https://github.com/rose-stl-lab/torchts
Time series forecasting with PyTorch
Science Score: 36.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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✓Committers with academic emails
2 of 14 committers (14.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (18.1%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Time series forecasting with PyTorch
Basic Info
- Host: GitHub
- Owner: Rose-STL-Lab
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://rose-stl-lab.github.io/torchTS/
- Size: 7.08 MB
Statistics
- Stars: 84
- Watchers: 7
- Forks: 22
- Open Issues: 39
- Releases: 2
Topics
Metadata Files
README.md
TorchTS is a PyTorch-based library for time series data.
Currently under active development!
Why Time Series?
Time series data modeling has broad significance in public health, finance and engineering. Traditional time series methods from statistics often rely on strong modeling assumptions, or are computationally expensive. Given the rise of large-scale sensing data and significant advances in deep learning, the goal of the project is to develop an efficient and user-friendly deep learning library that would benefit the entire research community and beyond.
Why TorchTS?
Existing time series analysis libraries include statsmodels and sktime. However, these libraries only include traditional statistics tools such as ARMA or ARIMA, which do not have the state-of-the-art forecasting tools based on deep learning. GluonTS is an open-source time series library developed by Amazon AWS, but is based on MXNet. Pyro is a probabilistic programming framework based on PyTorch, but is not focused on time series forecasting.
Installation
Installation Requirements
TorchTS supports Python 3.8+ and has the following dependencies:
Installing the latest release
The latest release of TorchTS is easily installed either via pip:
bash
pip install torchts
or via conda from the conda-forge channel:
bash
conda install -c conda-forge torchts
You can customize your PyTorch installation (i.e. CUDA version, CPU only option) by following the PyTorch installation instructions.
Important note for MacOS users:
- Make sure your PyTorch build is linked against MKL (the non-optimized version of TorchTS can be up to an order of magnitude slower in some settings). Setting this up manually on MacOS can be tricky - to ensure this works properly, please follow the PyTorch installation instructions.
- If you need CUDA on MacOS, you will need to build PyTorch from source. Please consult the PyTorch installation instructions above.
Getting Started
Check out our documentation and tutorials (coming soon).
Citing TorchTS
If you use TorchTS, please cite the following paper (coming soon):
bibtex
@inproceedings{TBD,
title={{TorchTS: A Framework for Efficient Time Series Modeling}},
author={TBD},
booktitle = {TBD},
year={TBD},
url = {TBD}
}
See here (coming soon) for a selection of peer-reviewed papers that either build off of TorchTS or were integrated into TorchTS.
Contributing
Interested in contributing to TorchTS? Please see the contributing guide to learn how to help out.
License
TorchTS is MIT licensed.
Owner
- Name: Spatiotemporal Machine Learning
- Login: Rose-STL-Lab
- Kind: organization
- Location: United States of America
- Website: roseyu.com
- Repositories: 15
- Profile: https://github.com/Rose-STL-Lab
Machine Learning Research Group at University of California, San Diego
GitHub Events
Total
- Watch event: 3
- Push event: 5
Last Year
- Watch event: 3
- Push event: 5
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Kevin Lane | l****a@g****m | 165 |
| dependabot[bot] | 4****] | 60 |
| Akash | a****9@g****m | 17 |
| pre-commit-ci[bot] | 6****] | 7 |
| Aadit Gupta | 5****1 | 6 |
| yuqirose | y****e@g****m | 6 |
| torchts-bot[bot] | 8****] | 2 |
| Kailing Ding | 4****g | 2 |
| Amartya (Marty) Mukherjee | 3****e | 2 |
| Giovanny Martinez | g****z@G****l | 2 |
| yuqirose | r****u@n****u | 1 |
| djleung | 5****g | 1 |
| Martinez | g****3@g****m | 1 |
| Rose Yu | q****u@u****u | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 2
- Total pull requests: 132
- Average time to close issues: 8 months
- Average time to close pull requests: 6 months
- Total issue authors: 1
- Total pull request authors: 7
- Average comments per issue: 0.0
- Average comments per pull request: 1.43
- Merged pull requests: 19
- Bot issues: 0
- Bot pull requests: 118
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- klane (2)
Pull Request Authors
- dependabot[bot] (164)
- klane (13)
- pre-commit-ci[bot] (4)
- amartyamukherjee (2)
- JudyJin (2)
- bhfxwangshida (2)
- kiddycharles (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
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Total downloads:
- pypi 6 last-month
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Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 1
(may contain duplicates) - Total versions: 3
- Total maintainers: 2
pypi.org: torchts
Time series forecasting with PyTorch
- Homepage: https://rose-stl-lab.github.io/torchTS
- Documentation: https://rose-stl-lab.github.io/torchTS/docs
- License: MIT
-
Latest release: 0.1.1
published almost 5 years ago
Rankings
conda-forge.org: torchts
Time series data modeling has broad significance in public health, finance, and engineering. Traditional time series methods from statistics often rely on strong modeling assumptions or are computationally expensive. Given the rise of large-scale sensing data and significant advances in deep learning, the goal of TorchTS is to provide an efficient and user-friendly deep learning library for time series that benefits the entire research community and beyond.
- Homepage: https://rose-stl-lab.github.io/torchTS
- License: MIT
-
Latest release: 0.1.1
published almost 5 years ago