DAMO-DI-ML/CIKM22-TFAD
Source code of CIKM'22 paper: TFAD: A Decomposition Time Series Anomaly Detection Architecture with Frequency Analysis
This project helps operations engineers and data analysts identify unusual patterns or anomalies in time-series data from systems, sensors, or networks. It takes in streams of time-stamped measurements and pinpoints unexpected events or deviations that could indicate a problem. This is useful for monitoring system health, detecting fraud, or anticipating equipment failures.
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Use this if you need to detect anomalies in multivariate time series data without relying on extensive historical anomaly examples or highly complex deep learning models.
Not ideal if your primary goal is to predict future values or classify time series into predefined categories rather than simply spotting unusual behavior.
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Python
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Last pushed
Nov 30, 2022
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