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:-e tsfast/. pip install
Key Features
Deep Learning Models: Implementation of various architectures including:
- RNN-based models (RNN, LSTM, GRU)
- CNN-based models
- Residual Networks
- Separate RNN implementations
System Identification: Specialized tools and models for system identification tasks
Data Processing:
- Efficient data loading and preprocessing
- Time series specific transforms
- Scalar normalization utilities
Training Utilities:
- Custom learners for different model types
- Specialized callbacks
- Weight clipping functionality
- Gradient flow visualization
Quick Start
To use tsfast in your notebooks, import the package:
from tsfast.basics import *
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}
}