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.
翻译:在时间序列中发现异常有多种方法,但这只是理解这些异常现象的第一步。我们努力通过解释这些异常现象来超越这些异常现象。因此,我们根据反事实推理,为多变时间序列制定了一个新的归因办法。我们的目标是回答反事实问题:如果所涉变量的子集与异常间隔之外的数据分布更为相似,反常事件是否会发生?具体地说,我们使用最大分辨间(MDI)算法探测异常间隔,用所探测到的间隔内分配值取代一组变量,并观察间隔是否变得不太异常,再与MDI相连接。我们评估了我们关于多变时间和时空数据的方法,并证实我们不同地将热浪和飓风等多深处的极端气候事件归因现象的准确性。