Multivariate Time series Anomaly Detection:A Framework of Hidden Markov Models
This work addresses anomaly detection in multivariate time series for applications like monitoring systems, but it is incremental as it combines existing methods without major breakthroughs.
The paper tackled multivariate time series anomaly detection by transforming multivariate data into univariate series using techniques like Fuzzy C-Means clustering and fuzzy integrals, then applied Hidden Markov Models for detection, with experimental comparisons reported.
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.