Exploiting negative correlation for unsupervised anomaly detection in contaminated time series

Xiaohui Lin, Zuoyong Li, Haoyi Fan, Yanggeng Fu, Xinwei Chen

Abstract: Highlights•Unsupervised time series anomaly detection under data contamination.•Calibrating the biased anomaly measurement by exploiting the negative correlation.•Normal samples from a learned Gaussian distribution to model negative correlation.•Single forward propagation enables anomaly detection using the trained autoencoder.