Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series

TL;DR

This paper introduces a cluster-aware causal mixer model for real-time anomaly detection in multivariate time series, effectively capturing complex inter-channel relationships and maintaining causality to improve detection accuracy.

Contribution

The paper proposes a novel cluster-aware causal mixer that groups channels by correlation, processes each cluster with dedicated embeddings, and incorporates causality for improved online anomaly detection.

Findings

Achieves superior F1 scores across six benchmark datasets.

Effectively captures inter-channel correlations and preserves causality.

Operates efficiently in an online setting for real-time detection.

Abstract

Early and accurate detection of anomalies in time series data is critical, given the significant risks associated with false or missed detections. While MLP-based mixer models have shown promise in time series analysis, they lack a causality mechanism to preserve temporal dependencies inherent in the system. Moreover, real-world multivariate time series often contain numerous channels with diverse inter-channel correlations. A single embedding mechanism for all channels does not effectively capture these complex relationships. To address these challenges, we propose a novel cluster-aware causal mixer to effectively detect anomalies in multivariate time series. Our model groups channels into clusters based on their correlations, with each cluster processed through a dedicated embedding layer. In addition, we introduce a causal mixer in our model, which mixes the information while maintaining causality. Furthermore, we present an anomaly detection framework that accumulates the anomaly evidence over time to prevent false positives due to nominal outliers. Our proposed model operates in an online fashion, making it suitable for real-time time-series anomaly detection tasks. Experimental evaluations across six public benchmark datasets demonstrate that our model consistently achieves superior F1 scores.