  Time series forecasting is an important yet challenging task. Though deep
learning methods have recently been developed to give superior forecasting
results, it is crucial to improve the interpretability of time series models.
Previous interpretation methods, including the methods for general neural
networks and attention-based methods, mainly consider the interpretation in the
feature dimension while ignoring the crucial temporal dimension. In this paper,
we present the series saliency framework for temporal interpretation for
multivariate time series forecasting, which considers the forecasting
interpretation in both feature and temporal dimensions. By extracting the
"series images" from the sliding windows of the time series, we apply the
saliency map segmentation following the smallest destroying region principle.
The series saliency framework can be employed to any well-defined deep learning
models and works as a data augmentation to get more accurate forecasts.
Experimental results on several real datasets demonstrate that our framework
generates temporal interpretations for the time series forecasting task while
produces accurate time series forecast.
