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Attributions are a common local explanation technique for deep learningmodels on single samples as they are easily extractable and demonstrate therelevance of input values. In many cases, heatmaps visualize such attributionsfor samples, for instance, on images. However, heatmaps are not always theideal visualization to explain certain model decisions for other data types. Inthis review, we focus on attribution visualizations for time series. We collectattribution heatmap visualizations and some alternatives, discuss theadvantages as well as disadvantages and give a short position towards futureopportunities for attributions and explanations for time series.

Time Series Model Attribution Visualizations as Explanations