Learn hybrid prototypes for multivariate time series anomaly detection
Keywords: prototypes;time series;anomaly detection
TL;DR: This paper propose a hybrid prototypes learning model for MTSAD based on reconstruction to fight against over-generalization.
Abstract: In multivariate time series anomaly detection (MTSAD), reconstruction-based models reconstruct testing series with learned knowledge of only normal series and identify anomalies with higher reconstruction errors. In practice, over-generalization often occurs with unexpectedly well reconstruction of anomalies. Although memory banks are employed by reconstruction-based models to fight against over-generalization, these models are only efficient to detect point anomalies since they learn normal prototypes from time points, leaving contextual anomalies and periodical anomalies to be discovered. To settle this problem, this paper propose a hybrid prototypes learning model for MTSAD based on reconstruction, named as H-PAD. First, normal prototypes are learned from different sizes of patches for time series to discover short-term anomalies. These prototypes in different sizes are integrated together to reconstruct query series so that any anomalies would be smoothed off and high reconstruction errors are produced. Furthermore, period prototypes are learned to discover periodical anomalies. One period prototype is memorized for one variable of query series. Finally, extensive experiments on five benchmark datasets show the effectiveness of H-PAD with state-of-the-art performance.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 12594
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