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.

翻译：时间序列数据中异常的早期准确检测至关重要，因为误检或漏检会带来重大风险。虽然基于多层感知机的混合模型在时间序列分析中展现出潜力，但其数据处理过程无法维持时间因果性。此外，真实世界多元时间序列通常包含具有多样通道间相关性的众多通道。重建时间序列中的虚假相关性会导致噪声表征，进而引发不准确的异常检测。同时，忽略时间连续性的异常评分方法可能误导序列检测。为应对这些挑战，我们提出了一种面向多元时间序列异常检测的聚类感知因果混合器。根据通道相关性将其分组为簇，并通过专用嵌入层对每个簇进行嵌入。引入因果混合器在保持时间因果性的同时整合信息。我们进一步开发了序列化异常评分方法，该方法随时间积累证据并优化异常边界。所提模型采用在线模式运行，适用于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，所提方法始终取得优越性能。