# 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.