  Given the increasing amount and general complexity of time series data in
domains such as finance, weather forecasting, and healthcare, there is a
growing need for state-of-the-art performance models that can provide
interpretable insights into underlying patterns and relationships. Attribution
techniques enable the extraction of explanations from time series models to
gain insights but are hard to evaluate for their robustness and
trustworthiness. We propose the Attribution Stability Indicator (ASI), a
measure to incorporate robustness and trustworthiness as properties of
attribution techniques for time series into account. We extend a perturbation
analysis with correlations of the original time series to the perturbed
instance and the attributions to include wanted properties in the measure. We
demonstrate the wanted properties based on an analysis of the attributions in a
dimension-reduced space and the ASI scores distribution over three whole time
series classification datasets.
