OpenTSLab/TimeOmni

[ICLR 2026] Official implementation of SciTS: Scientific Time Series Understanding and Generation with LLMs

26
/ 100
Experimental

This project helps researchers and scientists analyze and generate scientific time series data using advanced AI models. You provide a dataset of scientific time series, which can be in various formats like audio, CSV, or medical signals (EEG/MEG), and the system can perform tasks such as forecasting, classification, or anomaly detection. It's designed for data scientists and AI/ML researchers working with complex temporal data in scientific fields.

Use this if you need a unified framework to apply large language models for understanding and generating diverse scientific time series data across multiple domains and tasks.

Not ideal if you are looking for a simple, out-of-the-box solution without custom model training or if your data is not scientific time series.

scientific-data-analysis time-series-forecasting signal-processing anomaly-detection machine-learning-research
No Package No Dependents
Maintenance 10 / 25
Adoption 5 / 25
Maturity 11 / 25
Community 0 / 25

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Stars

10

Forks

Language

Python

License

Apache-2.0

Last pushed

Mar 03, 2026

Commits (30d)

0

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