/darts/pull/2680) by [Jules Authier](https://github.com/authierj)
-- Made it possible to run the quickstart notebook `00-quickstart.ipynb` locally. [#2691](https://github.com/unit8co/darts/pull/2691) by [Jules Authier](https://github.com/authierj)
-- Added `quantile` parameter to `RegressionModel.get_estimator()` to get the specific quantile estimator for probabilistic regression models using the `quantile` likelihood. [#2716](https://github.com/unit8co/darts/pull/2716) by [Antoine Madrona](https://github.com/madtoinou)
-- Improvements to documentation: Added examples to `TimeSeries` factory methods and updated quickstart notebook with the extended backend support for creating and exporting `TimeSeries`. [#2725](https://github.com/unit8co/darts/pull/2725) by [Dennis Bader](https://github.com/dennisbader).
+**Fixed**
+
+**Dependencies**
 
-**Removed**
+### For developers of the library:
+
+## [0.34.0](https://github.com/unit8co/darts/tree/0.34.0) (2025-03-09)
 
-- 🔴 Removed method `RegressionModel.get_multioutput_estimator()`. Use `RegressionModel.get_estimator()` instead. [#2716](https://github.com/unit8co/darts/pull/2716) by [Antoine Madrona](https://github.com/madtoinou)
+### For users of the library:
+
+**Improved**
+
+- Improvements to `TimeSeries`:
+  - `from_dataframe()` and `from_series()` now support creating `TimeSeries` from **additional DataFrame backends** (Polars, PyArrow, ...). We leverage `narwhals` as the compatibility layer between DataFrame libraries. See their [documentation](https://narwhals-dev.github.io/narwhals/) for all supported backends. [#2661](https://github.com/unit8co/darts/pull/2661) by [Jules Authier](https://github.com/authierj)
+  - Added **new export methods**: `to_dataframe()` and `to_series()`. These methods support exporting `TimeSeries` to DataFrames and Series for all backends supported by `narwhals`. See their [documentation](https://narwhals-dev.github.io/narwhals/extending/) for all supported backends. [#2701](https://github.com/unit8co/darts/pull/2701) by [Jules Authier](https://github.com/authierj)
+  - Added support for **embedding metadata** in `TimeSeries`. You can now attach any type of metadata as a dictionary to your time series, either at series creation using parameter `metadata` (or `metadata_cols` with `from_group_dataframe()`), or add it to an existing series via the `with_metadata()` method. Unlike static covariates, metadata is not utilized by Darts' models during the forecasting process. However, the metadata is propagated through all downstream tasks, preserving the integrity and continuity of the information throughout the data pipeline. [#2656](https://github.com/unit8co/darts/pull/2656) by [Yoav Matzkevich](https://github.com/ymatzkevich), [Jules Authier](https://github.com/authierj) and [Dennis Bader](https://github.com/dennisbader).
+  - Method `plot()` now supports **setting the color for each component** in the series. Simply pass a list / sequence of colors with length matching the number of components as parameters "c" or "colors". [#2680](https://github.com/unit8co/darts/pull/2680) by [Jules Authier](https://github.com/authierj)
+  - Improved documentation: Added examples to `TimeSeries` factory methods and updated quickstart notebook with the extended backend support for creating and exporting `TimeSeries`. [#2725](https://github.com/unit8co/darts/pull/2725) by [Dennis Bader](https://github.com/dennisbader).
+  - 🟠 Methods `pd_dataframe()` and `pd_series()` are deprecated and will be removed in Darts version 0.35.0. Use `to_dataframe()` and `to_series()` instead. [#2701](https://github.com/unit8co/darts/pull/2701) by [Jules Authier](https://github.com/authierj)
+- Improvements to `TorchForecastingModel`:
+  - Added ONNX support for torch-based models with method `to_onnx()`. Check out [this example](https://unit8co.github.io/darts/userguide/gpu_and_tpu_usage.html#exporting-model-to-onnx-format-for-inference) from the user guide on how to export and load a model for inference. [#2620](https://github.com/unit8co/darts/pull/2620) by [Antoine Madrona](https://github.com/madtoinou)
+- Improvements to `RegressionModel`:
+  - Added `quantile` parameter to method `get_estimator()` to get the specific quantile estimator for probabilistic regression models using the `quantile` likelihood. [#2716](https://github.com/unit8co/darts/pull/2716) by [Antoine Madrona](https://github.com/madtoinou)
+  - Improved `CatBoostModel` documentation by describing how to use native multi-output regression. [#2659](https://github.com/unit8co/darts/pull/2659) by [Jonas Blanc](https://github.com/jonasblanc)
+  - 🔴 Removed method `get_multioutput_estimator()`. Use `get_estimator()` instead. [#2716](https://github.com/unit8co/darts/pull/2716) by [Antoine Madrona](https://github.com/madtoinou)
+- Other improvements:
+  - Made method `ForecastingModel.untrained_model()` public. Use this method to get a new (untrained) model instance created with the same parameters. [#2684](https://github.com/unit8co/darts/pull/2684) by [Timon Erhart](https://github.com/turbotimon)
+  - Made it possible to run the quickstart notebook `00-quickstart.ipynb` in local dev mode without installation. [#2691](https://github.com/unit8co/darts/pull/2691) by [Jules Authier](https://github.com/authierj)
 
