tsfast

TSFast

Description

tsfast is an open-source deep learning library for time series analysis and system identification tasks. Built on PyTorch, it offers efficient deep learning models and utilities.

tsfast is an open-source deep learning package that focuses on system identification and time series analysis tasks. Built on PyTorch, it provides efficient implementations of various deep learning models and utilities.

Key Features

Specialized Data Handling — HDF5-backed data pipeline with sequence transforms, noise injection, normalization, and TBPTT (Truncated Backpropagation Through Time) support.
Benchmark Datasets — One-line access to Silverbox, Wiener-Hammerstein, Cascaded Tanks, and more via identibench.
Time Series Models — RNNs (DenseNet, Residual), TCNs, CRNNs with layer normalization, and combined architectures.
Integrated Training — RNNLearner, TCNLearner, CRNNLearner with custom losses (nrmse, cut_loss) and composable transforms.
System Identification — Simulation, N-step prediction, FranSys, and autoregressive models with InferenceWrapper for deployment.
Physics-Informed NNs — Embed governing equations into training via CollocationLoss, PhysicsLoss, and PIRNN.
Hyperparameter Optimization — Ray Tune integration via HPOptimizer.
Deployment — Model save/load, InferenceWrapper for NumPy-in/NumPy-out inference, and ONNX export.
Performance — CUDA graph acceleration via GraphedStatefulModel for low-overhead GPU training.

Installation

You can install the latest stable version using:

pip install tsfast

For development installation:

git clone https://github.com/daniel-om-weber/tsfast
cd tsfast
pip install -e '.[dev]'
# or using uv:
uv sync --extra dev

Quick Start

Here is a quick example using a benchmark dataloader. It demonstrates loading and visualizing data, training a RNN, and visualizing the results.

from tsfast.basics import *

# Load benchmark dataset
dls = create_dls_silverbox()

# Train an RNN and visualize results
lrn = RNNLearner(dls)
lrn.fit_flat_cos(1)
lrn.show_results(max_n=1)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Citation

If you use tsfast in your research, please cite:

@Misc{tsfast,
author = {Daniel O.M. Weber},
title = {tsfast - A deep learning library for time series analysis and system identification},
howpublished = {Github},
year = {2024},
url = {https://github.com/daniel-om-weber/tsfast}
}

Development Status
- 3 - Alpha
Intended Audience
- Developers
Natural Language
- English
Programming Language

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