Robust Group Anomaly Detection for Quasi-Periodic Network Time Series

Kai Yang, Shaoyu Dou, Pan Luo, Xin Wang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

Many real-world multivariate time series are collected from a network of physical objects embedded with software, electronics, and sensors. The quasi-periodic signals generated by these objects often follow a similar repetitive and periodic pattern, but have variations in the period, and come in different lengths caused by timing (synchronization) errors. Given a multitude of such quasi-periodic time series, can we build machine learning models to identify those time series that behave differently from the majority of the observations? In addition, can the models help human experts to understand how the decision was made? We propose a sequence to Gaussian Mixture Model (seq2GMM) framework. The overarching goal of this framework is to identify unusual and interesting time series within a network time series database. We further develop a surrogate-based optimization algorithm that can efficiently train the seq2GMM model. Seq2GMM exhibits strong empirical performance on a plurality of public benchmark datasets, outperforming state-of-the-art anomaly detection techniques by a significant margin. We also theoretically analyze the convergence property of the proposed training algorithm and provide numerical results to substantiate our theoretical claims.

Original languageEnglish (US)
Pages (from-to)2833-2845
Number of pages13
JournalIEEE Transactions on Network Science and Engineering
Volume9
Issue number4
DOIs
StatePublished - 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Group anomaly detection
  • gaussian mixture model
  • timing errors

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