Anomaly detection stands as a crucial aspect of time series analysis, aiming to identify abnormal events in time series samples. The central challenge of this task lies in effectively learning the representations of normal and abnormal patterns in a label-lacking scenario. Previous research mostly relied on reconstruction-based approaches, restricting the representational abilities of the models. In addition, most of the current deep learning-based methods are not lightweight enough, which prompts us to design a more efficient framework for anomaly detection. In this study, we introduce PatchAD, a novel multi-scale patch-based MLP-Mixer architecture that leverages contrastive learning for representational extraction and anomaly detection. Specifically, PatchAD is composed of four distinct MLP Mixers, exclusively utilizing the MLP architecture for high efficiency and lightweight architecture. Additionally, we also innovatively crafted a dual project constraint module to mitigate potential model degradation. Comprehensive experiments demonstrate that PatchAD achieves state-of-the-art results across multiple real-world multivariate time series datasets. Our code is publicly available https://github.com/EmorZz1G/PatchAD

翻译：摘要：异常检测作为时间序列分析的关键环节，旨在识别时间序列样本中的异常事件。该任务的核心挑战在于无标签场景下有效学习正常与异常模式的表征。以往研究多依赖重建方法，限制了模型的表征能力。此外，当前多数基于深度学习的方案缺乏轻量化设计，这促使我们设计更高效的异常检测框架。本研究提出PatchAD——一种新颖的多尺度分块MLP-Mixer架构，通过对比学习实现表征提取与异常检测。具体而言，PatchAD由四个独立的MLP混合器组成，完全采用MLP架构实现高效性与轻量化设计。同时，我们创新性地构建了双投影约束模块以缓解潜在模型退化问题。全面实验表明，PatchAD在多个真实世界多变量时间序列数据集上达到了最先进水平。我们的代码开源在https://github.com/EmorZz1G/PatchAD