  In the last decade neural network have made huge impact both in industry and
research due to their ability to extract meaningful features from imprecise or
complex data, and by achieving super human performance in several domains.
However, due to the lack of transparency the use of these networks is hampered
in the areas with safety critical areas. In safety-critical areas, this is
necessary by law. Recently several methods have been proposed to uncover this
black box by providing interpreation of predictions made by these models. The
paper focuses on time series analysis and benchmark several state-of-the-art
attribution methods which compute explanations for convolutional classifiers.
The presented experiments involve gradient-based and perturbation-based
attribution methods. A detailed analysis shows that perturbation-based
approaches are superior concerning the Sensitivity and occlusion game. These
methods tend to produce explanations with higher continuity. Contrarily, the
gradient-based techniques are superb in runtime and Infidelity. In addition, a
validation the dependence of the methods on the trained model, feasible
application domains, and individual characteristics is attached. The findings
accentuate that choosing the best-suited attribution method is strongly
correlated with the desired use case. Neither category of attribution methods
nor a single approach has shown outstanding performance across all aspects.
