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A Hybrid Spiking Model for Anomaly Detection in Multivariate Time Series.

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Title: A Hybrid Spiking Model for Anomaly Detection in Multivariate Time Series.
Authors: Zhang, Wei1 (AUTHOR), He, Ping2 (AUTHOR) heping@scse.hebut.edu.cn, Wang, Shengrui3 (AUTHOR), Yang, Fan1 (AUTHOR), Liu, Ying4 (AUTHOR)
Source: Expert Systems. Aug2025, Vol. 42 Issue 8, p1-13. 13p.
Subject Terms: *ARTIFICIAL neural networks, *TIME series analysis, *ENERGY consumption, ANOMALY detection (Computer security), TIME-varying networks
Abstract: Deep neural networks have exhibited preeminent performance in anomaly detection, but they struggle to effectively capture changes over time in multivariate time‐series data and suffer from resource consumption issues. Spiking neural networks address these limitations by capturing the change in time‐varying signals and decreasing resource consumption, but they sacrifice performance. This paper develops a novel spiking‐based hybrid model incorporated a temporal prediction network and a reconstruction network. It integrates a unique first‐spike frequency encoding scheme and a firing rate based anomaly score method. The encoding scheme enhances the event representation ability, while the anomaly score enables efficient anomaly identification. Our proposed model not only maintains low resource consumption but also improves the ability of anomaly detection. Experiments on publicly real‐world datasets confirmed that the proposed model acquires state‐of‐the‐art performance superior to existing approaches. Remarkably, it costs 5.04× lower energy consumption compared with the artificial neural network version. [ABSTRACT FROM AUTHOR]
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Database: Business Source Index
Description
Abstract:Deep neural networks have exhibited preeminent performance in anomaly detection, but they struggle to effectively capture changes over time in multivariate time‐series data and suffer from resource consumption issues. Spiking neural networks address these limitations by capturing the change in time‐varying signals and decreasing resource consumption, but they sacrifice performance. This paper develops a novel spiking‐based hybrid model incorporated a temporal prediction network and a reconstruction network. It integrates a unique first‐spike frequency encoding scheme and a firing rate based anomaly score method. The encoding scheme enhances the event representation ability, while the anomaly score enables efficient anomaly identification. Our proposed model not only maintains low resource consumption but also improves the ability of anomaly detection. Experiments on publicly real‐world datasets confirmed that the proposed model acquires state‐of‐the‐art performance superior to existing approaches. Remarkably, it costs 5.04× lower energy consumption compared with the artificial neural network version. [ABSTRACT FROM AUTHOR]
ISSN:02664720
DOI:10.1111/exsy.70086