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<p>Unsupervised anomaly detection in multivariate time series is important in many applications including cyber intrusion detection and medical diagnostics. Both traditional and supervised techniques had limitations due to data scale, labeling complexity, and cluster imbalance. Also, deep learning methods have drawbacks such as sensitivity to noise and difficulty in capturing spatial-temporal correlations. To address these challenges, we propose MTSAD, a new AE-based anomaly detection model for multivariate time series data that uses ConvLSTM and transposed convolution to effectively learn spatio-temporal features. Furthermore, in this paper, we explore the effect of noise injection and data amount utilization that improves the model performance and prevents overfitting. It increases the robustness to real sensor noise and improves the robustness of anomaly detection in industrial environments. On SWaT and WADI datasets, MTSAD achieves higher F1 scores than the competing models. The results of the study also show that data amount and noise injection are very important factors that can be used to improve the performance of AE-based anomaly detection. This work offers new understandings of the optimization of reconstruction-based architectures for unsupervised multivariate time series anomaly detection.</p>

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Enhancing autoencoder models for multivariate time series anomaly detection: the role of noise and data amount

  • 期刊论文

  • 发表日期：

    2025年2月28日

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  • Seyedeh Tina Sefati,
  • Seyed Naser Razavi,
  • Pedram Salehpour

摘要

Unsupervised anomaly detection in multivariate time series is important in many applications including cyber intrusion detection and medical diagnostics. Both traditional and supervised techniques had limitations due to data scale, labeling complexity, and cluster imbalance. Also, deep learning methods have drawbacks such as sensitivity to noise and difficulty in capturing spatial-temporal correlations. To address these challenges, we propose MTSAD, a new AE-based anomaly detection model for multivariate time series data that uses ConvLSTM and transposed convolution to effectively learn spatio-temporal features. Furthermore, in this paper, we explore the effect of noise injection and data amount utilization that improves the model performance and prevents overfitting. It increases the robustness to real sensor noise and improves the robustness of anomaly detection in industrial environments. On SWaT and WADI datasets, MTSAD achieves higher F1 scores than the competing models. The results of the study also show that data amount and noise injection are very important factors that can be used to improve the performance of AE-based anomaly detection. This work offers new understandings of the optimization of reconstruction-based architectures for unsupervised multivariate time series anomaly detection.