{"ID":2843254,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07995","arxiv_id":"2511.07995","title":"Multivariate Time series Anomaly Detection:A Framework of Hidden Markov Models","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.","short_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),...","url_abs":"https://arxiv.org/abs/2511.07995","url_pdf":"https://arxiv.org/pdf/2511.07995v1","authors":"[\"Jinbo Li\",\"Witold Pedrycz\",\"Iqbal Jamal\"]","published":"2025-11-11T08:59:45Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}