v0.1.0a1 (2026-06-15) — First Alpha Release

v0.1.0a1 (2026-06-15) — First Alpha Release

The first pre-release of chronocratic-models.

This alpha introduces the complete set of self-supervised time series models, the polymorphic augmentation framework, supervised fine-tuning infrastructure, and the Sphinx-based documentation.

Expect breaking changes before the 1.0 release.

Added

• Convolutional (Dilated) models: TS2Vec, CoST, AutoTCL — multi-scale representation learning via dilated convolutions, with PoolingEncodingMixin for sliding-window encoding.
• Convolutional (Standard) models: Series2Vec, TSTCC, FCN — contrastive and clustering-based pretraining with BasicEncodingMixin.
• Transformer model: TST — masked-reconstruction pretraining with configurable encoder depth and positional encoding.
• Recurrent model: TimeNet — GRU-based encoder-decoder with autoencoder pretraining.
• Generative model: TimeVAE — variational autoencoder with KL divergence + reconstruction loss.
• Polymorphic augmentation producer contract: Models accept any augmentation through {AugmentationProducer} protocols, eliminating enum-based branching.
• Augmentation primitives: Jitter, Scaling, Permutation, ComposeAugmentation with configurable *Parameters dataclasses.
• Augmentation producers: SingleViewProducer, IndependentPair, RolePair, FullOverlapPair.
• Trainable augmentation support: TrainableAugmentationProducer ABC and maybe_train_augmentation / maybe_configure_augmentation_optimizer utilities.
• Supervised fine-tuning: SupervisedModule wrapper with four modes — linear probe, full fine-tune, gradual unfreeze, supervised-from-scratch.
• Factory functions: make_tst_supervised, make_series2vec_supervised, make_tstcc_supervised for quick backbone + head setup.
• BackboneUnfreeze callback: Lightning callback for gradual unfreezing of pretrained encoders.
• ModelParameters dataclasses: One per model, with kw_only=True and Google-style Args: docstrings.
• Shared layers: BandedFourierLayer, LevelModel, ResidualConnection, SeasonalLayer, TrendLayer.
• Distance metrics: SoftDTW (differentiable dynamic time warping).
• Encoding mixins: BasicEncodingMixin and PoolingEncodingMixin.
• Sphinx documentation with autodoc-generated API reference per model family.
• BSD 3-Clause license.

Notes

• Namespace is chronocratic.models (PyPI name is chronocratic-models).
• Requires Python 3.12+.
• Uses PyTorch and PyTorch Lightning as the primary framework.