Low Rank Transformer for Multivariate Time Series Anomaly Detection and Localization

• URL: http://arxiv.org/abs/2602.08467v1
• Date: Mon, 09 Feb 2026 10:15:25 GMT
• Title: Low Rank Transformer for Multivariate Time Series Anomaly Detection and Localization
• Authors: Charalampos Shimillas, Kleanthis Malialis, Konstantinos Fokianos, Marios M. Polycarpou,
• Abstract summary: We study the learning process of a Transformer when applied to time series methods.<n>We propose the Attention Low-Rank Transformer (ALoRa-T) model, which applies low-rank regularization to self-attention.<n>We also introduce the Attention Low-Rank score, effectively capturing the temporal characteristics of anomalies.
• Score: 3.649198196896847
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multivariate time series (MTS) anomaly diagnosis, which encompasses both anomaly detection and localization, is critical for the safety and reliability of complex, large-scale real-world systems. The vast majority of existing anomaly diagnosis methods offer limited theoretical insights, especially for anomaly localization, which is a vital but largely unexplored area. The aim of this contribution is to study the learning process of a Transformer when applied to MTS by revealing connections to statistical time series methods. Based on these theoretical insights, we propose the Attention Low-Rank Transformer (ALoRa-T) model, which applies low-rank regularization to self-attention, and we introduce the Attention Low-Rank score, effectively capturing the temporal characteristics of anomalies. Finally, to enable anomaly localization, we propose the ALoRa-Loc method, a novel approach that associates anomalies to specific variables by quantifying interrelationships among time series. Extensive experiments and real data analysis, show that the proposed methodology significantly outperforms state-of-the-art methods in both detection and localization tasks.

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