AMAD：用于无监督多元时间序列异常检测的自动掩码注意力
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection
AI总结：
针对无监督多元时间序列异常检测中关联假设受限的问题，提出AMAD模型，结合自动掩码注意力与注意力混合模块，辅以最大-最小训练和局部-全局对比学习，实现稳健且通用的异常检测，性能媲美最先进基准。
摘要：
无监督多元时间序列异常检测（UMTSAD）在金融、网络和传感器系统等多个领域发挥着关键作用。近年来，由于深度学习在一般序列任务中的出色表现，许多模型被专门用于深度UMTSAD任务并取得了令人瞩目的成果，尤其是那些基于Transformer和自注意力机制的模型。然而，这些模型所基于的序列异常关联假设往往局限于特定的预定义模式和场景，例如集中式或峰值异常模式。这些局限性阻碍了它们推广到多样化的异常情况，尤其是在缺乏标签带来重大挑战的情况下。为了解决这些问题，我们提出了AMAD，它将自动掩码注意力（AutoMasked Attention）集成到UMTSAD场景中。AMAD引入了一种基于自动掩码机制（AutoMask机制）和注意力混合（attention mixup）模块的新颖结构，形成了一个简单而通用的异常关联表示框架。该框架通过最大-最小训练策略和局部-全局对比学习方法进一步增强。通过结合多尺度特征提取与自动相对关联建模，AMAD为UMTSAD挑战提供了稳健且适应性强的解决方案。大量实验结果表明，所提出的模型在各种数据集上与最先进的基准相比取得了具有竞争力的性能。
英文摘要：
Unsupervised multivariate time series anomaly detection (UMTSAD) plays a critical role in various domains, including finance, networks, and sensor systems. In recent years, due to the outstanding performance of deep learning in general sequential tasks, many models have been specialized for deep UMTSAD tasks and have achieved impressive results, particularly those based on the Transformer and self-attention mechanisms. However, the sequence anomaly association assumptions underlying these models are often limited to specific predefined patterns and scenarios, such as concentrated or peak anomaly patterns. These limitations hinder their ability to generalize to diverse anomaly situations, especially where the lack of labels poses significant challenges. To address these issues, we propose AMAD, which integrates \textbf{A}uto\textbf{M}asked Attention for UMTS\textbf{AD} scenarios. AMAD introduces a novel structure based on the AutoMask mechanism and an attention mixup module, forming a simple yet generalized anomaly association representation framework. This framework is further enhanced by a Max-Min training strategy and a Local-Global contrastive learning approach. By combining multi-scale feature extraction with automatic relative association modeling, AMAD provides a robust and adaptable solution to UMTSAD challenges. Extensive experimental results demonstrate that the proposed model achieving competitive performance results compared to SOTA benchmarks across a variety of datasets.
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