  Explainable Artificial Intelligence (XAI) has gained significant attention
recently as the demand for transparency and interpretability of machine
learning models has increased. In particular, XAI for time series data has
become increasingly important in finance, healthcare, and climate science.
However, evaluating the quality of explanations, such as attributions provided
by XAI techniques, remains challenging. This paper provides an in-depth
analysis of using perturbations to evaluate attributions extracted from time
series models. A perturbation analysis involves systematically modifying the
input data and evaluating the impact on the attributions generated by the XAI
method. We apply this approach to several state-of-the-art XAI techniques and
evaluate their performance on three time series classification datasets. Our
results demonstrate that the perturbation analysis approach can effectively
evaluate the quality of attributions and provide insights into the strengths
and limitations of XAI techniques. Such an approach can guide the selection of
XAI methods for time series data, e.g., focusing on return time rather than
precision, and facilitate the development of more reliable and interpretable
machine learning models for time series analysis.
