Paper

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

arXiv:2506.00188v2 Announce Type: replace Abstract: 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, an…

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