sintel-dev/sigllm
Using Large Language Models for Time Series Anomaly Detection
This project helps operations engineers, data analysts, or researchers quickly identify unusual patterns in their time-series data. You feed it a timestamped sequence of measurements, and it tells you where anomalies occur. This tool is designed for anyone needing to spot unexpected shifts or events in streams of data, like system metrics, sensor readings, or financial data.
Used by 1 other package. Available on PyPI.
Use this if you need to automatically find unusual segments or spikes in your time-series data, leveraging the latest AI models without deep expertise in machine learning.
Not ideal if you need a simple, explainable anomaly detection method or if your time-series data is not clearly timestamped with corresponding values.
Stars
85
Forks
27
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
Dependencies
11
Reverse dependents
1
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