简单(哲学)
系列(地层学)
异常检测
异常(物理)
模式识别(心理学)
计算机科学
人工智能
数据挖掘
地质学
物理
哲学
古生物学
认识论
凝聚态物理
作者
Zhijie Zhong,Zhiwen Yu,Xing Xi,Yue Xu,Jiahui Chen,Kaixiang Yang
出处
期刊:Cornell University - arXiv
日期:2024-05-18
标识
DOI:10.48550/arxiv.2405.11238
摘要
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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