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

Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting