LoCo: Local Competitive Learning for Interpretable Multivariate Time Series Anomaly Detection

In streaming data scenarios, multivariate time series anomaly detection is critical for system reliability. Beyond detection, operators require interpretable diagnostics to make informed decisions and repair faults efficiently. However, state-of-the-art detection models are often computationally heavy and opaque, and existing root cause analysis methods are confined to locating anomalous variables at the current timestamp, overlooking the temporal evolution of anomalies along causal chains. Inspired by physical phenomena and real cases, we introduce two design principles—Finite Temporal Horizon and Causal Horizon. These principles and the sparsity of fault-relevant causal connections motivate the design of LoCo, a lightweight interpretable framework based on Local Competitive Learning. LoCo performs online anomaly detection via forecasting residuals and, upon detection, traces root causes along the learned time-lagged causal matrix, from current anomalous variables back to earlier upstream sources. This time-lagged causal matrix not only captures causal influences among variables across different past time steps, but can also be compressed into a global causal graph to accommodate conventional causal discovery tasks. Theoretically, the recovered lagged predictive support coincides with the true structural causal support under identifiability assumptions. Extensive experiments across anomaly detection, root cause analysis, and causal discovery demonstrate that LoCo achieves competitive accuracy and computational efficiency, while providing end-to-end interpretable visualizations suitable for resource-constrained online deployment. The code is available at https://anonymous.4open.science/r/LoCo-491F/.