基于聚类的因果混合器用于多变量时间序列的在线异常检测
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series
AI总结：
本文提出了一种基于聚类的因果混合器，用于多变量时间序列的在线异常检测，通过聚类处理通道间的相关性，结合因果混合器保持时间因果性，并开发了序列异常评分方法以提高检测准确性。
摘要：
在时间序列数据中早期和准确地检测异常至关重要，因为假阳性和漏检带来的风险很大。虽然基于MLP的混合模型在时间序列分析中显示出潜力，但它们在数据处理过程中不维护时间因果性。此外，现实中的多变量时间序列通常包含众多通道，具有多样的通道间相关性。重构时间序列中的虚假相关性导致表示噪声，从而导致检测不准确。此外，忽略时间连续性的异常评分方法可能会误导连续检测。为了解决这些挑战，我们提出了一种多变量时间序列异常检测的基于聚类的因果混合器。根据相关性将通道分组为集群，并通过专用嵌入层对每个集群进行嵌入。引入因果混合器以在保持时间因果性的同时整合信息。我们进一步开发了一种序列异常评分方法，该方法在时间上累积证据并细化异常边界。我们提出的模型以在线方式运行，使其适合实时时间序列异常检测。在六个公开基准数据集上的实验评估表明，所提出的方法在性能上始终优于其他方法。
英文摘要：
Early and accurate detection of anomalies in time-series data is critical due to the substantial risks associated with false or missed detections. While MLP-based mixer models have shown promise in time-series analysis, they do not maintain temporal causality during data processing. Moreover, real-world multivariate time series often contain numerous channels with diverse inter-channel correlations. Spurious correlations in the reconstructed time series lead to noisy representations, resulting in inaccurate anomaly detection. In addition, anomaly scoring methods that ignore temporal continuity can mislead sequential detection. To address these challenges, we propose a cluster-aware causal mixer for multivariate time-series anomaly detection. Channels are grouped into clusters based on their correlations, and each cluster is embedded through a dedicated embedding layer. A causal mixer is introduced to integrate information while maintaining temporal causality. We further develop a sequential anomaly-scoring method that accumulates evidence over time and refines anomaly boundaries. Our proposed model operates in an online fashion, making it suitable for real-time time-series anomaly detection. Experimental evaluations across six public benchmark datasets demonstrate that the proposed approach consistently achieves superior performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
