OpenTSLab/TimeOmni
[ICLR 2026] Official implementation of SciTS: Scientific Time Series Understanding and Generation with LLMs
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.
Stars
10
Forks
—
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 03, 2026
Commits (30d)
0
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