SLRCA: Lightweight Root Cause Analysis of Multivariate Time Series via Parameter Sharing

Tracing the root causes of anomalies in multivariate time series is an important and challenging task. Root cause localization requires modeling each variable's normal behavior, but assigning a separate neural network to each variable duplicates temporal prediction parameters, leading to overfitting and parameter proliferation. We propose SLRCA (Shared Lightweight Root Cause Analysis), a lightweight framework for root cause localization through parameter sharing. SLRCA uses vector autoregression (VAR) to model variable-specific linear lagged dependencies, then uses a single temporal convolutional network (TCN) to refine the linear predictions from each variable's residual history. Together, the two components predict each variable's normal behavior; the model uses deviations of actual observations from these predictions to rank candidate root causes. Across four types of synthetic systems and three real-world datasets, SLRCA achieves excellent performance while using only approximately 0.1%–1% as many model parameters as the million-parameter comparison methods.