  Our research aims to propose a new performance-explainability analytical
framework to assess and benchmark machine learning methods. The framework
details a set of characteristics that systematize the
performance-explainability assessment of existing machine learning methods. In
order to illustrate the use of the framework, we apply it to benchmark the
current state-of-the-art multivariate time series classifiers.
