EmorZz1G/PatchAD
PatchAD, TBD 2025, deep learning, anomaly detection, outlier detection, time series, PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection
This project helps operations engineers and data analysts identify unusual patterns or outliers in streaming data, even when they don't have historical examples of what's 'bad'. It takes in continuous streams of operational data, like sensor readings or system metrics, and flags points in time when something deviates from normal behavior, helping you proactively address issues. The primary users are those who need to monitor systems and detect anomalies without extensive manual labeling.
No commits in the last 6 months.
Use this if you need a highly efficient and accurate way to automatically detect anomalies in various types of time-series data without needing pre-labeled examples of anomalies.
Not ideal if your data is not time-series based, or if you require an anomaly detection solution that has been rigorously tested for installation stability.
Stars
44
Forks
5
Language
Python
License
Apache-2.0
Category
Last pushed
Aug 18, 2025
Commits (30d)
0
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