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

arXiv: https://arxiv.org/abs/2511.07995 · HTML: https://mlsanity.com/paper/2511.07995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 46 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source](https://mlsanity.com/paper/2605.31259/) (2026)

## Abstract (arXiv)

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.