  Time series data is fundamentally important for describing many critical
domains such as healthcare, finance, and climate, where explainable models are
necessary for safe automated decision-making. To develop eXplainable AI (XAI)
in these domains therefore implies explaining salient information in the time
series. Current methods for obtaining saliency maps assumes localized
information in the raw input space. In this paper, we argue that the salient
information of a number of time series is more likely to be localized in the
frequency domain. We propose FreqRISE, which uses masking based methods to
produce explanations in the frequency and time-frequency domain, which shows
the best performance across a number of tasks.
