We present a novel time series anomaly detection method that achieves excellent detection accuracy while offering a superior level of explainability. Our proposed method, TimeVQVAE-AD, leverages masked generative modeling adapted from the cutting-edge time series generation method known as TimeVQVAE. The prior model is trained on the discrete latent space of a time-frequency domain. Notably, the dimensional semantics of the time-frequency domain are preserved in the latent space, enabling us to compute anomaly scores across different frequency bands, which provides a better insight into the detected anomalies. Additionally, the generative nature of the prior model allows for sampling likely normal states for detected anomalies, enhancing the explainability of the detected anomalies through counterfactuals. Our experimental evaluation on the UCR Time Series Anomaly archive demonstrates that TimeVQVAE-AD significantly surpasses the existing methods in terms of detection accuracy and explainability. We provide our implementation on GitHub: \url{https://github.com/ML4ITS/TimeVQVAE-AnomalyDetection}.

翻译：我们提出了一种新颖的时间序列异常检测方法，该方法在实现卓越检测精度的同时，提供了高水平的可解释性。所提出的方法TimeVQVAE-AD，利用了源自前沿时间序列生成方法TimeVQVAE的掩码生成建模技术。先验模型在时频域的离散潜空间上进行训练。值得注意的是，时频域的维度语义在潜空间中得以保留，使我们能够计算不同频带上的异常分数，从而为检测到的异常提供更深入的洞察。此外，先验模型的生成特性允许对检测到的异常进行可能的正常状态采样，通过反事实分析增强了检测异常的可解释性。我们在UCR时间序列异常档案上的实验评估表明，TimeVQVAE-AD在检测精度和可解释性方面显著超越了现有方法。代码实现已发布于GitHub：\url{https://github.com/ML4ITS/TimeVQVAE-AnomalyDetection}。