Explainable AI for time series via Virtual Inspection Layers

Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek

Abstract: Highlights•We propose a new form of explanation for models trained on time series data.•We derive a closed-form formula for relevance propagation through the DFT and STDFT.•We extend evaluations to allow for comparison of explanations in different formats.•We show how our method gives insights into strategies of audio and ECG classifiers.