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LSTD-MTS: Anomaly Detection with Capturing Long-Term Spatio-Temporal Dependence for Multi-dimensional Time Series

Published: 24 July 2024 Publication History

Abstract

In the Industrial Internet of Things (IIoT), accurate time series anomaly detection is key to ensuring operational efficiency and safety. The current challenge is to process and analyze the high-dimensional data collected over the long term, especially the combined temporal and spatial dependence, which are crucial to the accuracy of anomaly detection. The existing methods often overlook the spatio-temporal dual dependence of time series data, resulting in inevitable false positives. This paper proposes an anomaly detection model capturing long-term spatio-temporal dependence for multi-dimensional time series (LSTD-MTS) to solve the above problems. LSTD-MTS utilizes Multi-Scale Temporal Convolutional Network (MSTCN) and Temporal-GRU components to enhance the model's ability to capture temporal dependence of long-term series, combines relational vector graph learning and Graph Attention Network (GAT) to capture multi-dimensional spatial dependence, and maintains the linear relationship of data through autoregressive (AR) component. Compared with seven baseline methods across five datasets, LSTD-MTS demonstrates superior detection performance and greater robustness than those of the baseline methods. Its F1 score is at least 2.76% higher than baseline methods, which supports the robust long-term multi-dimensional analysis ability of LSTD-MTS.

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cover image ACM Conferences
Internetware '24: Proceedings of the 15th Asia-Pacific Symposium on Internetware
July 2024
518 pages
ISBN:9798400707056
DOI:10.1145/3671016
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Published: 24 July 2024

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Author Tags

  1. Anomaly detection
  2. Internet of things
  3. Multi-dimensional time series
  4. Spatio-temporal dependence

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