 **Fixed**
 
-- 🔴 / 🟢 Fixed a bug which raised an error when loading torch models that were saved with Darts versions < 0.33.0. This is a breaking change and models saved with version 0.33.0 will not be loadable anymore. [#2692](https://github.com/unit8co/darts/pull/2692) by [Dennis Bader](https://github.com/dennisbader).
-- Fixed a bug in `NLinearModel` with `normalize=True` where the normalization was not applied properly. The predictive power is now greatly improved. [#2699](https://github.com/unit8co/darts/pull/2699) by [Dennis Bader](https://github.com/dennisbader).
+- 🔴 / 🟢 Fixed a bug which raised an error when loading a `TorchForecastingModel` that was saved with Darts versions < 0.33.0. This is a breaking change and models saved with version 0.33.0 will not be loadable anymore. [#2692](https://github.com/unit8co/darts/pull/2692) by [Dennis Bader](https://github.com/dennisbader).
+- Fixed a bug in `NLinearModel` with `normalize=True` where the normalization was not applied properly. The predictive power is now greatly improved. [#2724](https://github.com/unit8co/darts/pull/2724) by [Dennis Bader](https://github.com/dennisbader).
 - Fixed a bug in `StaticCovariatesTransformer` which raised an error when trying to inverse transform one-hot encoded categorical static covariates with identical values across time-series. Each categorical static covariates is now referred to by `{covariate_name}_{category_name}`, regardless of the number of categories. [#2710](https://github.com/unit8co/darts/pull/2710) by [Antoine Madrona](https://github.com/madtoinou)
-- Fixed a bug in `13-TFT-examples.ipynb` where two calls to `TimeSeries.from_series()` were not providing `series` but `pd.Index`. The method calls were changed to `TimeSeries.from_values()`. [#2719](https://github.com/unit8co/darts/pull/2719) by [Jules Authier](https://github.com/authierj)
 - Fixed a bug in `RegressionModel` where performing optimized historical forecasts with `max(lags) < -1` resulted in forecasts that extended too far into the future. [#2715](https://github.com/unit8co/darts/pull/2715) by [Jules Authier](https://github.com/authierj)
 
 **Dependencies**

README.md

@@ -216,7 +216,7 @@ series.plot()
 * **Datasets** The `darts.datasets` submodule contains some popular time series datasets for rapid
   and reproducible experimentation.
 
-* **Compatibility with Multiple Backends:** `TimeSeries` objects can be created from and exported to various backends such as pandas, polars, numpy, pyarrow, datarray, and more, facilitating seamless integration with different data processing libraries.
+* **Compatibility with Multiple Backends:** `TimeSeries` objects can be created from and exported to various backends such as pandas, polars, numpy, pyarrow, xarray, and more, facilitating seamless integration with different data processing libraries.
 
 ## Forecasting Models
 Here's a breakdown of the forecasting models currently implemented in Darts. We are constantly working

setup_u8darts.py

@@ -29,7 +29,7 @@ def read_requirements(path):
 
 setup(
     name="u8darts",
-    version="0.33.0",
+    version="0.34.0",
     description="A python library for easy manipulation and forecasting of time series.",
     long_description=LONG_DESCRIPTION,
     long_description_content_type="text/markdown",