sintel-dev/Orion
Unsupervised time series anomaly detection library
This tool helps operations engineers, data scientists, or anyone monitoring systems or processes to automatically find unusual patterns in their time-based data. You feed it a stream of observations, like server metrics or sensor readings, and it tells you when and where abnormal behavior occurred, helping you quickly spot issues. It's designed for someone who needs to identify unexpected events without manually sifting through mountains of data.
1,343 stars.
Use this if you have continuous streams of data over time and need to automatically identify unusual spikes, drops, or changes that could indicate problems or interesting events.
Not ideal if your data isn't time-series based, or if you need to detect anomalies in categorical data or static datasets without a temporal component.
Stars
1,343
Forks
195
Language
Python
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/sintel-dev/Orion"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
elki-project/elki
ELKI Data Mining Toolkit
raphaelvallat/antropy
AntroPy: entropy and complexity of (EEG) time-series in Python
Minqi824/ADBench
Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.