Changelog#

All notable changes to chronocratic-datasets will be documented in this file.

v0.1.0a1 (2026-06-10) — First Alpha Release#

The first pre-release of chronocratic-datasets. This alpha introduces the complete set of time series datasets, a clean and type-safe API, and full PyTorch Lightning integration.

Expect breaking changes before the 1.0 release. Feedback is welcome.

Added#

  • Forecasting datasets: ETT, Weather, Electricity with LightningDataModule integration

  • Classification datasets: UCR (univariate) and UEA (multivariate) benchmarks

  • ForecastingLoaderMode enum: RAW_SERIES, INPUT_TARGET, INPUT_ONLY

  • ClassificationLoaderMode enum: SAMPLE_ONLY, SAMPLE_LABEL

  • ForecastingMode enum: UNIVARIATE, MULTIVARIATE

  • Data caching: Automatic NPZ caching for downloaded and preprocessed data

  • Data scaling: Configurable normalization via scikit-learn scalers

  • DDP compliance: All data modules work with distributed training strategies

  • Utility functions: Cache management, feature extraction, ARFF parsing, collation

  • Package structure: Full __init__.py with 49 re-exported public symbols

  • BSD 3-Clause license

  • Sphinx documentation with autodoc-generated API reference

Notes#

  • Namespace is chronocratic.datasets (installed via chronocratic-datasets on PyPI).

  • Requires Python 3.12+.

  • Uses PyTorch Lightning as the primary training framework integration.