Update darts requirement from >=0.44.0 to >=0.44.1

[dependabot]

Updates the requirements on darts to permit the latest version.

Release notes

Sourced from darts's releases.

Darts patch 0.44.1

We are pleased to announce the release of a new Darts version.

You can find a list with all changes in the release notes.

Changelog

Sourced from darts's changelog.

0.44.1 (2026-05-05)

For users of the library:

Improved

Fixed

• Fixed a ValueError in backtest() when using overlap_end=True with predict_likelihood_parameters=True and a quantile metric. The final forecast window could extend beyond the series end, producing an empty intersection that caused a reshape failure in the metric computation. #3111 by Dennis Bader
• Fixed rendering issues of CustomBlockRNNModule and CustomRNNModule in the documentation. #3094 by Zhihao Dai
• Fixed rendering issues of 20-SKLearnModel-examples notebook in the documentation. #3094 by Zhihao Dai

Dependencies

• Re-added support for numpy < 2 (numpy>=1.26.0) to allow compatibility with cloud platforms that require older numpy versions (e.g., Foundry Transforms due to Spark 3). #3110 by Dennis Bader

For developers of the library:

• PyPI package release is now part of the release workflow using secure Trusted Publishing via OpenID Connect (OIDC). #3100 by Dennis Bader
• Sped up the documentation build by utilizing multiple CPU cores. #3094 by Zhihao Dai.
• Added a weekly GitHub Actions workflow to run uv audit and fail CI when vulnerabilities are found in the dependencies. #3102 by Zhihao Dai.
• Updated GitHub Actions to use Node.js 24 runtime as Node.js 20 has reached end-of-life. #3105 by Zhihao Dai.

Dependencies

• Added a uv 7-day cooldown period to reduce supply-chain risk from newly published packages during dependency resolution. #3096 by Zhihao Dai
• Configured uv to install only binary wheels for all dependencies to reduce supply-chain risk from source-distribution build execution. #3099 by Zhihao Dai
• Replaced m2r2 and recommonmark with myst-parser for Sphinx Markdown support, fixing 3 nbconvert vulnerabilities caused by m2r2 pinning mistune==0.8.4. #3104 by Dennis Bader

0.44.0 (2026-04-30)

For users of the library:

Improved

• 🚀🚀 Dramatically reduced import times by deferring heavy third-party dependencies (torch, sklearn, scipy, ...) until they are actually needed. This benefits cold-start scenarios (serverless functions, CLI tools, short-lived scripts), CI pipelines, and interactive development workflows where fast feedback loops matter. Here are some import speed-up examples: #3066 by Dennis Bader
  • TimeSeries, metrics, datasets, data transformers: 8x faster (2.4 → 0.3 seconds)
  • Baseline models: 19x faster (5.7 → 0.3 seconds)
  • SKLearn models: 4.4x faster (5.7 → 1.3 seconds)
  • Torch models: 1.9x faster (5.7 → 3.0 seconds)
• 🚀🚀 Added new forecasting model TiRexModel : NX-AI's pre-trained 35M-parameter foundational model for zero-shot forecasting. It supports univariate, multivariate, and multiple time series forecasting without training and can output deterministic or probabilistic forecasts. #3038 by Lukas Fischer, Martin Loretz, and Zhihao Dai.
• Improvements to InvertibleDataTransformer and data Pipeline : #3085 by Jakub Chłapek
  • Added optional insample parameter to inverse_transform() to supply the transformed history needed by transformers like Diff, removing the need to manually concatenate the in-sample series before inverse-transforming a forecast.
  • Diff can now be properly used inside a target series Pipeline with historical_forecasts() via the data_transformers parameter. The backtested forecasts are correctly inverse-transformed back to the original scale.
• Improvements to RegressionEnsembleModel : #3041 by Gabriel Margaria and #3074 by Junghwan Na.
  • Base forecasting models using output_chunk_shift>0 are now fully supported. If you're using a custom regression_model, simply set its output shift to be the same as that of the base models.
  • Added support for output_chunk_length>1 for the ensemble (regression) model. This means that the ensemble model can now consume information from base model forecasts over the entire horizon.
• Improvements to Gradient-Boosted Models:
  • Added native multi-quantile support for CatBoostModel by using CatBoost’s MultiQuantile loss for faster training and inference. Set likelihood="multiquantile" to enable this feature. #3032 by Zhihao Dai
  • Added native multi-quantile support for XGBModel. Similar to the regular quantile support, it still fits dedicated models per quantile, but it is more efficient due to fewer tabularization operations. Set likelihood="multiquantile" to enable this feature. #3056 by Oswald Zink
• Improvements to metrics:

... (truncated)

Commits

• c33b5e4 Bump version: 0.44.0 → 0.44.1
• 690606f Release 0.44.1 (#3113)
• ec0b7fa Revert "rename shap-related explainability to sklearn (#3103)" (#3112)
• 36ea035 fix bug with backtest overlap end and metrics using quantiles (#3111)
• 27a65a8 add back numpy<2.0.0 for foundry (#3110)
• c7205a5 Devops: Update GitHub Actions to use Node.js 24 (#3105)
• 3095a02 Security: uv audit workflow (#3102)
• 21924b1 Devops/drop m2r2 with myst parser (#3104)
• 2b1b8a2 rename shap-related explainability to sklearn (#3103)
• 8a834c8 Expose tirex to darts.models (#3101)
• Additional commits viewable in compare view

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