用上下文与周期性建模提升时序异常检测精度
Contextual and Seasonal LSTMs for Time Series Anomaly Detection
- 通过噪声分解联合建模上下文与周期模式
- 在多个基准数据集上优于现有方法
- 适合检测微小点异常和缓慢上升异常
单变量时间序列（UTS）在互联网系统和云服务器中是关键指标，其异常检测对数据挖掘和系统可靠性管理至关重要。然而，现有基于重建和预测的方法难以捕捉某些细微异常，特别是微小点异常和缓慢上升异常。为此，我们提出一种新型预测框架——上下文与季节性LSTM（CS-LSTMs）。该模型基于噪声分解策略，同时利用上下文依赖关系和季节性模式，强化对细微异常的检测能力。通过融合时域与频域表示，实现对周期性趋势的更精确建模及异常定位。在多个公开基准数据集上的大量实验表明，CS-LSTMs始终优于当前最优方法，凸显其在鲁棒时序异常检测中的有效性与实际价值。
原文摘要 · Abstract (English)
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.
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