aleflabo/HypAD
The official PyTorch implementation of the IEEE/CVF CVPR Visual Anomaly and Novelty Detection (VAND) Workshop paper Are we certain it's anomalous?.
This project helps identify unusual patterns in time-series data, such as sensor readings or financial metrics. It takes your historical time-series data and pinpoints moments or segments that don't fit the established normal behavior. This is ideal for operations engineers monitoring equipment, data analysts tracking system health, or anyone needing to spot critical deviations in sequential data.
No commits in the last 6 months.
Use this if you need to automatically detect anomalies in univariate or multivariate time-series datasets to identify unexpected events or system failures.
Not ideal if you're working with static image data or tabular datasets where the temporal order of observations is not important.
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26
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3
Language
Python
License
MIT
Category
Last pushed
Dec 06, 2023
Commits (30d)
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