Detecting anomalies in real-world multivariate time series data is challenging due to complex temporal dependencies and inter-variable correlations. Recently, reconstruction-based deep models have been widely used to solve the problem. However, these methods still suffer from an over-generalization issue and fail to deliver consistently high performance. To address this issue, we propose the MEMTO, a memory-guided Transformer using a reconstruction-based approach. It is designed to incorporate a novel memory module that can learn the degree to which each memory item should be updated in response to the input data. To stabilize the training procedure, we use a two-phase training paradigm which involves using K-means clustering for initializing memory items. Additionally, we introduce a bi-dimensional deviation-based detection criterion that calculates anomaly scores considering both input space and latent space. We evaluate our proposed method on five real-world datasets from diverse domains, and it achieves an average anomaly detection F1-score of 95.74%, significantly outperforming the previous state-of-the-art methods. We also conduct extensive experiments to empirically validate the effectiveness of our proposed model's key components.

翻译：现实世界中的多变量时间序列数据因存在复杂的时序依赖关系和变量间相关性，使得异常检测极具挑战性。近期，基于重构的深度模型被广泛用于解决该问题。然而，这些方法仍存在泛化过度问题，难以持续保持高性能。为此，我们提出MEMTO——一种采用重构策略的记忆引导Transformer模型。该模型设计了一种新颖的记忆模块，能根据输入数据学习每个记忆项应被更新的程度。为稳定训练流程，我们采用两阶段训练范式，利用K-means聚类初始化记忆项。此外，我们引入基于双维度偏差的检测准则，结合输入空间与隐空间特征计算异常分数。在五个不同领域的真实世界数据集上，所提方法取得平均异常检测F1值95.74%，显著超越现有最优方法。我们还通过大量实验验证了模型关键组件的有效性。