  Deep neural networks are one of the most successful classifiers across
different domains. However, due to their limitations concerning
interpretability their use is limited in safety critical context. The research
field of explainable artificial intelligence addresses this problem. However,
most of the interpretability methods are aligned to the image modality by
design. The paper introduces TimeREISE a model agnostic attribution method
specifically aligned to success in the context of time series classification.
The method shows superior performance compared to existing approaches
concerning different well-established measurements. TimeREISE is applicable to
any time series classification network, its runtime does not scale in a linear
manner concerning the input shape and it does not rely on prior data knowledge.
