Changelog#

Changelog#

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Changes are managed using towncrier and stored in the changelog.d/ directory. See changelog.d/README.md for instructions on adding changelog fragments.

v0.1.0a5 (2026-06-23)#

Changed#

  • Standardize batch extraction across all models. (#39)

  • Strip supervision leakage from TSTCC core model. (#40)

v0.1.0a4 (2026-06-22)#

Added#

  • Add RecurrentAutoEncoder. (#20)

Changed#

  • Remove BC code, standardize *Producer naming, add parameter docs. (#34)

v0.1.0a3 (2026-06-18)#

Changed#

  • Standardize hyperparameter naming and defaults across all models. (#31)

v0.1.0a2 (2026-06-17)#

Added#

  • Standardize encoder/decoder access with HasEncoder/HasDecoder. (#25)

Changed#

  • Phase 06: prepare chronocratic-models for PyPI publication. (#21)

Fixed#

  • Correct Quick Start example and pin build badge to main. (#26)

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.