{"repo":"Ladbaby/PyOmniTS","free":true,"listed":false,"github":"https://github.com/Ladbaby/PyOmniTS","clone":"git clone https://github.com/Ladbaby/PyOmniTS.git","description":"🔬 A Researcher&Agent-Friendly Framework for Time Series Analysis. Train Any Model on Any Dataset!","language":"Python","stars":93,"topics":["benchmarking","deep-learning","irregular-time-series","missing-values","time-series","time-series-analysis","time-series-classification","time-series-forecasting","claude-code","codex"],"license":"MIT","category":"machine-learning","readme_excerpt":"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 Irregular Multivariate Time Series Forecasting (ICML 2025) [[poster]](https://icml.cc/virtual/2025/poster/43741) [[OpenReview]](https://openreview.net/forum?id=u8wRbX2r2V) [[arXiv]](https://arxiv.org/abs/2505.17431) 1. ✨ Hightlighted Features - Extensibility : Adapt your model/dataset once , train almost any combination of \"model\" $\\times$ \"dataset\" $\\times$ \"loss function\". - Compatibility : Accept models with any number/type of arguments in forward ; Accept datasets with any number/type of return values in getitem ; Accept tailored loss calculation for specific models. - Maintainability : No need to worry about breaking the training codes of existing models/datasets/loss functions when adding new ones. - Reproducibility : Minimal library dependencies for core components. Try the best to get rid of fancy third-party libraries (e.g., PyTorch Lightning, EasyTorch). - Efficiency : Mu","default_branch":null,"files":null,"tree":[],"storefront":"/r/Ladbaby","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Ladbaby/PyOmniTS/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}