ruiking04/COCA
Deep Contrastive One-Class Time Series Anomaly Detection
This project helps identify unusual patterns or abnormal behaviors within long streams of data collected over time. You input raw time-series data, and it outputs indicators of anomalies, highlighting points or periods that deviate significantly from the norm. This is designed for data analysts, operations engineers, or researchers who need to monitor systems and detect unexpected events in data like sensor readings or system logs.
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Use this if you need to automatically detect anomalies in large volumes of unlabelled time-series data where normal behavior is complex or varies.
Not ideal if your data is not time-series based, you have clear labels for both normal and anomalous data, or you only have a small amount of data.
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Language
Python
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Last pushed
Feb 20, 2025
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