MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2312.02530v1
• Date: Tue, 5 Dec 2023 06:28:19 GMT
• Title: MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
• Authors: Junho Song, Keonwoo Kim, Jeonglyul Oh, Sungzoon Cho
• Abstract summary: MEMTO is a memory-guided Transformer that learns the degree to which each memory item should be updated in response to the input data. We evaluate our proposed method on five real-world datasets from diverse domains.
• Score: 6.16984478518058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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