Multivariate Time series Anomaly Detection:A Framework of Hidden Markov Models

Jinbo Li, Witold Pedrycz, Iqbal Jamal

Abstract: In this study, we develop an approach to multivariate time series anomaly detection focused on the transformation of multivariate time series to univariate time series. Several transformation techniques involving Fuzzy C-Means (FCM) clustering and fuzzy integral are studied. In the sequel, a Hidden Markov Model (HMM), one of the commonly encountered statistical methods, is engaged here to detect anomalies in multivariate time series. We construct HMM-based anomaly detectors and in this context compare several transformation methods. A suite of experimental studies along with some comparative analysis is reported.

External IDs:dblp:journals/corr/abs-2511-07995