DDMT：用于多元时间序列异常检测的去噪扩散掩码Transformer模型
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection
AI总结：
本文提出DDMT框架，通过自适应动态邻居掩码缓解弱身份映射问题，并首次结合去噪扩散模型与Transformer建模正常数据分布，在五个公开数据集上取得最先进的多元时间序列异常检测性能。
摘要：
多元时间序列中的异常检测已成为时间序列研究中的一个关键挑战，在欺诈检测、故障诊断和系统状态估计等多个领域具有重要的研究意义。近年来，基于重建的模型在检测时间序列数据异常方面展现出良好的潜力。然而，由于数据规模和维度的快速增长，时间序列重建过程中的噪声和弱身份映射（WIM）问题日益突出。为解决这一问题，我们提出了一种新颖的自适应动态邻居掩码（ADNM）机制，并将其与Transformer和去噪扩散模型相结合，构建了一个新的多元时间序列异常检测框架，命名为去噪扩散掩码Transformer（DDMT）。引入ADNM模块是为了在数据重建过程中缓解输入与输出特征之间的信息泄漏，从而减轻重建过程中的WIM问题。去噪扩散Transformer（DDT）采用Transformer作为去噪扩散模型的内部神经网络结构。它学习时间序列数据的逐步生成过程，以建模数据的概率分布，捕获正常数据模式，并通过去除噪声逐步恢复时间序列数据，从而清晰地恢复异常。据我们所知，这是第一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。在五个公开可用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，在异常检测方面达到了最先进的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
