MEMTO：用于多元时间序列异常检测的记忆引导 Transformer
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
AI总结：
针对多元时间序列异常检测中重构模型过度泛化的问题，提出记忆引导 Transformer MEMTO，通过可学习更新记忆模块、两阶段训练和双空间偏差检测，在五个数据集上取得 95.74% 的平均 F1 分数。
摘要：
由于复杂的时间依赖关系和变量间相关性，检测现实世界多元时间序列数据中的异常颇具挑战性。近年来，基于重构的深度模型被广泛用于解决该问题。然而，这些方法仍存在过度泛化问题，无法持续提供高性能。为解决这一问题，我们提出 MEMTO，这是一种采用基于重构方法的记忆引导 Transformer。它被设计为纳入一个新颖的记忆模块，该模块能够学习每个记忆项响应输入数据时应被更新的程度。为稳定训练过程，我们采用两阶段训练范式，其中包括使用 K-means 聚类初始化记忆项。此外，我们引入一种基于双维度偏差的检测准则，该准则同时考虑输入空间和潜在空间来计算异常分数。我们在来自不同领域的五个现实世界数据集上评估所提出的方法，该方法取得 95.74% 的平均异常检测 F1 分数，显著优于以往的最先进方法。我们还进行了大量实验，从经验上验证所提出模型关键组件的有效性。
英文摘要：
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.
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。
