Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series

• URL: http://arxiv.org/abs/2403.01895v1
• Date: Mon, 4 Mar 2024 09:55:16 GMT
• Title: Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series
• Authors: Hanyang Yuan, Qinglin Cai, Keting Yin
• Abstract summary: We propose FCM-wDTW, an unsupervised distance metric learning method for anomaly detection over time series. FCM-wDTW encodes raw data into latent space and reveals normal dimension relationships through cluster centers. Experiments with 11 different types of benchmarks demonstrate our method's competitive accuracy and efficiency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distance-based time series anomaly detection methods are prevalent due to their relative non-parametric nature and interpretability. However, the commonly used Euclidean distance is sensitive to noise. While existing works have explored dynamic time warping (DTW) for its robustness, they only support supervised tasks over multivariate time series (MTS), leaving a scarcity of unsupervised methods. In this work, we propose FCM-wDTW, an unsupervised distance metric learning method for anomaly detection over MTS, which encodes raw data into latent space and reveals normal dimension relationships through cluster centers. FCM-wDTW introduces locally weighted DTW into fuzzy C-means clustering and learns the optimal latent space efficiently, enabling anomaly identification via data reconstruction. Experiments with 11 different types of benchmarks demonstrate our method's competitive accuracy and efficiency.

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