This release is a pre-release and may not be stable for production use.
chronocratic-models
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.
Installation
pip install chronocratic-models
Quick Start
import 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)
Models
Convolutional (Dilated)
| Model | Description |
|---|---|
| TS2Vec | Multi-scale hierarchical representation learning via dilated convolutions with hierarchical clustering. Code source: zhihanyue/ts2vec |
| CoST | Decomposition-based contrastive self-supervised learning with trend-seasonal decomposition and contrastive objectives. Code source: salesforce/CoST |
| AutoTCL | Automatic temporal contrastive learning with a trainable augmentation module for self-supervised time-series encoding. Code source: AslanDing/AutoTCL |
Convolutional (Standard)
| Model | Description |
|---|---|
| Series2Vec | Self-supervised pretraining via contrastive learning on augmented time-series segments. |
| TSTCC | Temporal and contextual contrastive pretraining for time-series representation learning. |
| MCL | Fully convolutional encoder designed for Mixup Contrastive Learning objectives. |
Transformer
| Model | Description |
|---|---|
| TST | Time Series Transformer with masked-reconstruction-based self-supervised pretraining. |
Recurrent
| Model | Description |
|---|---|
| TimeNet | Recurrent encoder-decoder architecture for time-series representation learning. |
| RecurrentAutoEncoder | Recurrent autoencoder for time-series representation learning. Code source: time-series-foundation-models/time-series-autoencoder |
Generative
| Model | Description |
|---|---|
| TimeVAE | Variational autoencoder for time-series data with latent representation encoding and generation. |
Important: These models are provided as training-ready implementations. No pre-trained weights are included — you must train them on your own data before using them for inference.
Features
- 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.
Documentation
For full API reference, guides, and examples, visit chronocratic-models.readthedocs.io.
License
This project is licensed under the BSD 3-Clause License — see the LICENSE file for details.
1 maintainer
Metadata
1 maintainer
Release files for chronocratic-models 0.1.0a9
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