Unsupervised Distance Metric Learning for Anomaly Detection in Multivariate Time Series

The author proposes FCM-wDTW, an unsupervised distance metric learning method for anomaly detection over multivariate time series. By encoding raw data into a latent space and utilizing fuzzy C-means clustering with locally weighted DTW, anomalies can be efficiently identified through data reconstruction.