# tsfast
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
# 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:
``` python
pip install tsfast
```
For development installation:
``` python
git clone https://github.com/daniel-om-weber/tsfast
cd tsfast
pip install -e '.[dev]'
```
## Quick Start
Here is a quick example using a test dataloader. It demonstrates loading
and visualizing data, training a RNN, and visualizing the results.
``` python
from tsfast.basics import *
dls = create_dls_test()
dls.show_batch(max_n=1)
```
``` python
lrn = RNNLearner(dls)
lrn.fit_flat_cos(1)
```
<style>
/* Turns off some styling */
progress {
/* gets rid of default border in Firefox and Opera. */
border: none;
/* Needs to be in here for Safari polyfill so background images work as expected. */
background-size: auto;
}
progress:not([value]), progress:not([value])::-webkit-progress-bar {
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| epoch | train_loss | valid_loss | fun_rmse | time |
|-------|------------|------------|----------|------|
<p>
<div>
<progress value='0' class='' max='12' style='width:300px; height:20px; vertical-align: middle;'></progress>
0.00% [0/12 00:00<?]
</div>
``` python
lrn.show_results(max_n=1)
```
<style>
/* Turns off some styling */
progress {
/* gets rid of default border in Firefox and Opera. */
border: none;
/* Needs to be in here for Safari polyfill so background images work as expected. */
background-size: auto;
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progress:not([value]), progress:not([value])::-webkit-progress-bar {
background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);
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.progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {
background: #F44336;
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## Documentation
For detailed documentation, visit our [documentation
site](https://daniel-om-weber.github.io/tsfast/).
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:
``` text
@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}
}
```
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