SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2405.11238v1
• Date: Sat, 18 May 2024 09:37:04 GMT
• Title: SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection
• Authors: Zhijie Zhong, Zhiwen Yu, Xing Xi, Yue Xu, Jiahui Chen, Kaixiang Yang,
• Abstract summary: We introduce SimAD, a $textbfSim$ple dissimilarity-based approach for time series anomaly detection. SimAD incorporates an advanced feature extractor adept at processing extended temporal windows, and a ContrastFusion module designed to accentuate distributional divergences between normal and abnormal data. Experiments across $textbfseven$ diverse time series datasets demonstrate SimAD's superior performance compared to state-of-the-art methods.
• Score: 11.846850082915084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the prevalence of reconstruction-based deep learning methods, time series anomaly detection remains challenging. Existing approaches often struggle with limited temporal contexts, inadequate representation of normal patterns, and flawed evaluation metrics, hindering their effectiveness in identifying aberrant behavior. To address these issues, we introduce $\textbf{{SimAD}}$, a $\textbf{{Sim}}$ple dissimilarity-based approach for time series $\textbf{{A}}$nomaly $\textbf{{D}}$etection. SimAD incorporates an advanced feature extractor adept at processing extended temporal windows, utilizes the EmbedPatch encoder to integrate normal behavioral patterns comprehensively, and introduces an innovative ContrastFusion module designed to accentuate distributional divergences between normal and abnormal data, thereby enhancing the robustness of anomaly discrimination. Additionally, we propose two robust evaluation metrics, UAff and NAff, addressing the limitations of existing metrics and demonstrating their reliability through theoretical and experimental analyses. Experiments across $\textbf{seven}$ diverse time series datasets demonstrate SimAD's superior performance compared to state-of-the-art methods, achieving relative improvements of $\textbf{19.85%}$ on F1, $\textbf{4.44%}$ on Aff-F1, $\textbf{77.79%}$ on NAff-F1, and $\textbf{9.69%}$ on AUC on six multivariate datasets. Code and pre-trained models are available at https://github.com/EmorZz1G/SimAD.

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