ML4ITS/mtad-gat-pytorch
PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).
This tool helps engineers and operators identify unusual behavior in complex systems by analyzing multiple sensor readings or operational metrics over time. You input raw, multivariate time-series data from machinery, spacecraft, or IT infrastructure, and it outputs visual charts highlighting detected anomalies. It's designed for professionals managing the health and performance of critical systems.
389 stars. No commits in the last 6 months.
Use this if you need to automatically detect unexpected patterns or malfunctions in streams of interconnected operational data to prevent failures or understand system incidents.
Not ideal if you are working with simple, single-stream data or if you require real-time, ultra-low-latency anomaly detection in a production environment.
Stars
389
Forks
88
Language
Python
License
MIT
Category
Last pushed
Jan 16, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ML4ITS/mtad-gat-pytorch"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
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.