  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.
