Explaining predictions based on multivariate time series data carries the additional difficulty of handling not only multiple features, but also time dependencies. It matters not only what happened, but also when, and the same feature could have a very different impact on a prediction depending on this time information. Previous work has used perturbation-based saliency methods to tackle this issue, perturbing an input using a trainable mask to discover which features at which times are driving the predictions. However these methods introduce fixed perturbations, inspired from similar methods on static data, while there seems to be little motivation to do so on temporal data. In this work, we aim to explain predictions by learning not only masks, but also associated perturbations. We empirically show that learning these perturbations significantly improves the quality of these explanations on time series data.

翻译：基于多变量时间序列数据解释预测结果，除了需处理多个特征外，还需应对时间依赖性的额外挑战。不仅事件发生的内容至关重要，其发生时间同样关键——同一特征因时间信息差异可能对预测产生截然不同的影响。以往研究采用基于扰动的显著性方法解决该问题，通过可训练掩码扰动输入，从而识别驱动预测的关键特征与时间点。然而，这些方法受静态数据相似方法启发而引入固定扰动，但将此类策略应用于时序数据似乎缺乏充分动机。本研究旨在通过同时学习掩码及其关联扰动来解释预测。实证结果表明，学习这些扰动可显著提升时间序列数据解释质量。