  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.
