Contextual and Seasonal LSTMs for Time Series Anomaly Detection
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917263734472704 |
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| author | Zhang, Lingpei Li, Qingming Yang, Yong Chen, Jiahao Zeng, Rui Lyu, Chenyang Ji, Shouling |
| author_facet | Zhang, Lingpei Li, Qingming Yang, Yong Chen, Jiahao Zeng, Rui Lyu, Chenyang Ji, Shouling |
| contents | 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 frequency-domain 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-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09690 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Contextual and Seasonal LSTMs for Time Series Anomaly Detection Zhang, Lingpei Li, Qingming Yang, Yong Chen, Jiahao Zeng, Rui Lyu, Chenyang Ji, Shouling Machine Learning 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 frequency-domain 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-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection. |
| title | Contextual and Seasonal LSTMs for Time Series Anomaly Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.09690 |