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Sparsity-Constrained Vector Autoregressive Moving Average Models for Anomaly Detection of Complex Systems with Multisensory Signals

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Listed:
  • Meng Ma

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Zhongyi Zhang

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Zhi Zhai

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Zhirong Zhong

    (School of Future Technology, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Detecting anomalies in large, complex systems is a critical and challenging task, and this is especially true for high-dimensional anomaly detection due to the underlying dependency structures among sensors. To incorporate the interrelationships among various sensors, a novel sparsity-constrained vector autoregressive moving average (scVARMA) model is proposed for anomaly detection in complex systems with multisensory signals. This model aims to leverage the inherent relationships and dynamics among various sensor readings, providing a more comprehensive and accurate analysis suitable for complex systems’ complex behavior. This research uses convex optimization to search for a parameterization that is sparse based on the principal of parsimony. This sparse model will not only contribute to meeting the real-time requirements of online monitoring strategies but also keeps the correlations among different sensory signals. The performance of the proposed scVARMA model is validated using real-world data from complex systems. The results affirm the superiority of the proposed scheme, demonstrating its enhanced performance and potential in practical applications.

Suggested Citation

  • Meng Ma & Zhongyi Zhang & Zhi Zhai & Zhirong Zhong, 2024. "Sparsity-Constrained Vector Autoregressive Moving Average Models for Anomaly Detection of Complex Systems with Multisensory Signals," Mathematics, MDPI, vol. 12(9), pages 1-14, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1304-:d:1382763
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    References listed on IDEAS

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    1. Runkle, David E, 1987. "Vector Autoregressions and Reality," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 437-442, October.
    2. Oksana Mandrikova & Bogdana Mandrikova & Oleg Esikov, 2023. "Detection of Anomalies in Natural Complicated Data Structures Based on a Hybrid Approach," Mathematics, MDPI, vol. 11(11), pages 1-17, May.
    3. Runkle, David E, 1987. "Vector Autoregressions and Reality: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 454-454, October.
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