from tsfast.basics import *
= create_dls_test()
dls =1) dls.show_batch(max_n
tsfast
tsfast
Description
A deep learning library for time series analysis and system identification built on top of PyTorch & fastai.
tsfast
is an open-source deep learning package that focuses on system identification and time series analysis tasks. Built on the foundations of PyTorch and fastai, it provides efficient implementations of various deep learning models and utilities.
Installation
You can install the latest stable version from pip using:
pip install tsfast
For development installation:
//github.com/daniel-om-weber/tsfast
git clone https:
cd tsfast-e '.[dev]' pip install
Quick Start
Here is a quick example using a test dataloader. It demonstrates loading and visualizing data, training a RNN, and visualizing the results.
= RNNLearner(dls)
lrn 1) lrn.fit_flat_cos(
epoch | train_loss | valid_loss | fun_rmse | time |
---|
=1) lrn.show_results(max_n
Documentation
For detailed documentation, visit our documentation site.
Key documentation sections: - Core Functions - Data Processing - Models - Learner API - Hyperparameter Optimization
Requirements
- Python ≥ 3.9
- fastai
- PyTorch
- sysbench_loader
- matplotlib
- ray[tune] (for hyperparameter optimization)
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.
License
This project is licensed under the Apache 2.0 License.
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}
}