Open Issues Need Help
View All on GitHubAI Summary: The `TimeSeries.add_datetime_attribute` method in the `darts` library has a `tz` parameter with a restrictive type hint (`str | None`). This parameter is ultimately passed to Pandas' `DatetimeIndex.tz_convert`, which supports a broader range of types, including `tzinfo` objects and integers. The bug is that `darts`'s type hints prevent users from utilizing the full capabilities of the underlying Pandas method, and the proposed fix is to broaden the type hints in the `darts` call chain to match Pandas' flexibility.
A python library for user-friendly forecasting and anomaly detection on time series.
A python library for user-friendly forecasting and anomaly detection on time series.
A python library for user-friendly forecasting and anomaly detection on time series.
A python library for user-friendly forecasting and anomaly detection on time series.
A python library for user-friendly forecasting and anomaly detection on time series.
A python library for user-friendly forecasting and anomaly detection on time series.
A python library for user-friendly forecasting and anomaly detection on time series.
A python library for user-friendly forecasting and anomaly detection on time series.
A python library for user-friendly forecasting and anomaly detection on time series.
AI Summary: The user is encountering an issue where `darts.models.XGBModel`, particularly with its `historical_forecasts` method, fails to utilize GPU acceleration for XGBoost, despite having a CUDA-enabled XGBoost installation that works when called directly. The error messages indicate that no GPU is found and XGBoost is not compiled with CUDA support, suggesting a problem with how `darts` initializes or passes GPU-related parameters to the underlying XGBoost library.
A python library for user-friendly forecasting and anomaly detection on time series.
AI Summary: The task is to investigate why the `darts` Python library's wheel file includes an `examples` package, which is considered a potential source of naming conflicts with other packages. The solution likely involves removing the `examples` directory from the wheel's contents during the build process, ensuring that example code is distributed separately (e.g., via a repository or documentation site) and not packaged within the main library.
A python library for user-friendly forecasting and anomaly detection on time series.
AI Summary: The task is to improve the parameter validation in the Darts Python library's `torch_forecasting_model.py` file. The current validation breaks subclassing due to hardcoded class checks. The solution involves modifying the validation to dynamically inspect the inheritance chain of a model class to determine valid parameters, ensuring compatibility with subclasses.
A python library for user-friendly forecasting and anomaly detection on time series.
AI Summary: The task is to debug and fix a bug in the Darts Python library where saving global baseline models (GlobalNaiveSeasonal, GlobalNaiveAggregate, GlobalNaiveDrift) fails due to an AttributeError related to PyTorch Lightning's Trainer. The solution involves investigating why the Trainer isn't properly attached to these models during the saving process and modifying the `save()` method to ensure compatibility.
A python library for user-friendly forecasting and anomaly detection on time series.