Hierarchical Granger-Causal Root Cause Analysis of Anomalies in Industrial Multivariate Time Series

Root cause analysis (RCA) of industrial multivariate time series aims to identify the variables directly responsible for anomalous events. This task is challenging because disturbances can propagate across interacting subsystems, causing many sensors to respond to a small number of root variables. Existing methods mainly model dependencies at the variable level, which may mix subsystem-level dynamics with local variable interactions. We propose HiRCA, a hierarchical Granger-causal predictive framework that models these two levels separately. HiRCA uses a soft engineering prior on subsystem membership to learn latent subsystem states and their temporal dependencies. At the variable level, it separates the effects of self-history, shared subsystem context, and cross-variable interactions on prediction. For diagnosis, HiRCA first aggregates calibrated innovation scores to identify candidate subsystems and then prioritizes their variables in the final ranking. On two synthetic benchmarks, HiRCA shows strong hierarchical graph-recovery performance and achieves Avg@10 scores above 0.90 for root cause localization. Across four real-world datasets, HiRCA ranks among the top three in nearly all evaluations, including 16 top-two variable ranking results and 15 best time-variable ranking results. These results demonstrate the effectiveness of hierarchical predictive modeling for structured, coarse-to-fine root cause localization in industrial multivariate time series.