tsfast 0.1.2

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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:

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.

from tsfast.basics import *
dls = create_dls_test()
dls.show_batch(max_n=1)

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 { background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px); } .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar { background: #F44336; } </style>

0.00% [0/1 00:00<?]

epoch	train_loss	valid_loss	fun_rmse	time

0.00% [0/12 00:00<?]

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; } progress:not([value]), progress:not([value])::-webkit-progress-bar { background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px); } .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar { background: #F44336; } </style>

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
• identibench
• 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}
}