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.

翻译：单变量时间序列（UTS）作为网络系统与云服务器的关键指标，其每个时间戳仅记录单一变量。UTS异常检测在数据挖掘与系统可靠性管理中具有重要作用。然而，现有基于重构与基于预测的方法难以捕捉特定细微异常，尤其是微小点异常与缓升型异常。为解决这些挑战，本文提出一种名为"上下文与季节性LSTM"（CS-LSTM）的新型预测框架。该框架基于噪声分解策略，联合利用上下文依赖性与季节性模式，从而增强对细微异常的检测能力。通过融合时域与频域表征，CS-LSTM实现了对周期性趋势与异常定位的更精确建模。在公开基准数据集上的大量实验表明，CS-LSTM持续优于现有先进方法，凸显了其在鲁棒时间序列异常检测中的有效性与实用价值。