  The field of eXplainable Artificial Intelligence (XAI) has greatly advanced
in recent years, but progress has mainly been made in computer vision and
natural language processing. For time series, where the input is often not
interpretable, only limited research on XAI is available. In this work, we put
forward a virtual inspection layer, that transforms the time series to an
interpretable representation and allows to propagate relevance attributions to
this representation via local XAI methods like layer-wise relevance propagation
(LRP). In this way, we extend the applicability of a family of XAI methods to
domains (e.g. speech) where the input is only interpretable after a
transformation. Here, we focus on the Fourier transformation which is
prominently applied in the interpretation of time series and LRP and refer to
our method as DFT-LRP. We demonstrate the usefulness of DFT-LRP in various time
series classification settings like audio and electronic health records. We
showcase how DFT-LRP reveals differences in the classification strategies of
models trained in different domains (e.g., time vs. frequency domain) or helps
to discover how models act on spurious correlations in the data.
