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

• URL: http://arxiv.org/abs/2511.07995v1
• Date: Wed, 12 Nov 2025 01:33:05 GMT
• Title: Multivariate Time series Anomaly Detection:A Framework of Hidden Markov Models
• Authors: Jinbo Li, Witold Pedrycz, Iqbal Jamal,
• Abstract summary: Fuzzy C-Means (FCM) clustering and fuzzy integral are studied.<n>In the sequel, a Hidden Markov Model (HMM) is engaged here to detect anomalies in multivariate time series.
• Score: 47.05277984960841
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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