用图结构增强LSTM，提升稀疏异常检测效果
Graph-Augmented LSTM for Forecasting Sparse Anomalies in Graph-Structured Time Series
- 将节点间关系图融入LSTM，显式建模跨传感器依赖
- 在两个真实数据集上F1-score提升最高达10%
- 适合处理多变量、稀疏异常的时序图数据
在多个领域中，时间序列异常检测至关重要。当异常稀疏且数据为具有传感器或节点间依赖关系的多变量时，挑战尤为严峻。传统单变量异常检测器难以捕捉此类跨节点依赖，尤其在稀疏异常场景下表现不佳。为此，我们提出一种图增强的时间序列预测方法，将时间序列间的图关系显式集成到LSTM预测模型中，从而识别可能被纯单变量方法遗漏的罕见异常。我们在两个基准数据集——Yahoo Webscope S5异常数据集和METR-LA交通传感器网络——上评估该方法，并与仅使用LSTM、ARIMA和Prophet的基线模型进行对比。结果表明，图增强模型显著提升了精确率和召回率，其F1分数相比最佳基线最高提升10%。
原文摘要 · Abstract (English)
Detecting anomalies in time series data is a critical task across many domains. The challenge intensifies when anomalies are sparse and the data are multivariate with relational dependencies across sensors or nodes. Traditional univariate anomaly detectors struggle to capture such cross-node dependencies, particularly in sparse anomaly settings. To address this, we propose a graph-augmented time series forecasting approach that explicitly integrates the graph of relationships among time series into an LSTM forecasting model. This enables the model to detect rare anomalies that might otherwise go unnoticed in purely univariate approaches. We evaluate the approach on two benchmark datasets - the Yahoo Webscope S5 anomaly dataset and the METR-LA traffic sensor network - and compare the performance of the Graph-Augmented LSTM against LSTM-only, ARIMA, and Prophet baselines. Results demonstrate that the graph-augmented model achieves significantly higher precision and recall, improving F1-score by up to 10% over the best baseline
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