  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.
