by Ladbaby · Codex Skill · ★ 84
A Researcher&Agent-Friendly Framework for Time Series Analysis. Train Any Model on Any Dataset. 📊 Time series analysis leaderboard is now available on our 🤗 Hugging Face space. Discover the performance of different models! This is also the official repository for the following paper: Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting (ICLR 2026) [[poster]](https://iclr.cc/virtual/2026/poster/10010222) [[OpenReview]](https://openreview.net/forum?id=JEIDxiTWzB) [[arXiv]](https://arxiv.org/abs/2602.21498) HyperIMTS: Hypergraph Neural Network for...
| Stars | 84 |
| Forks | 11 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 56.2728840568281/100 |
| Last Updated | 2026-06-17 |
| Created | 2025-05-20 |
| Platforms | claude-code, cli, codex, gemini, python |
| Est. Tokens | ~16k |
These tools work well together with PyOmniTS for enhanced workflows:
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PyOmniTS is 🔬 A Researcher&Agent-Friendly Framework for Time Series Analysis. Train Any Model on Any Dataset!. It is categorized as a Codex Skill with 84 GitHub stars.
PyOmniTS is primarily written in Python. It covers topics such as benchmarking, claude-code, codex.
You can find installation instructions and usage details in the PyOmniTS GitHub repository at github.com/Ladbaby/PyOmniTS. The project has 84 stars and 11 forks, indicating an active community.
PyOmniTS is released under the MIT license, making it free to use and modify according to the license terms.