Ready-to-use time series models implemented in PyTorch and Lightning.
Note: The PyPI package name uses a hyphen (
chronocratic-models), but the import uses thechronocratic.modelsnamespace.
pip install chronocratic-modelsimport torch
from lightning.pytorch import Trainer
from chronocratic.models import TS2Vec, TS2VecModelParameters
# Create model using parameters dataclass
params = TS2VecModelParameters(input_dims=1)
model = TS2Vec(**vars(params))
# Prepare synthetic time series (n_instance, n_timestamps, n_features)
synthetic_data = torch.randn(2, 100, 1)
# Train the model first (models do not ship with pre-trained weights)
trainer = Trainer(max_epochs=1, accelerator="cpu", enable_checkpointing=False)
trainer.fit(model, train_dataloaders=synthetic_data)
# Get multi-scale representations
representations = model.encode(
synthetic_data,
batch_size=2,
num_workers=0,
encoding_window="multiscale",
)
print(representations.shape)The package ships with self-supervised time-series models across these architectures:
| Category | Import |
|---|---|
| Convolutional (Dilated) | TS2Vec, CoST, AutoTCL |
| Convolutional (Standard) | Series2Vec, TSTCC, SimCLR, MCL |
| Transformer | TST |
| Recurrent | TimeNet, RecurrentAutoEncoder |
| Generative | TimeVAE |
For details (original papers, encoder architecture, default hyperparameters), see the API reference and the ModelParameters dataclass for each model. The list above is maintained by the exports in chronocratic.models; adding a model is just extending __init__.py.
Important: No pre-trained weights are included — train on your own data before inference.
- Polymorphic augmentation producer contract — models accept any augmentation through a unified interface, eliminating enum-based branching.
- Lightning integration — all models are built on PyTorch Lightning for clean training loops and extensibility.
- Self-supervised representation learning — train encoders for downstream tasks without labeled data.
- Pre-configured model parameters — each model ships with tested default configuration dataclasses.
- NumPy and PyTorch tensor support — flexible input handling for both frameworks.
For full API reference, guides, and examples, visit chronocratic-models.readthedocs.io.
For development setup, linting, testing, and coding standards, see docs/contributing.md.
This project is licensed under the BSD 3-Clause License — see the LICENSE file for details.