Anomaly detection in multivariate time series has emerged as a crucial challenge in time series research, with significant research implications in various fields such as fraud detection, fault diagnosis, and system state estimation. Reconstruction-based models have shown promising potential in recent years for detecting anomalies in time series data. However, due to the rapid increase in data scale and dimensionality, the issues of noise and Weak Identity Mapping (WIM) during time series reconstruction have become increasingly pronounced. To address this, we introduce a novel Adaptive Dynamic Neighbor Mask (ADNM) mechanism and integrate it with the Transformer and Denoising Diffusion Model, creating a new framework for multivariate time series anomaly detection, named Denoising Diffusion Mask Transformer (DDMT). The ADNM module is introduced to mitigate information leakage between input and output features during data reconstruction, thereby alleviating the problem of WIM during reconstruction. The Denoising Diffusion Transformer (DDT) employs the Transformer as an internal neural network structure for Denoising Diffusion Model. It learns the stepwise generation process of time series data to model the probability distribution of the data, capturing normal data patterns and progressively restoring time series data by removing noise, resulting in a clear recovery of anomalies. To the best of our knowledge, this is the first model that combines Denoising Diffusion Model and the Transformer for multivariate time series anomaly detection. Experimental evaluations were conducted on five publicly available multivariate time series anomaly detection datasets. The results demonstrate that the model effectively identifies anomalies in time series data, achieving state-of-the-art performance in anomaly detection.

翻译：多元时间序列中的异常检测已成为时间序列研究中的关键挑战，在欺诈检测、故障诊断及系统状态估计等多个领域具有重要研究意义。基于重构的模型近年来在时间序列数据异常检测中展现出巨大潜力。然而，随着数据规模与维度的急剧增长，时间序列重构过程中的噪声与弱恒等映射问题愈发突出。为此，我们提出一种新颖的自适应动态邻域掩码机制，并将其与Transformer及去噪扩散模型相结合，构建了面向多元时间序列异常检测的新框架——去噪扩散掩码Transformer。该自适应动态邻域掩码模块通过抑制数据重构过程中输入与输出特征间的信息泄漏，有效缓解重构时的弱恒等映射问题。去噪扩散Transformer采用Transformer作为去噪扩散模型的内部神经网络结构，通过学习时间序列数据的逐步生成过程来建模数据概率分布，捕捉正常数据模式，并通过消除噪声逐步恢复时间序列数据，从而清晰还原异常特征。据我们所知，这是首个将去噪扩散模型与Transformer相结合用于多元时间序列异常检测的模型。我们在五个公开的多元时间序列异常检测数据集上进行了实验评估，结果表明该模型能够有效识别时间序列数据中的异常，取得了最先进的异常检测性能。