4.7 Article

Robust Group Anomaly Detection for Quasi-Periodic Network Time Series

期刊

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2022.3170364

关键词

Time series analysis; Timing; Anomaly detection; Shape; Electrocardiography; Training; Databases; Group anomaly detection; gaussian mixture model; timing errors

资金

  1. Fundamental Research Funds for the Central Universities of China
  2. National Natural Science Foundation of China [61771013]
  3. Fundamental Research Funds of Shanghai Jiading District

向作者/读者索取更多资源

This paper introduces a sequence to Gaussian Mixture Model (seq2GMM) framework, aiming to identify anomalous and interesting time series within a network time series database. By developing a surrogate-based optimization algorithm, the model exhibits strong performance on multiple public benchmark datasets, outperforming state-of-the-art anomaly detection techniques.
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.

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