  Time series classification is a task which deals with temporal sequences, a
prevalent data type common in domains such as human activity recognition,
sports analytics and general sensing. In this area, interest in explainability
has been growing as explanation is key to understand the data and the model
better. Recently, a great variety of techniques have been proposed and adapted
for time series to provide explanation in the form of saliency maps, where the
importance of each data point in the time series is quantified with a numerical
value. However, the saliency maps can and often disagree, so it is unclear
which one to use. This paper provides a novel framework to quantitatively
evaluate and rank explanation methods for time series classification. We show
how to robustly evaluate the informativeness of a given explanation method
(i.e., relevance for the classification task), and how to compare explanations
side-by-side. The goal is to recommend the best explainer for a given time
series classification dataset. We propose AMEE, a Model-Agnostic Explanation
Evaluation framework, for recommending saliency-based explanations for time
series classification. In this approach, data perturbation is added to the
input time series guided by each explanation. Our results show that perturbing
discriminative parts of the time series leads to significant changes in
classification accuracy, which can be used to evaluate each explanation. To be
robust to different types of perturbations and different types of classifiers,
we aggregate the accuracy loss across perturbations and classifiers. This novel
approach allows us to recommend the best explainer among a set of different
explainers, including random and oracle explainers. We provide a quantitative
and qualitative analysis for synthetic datasets, a variety of timeseries
datasets, as well as a real-world case study with known expert ground truth.
