Unsupervised Distance Metric Learning for Anomaly Detection Over
  Multivariate Time Series
Hanyang Yuan Qinglin Cai Keting Yin
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.