Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning
This work addresses the need for interpretability in anomaly detection for domains such as climate science, though it is incremental as it builds on existing detection methods.
The paper tackles the problem of explaining anomalies in multivariate time series by developing a novel attribution scheme using counterfactual reasoning, and it confirms the accuracy of this method on extreme climate events like heatwaves and hurricanes.
There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them. We strive to exceed this by explaining those anomalies. Thus we develop a novel attribution scheme for multivariate time series relying on counterfactual reasoning. We aim to answer the counterfactual question of would the anomalous event have occurred if the subset of the involved variables had been more similarly distributed to the data outside of the anomalous interval. Specifically, we detect anomalous intervals using the Maximally Divergent Interval (MDI) algorithm, replace a subset of variables with their in-distribution values within the detected interval and observe if the interval has become less anomalous, by re-scoring it with MDI. We evaluate our method on multivariate temporal and spatio-temporal data and confirm the accuracy of our anomaly attribution of multiple well-understood extreme climate events such as heatwaves and hurricanes.