Contextual and Seasonal LSTMs for Time Series Anomaly Detection

Jiahao Chen, Yong Yang, Shouling Ji, Chenyang Lyu, Rui Zeng, Qingming Li, Lingpei Zhang

arXiv · PDF · Open in the Atlas

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• NESA-Lab/Contextual-and-Seasonal-LSTMs-for-TSAD

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Abstract

Univariate time series (UTS), where each timestamp records a single variable, serve as crucial indicators in web systems and cloud servers. Anomaly detection in UTS plays an essential role in both data mining and system reliability management. However, existing reconstruction-based and prediction-based methods struggle to capture certain subtle anomalies, particularly small point anomalies and slowly rising anomalies. To address these challenges, we propose a novel prediction-based framework named Contextual and Seasonal LSTMs (CS-LSTMs). CS-LSTMs are built upon a noise decomposition strategy and jointly leverage contextual dependencies and seasonal patterns, thereby strengthening the detection of subtle anomalies. By integrating both time-domain and frequencydomain representations, CS-LSTMs achieve more accurate modeling of periodic trends and anomaly localization. Extensive evaluations on public benchmark datasets demonstrate that CS-LSTMs consistently outperform state-of-theart methods, highlighting their effectiveness and practical value in robust time series anomaly detection.

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