Most of state of the art methods applied on time series consist of deep learning methods that are too complex to be interpreted. This lack of interpretability is a major drawback, as several applications in the real world are critical tasks, such as the medical field or the autonomous driving field. The explainability of models applied on time series has not gather much attention compared to the computer vision or the natural language processing fields. In this paper, we present an overview of existing explainable AI (XAI) methods applied on time series and illustrate the type of explanations they produce. We also provide a reflection on the impact of these explanation methods to provide confidence and trust in the AI systems.

The useful This paper reports on the ongoing work done in the information is obviously multilevel. In this work we are GVLEX project. The aim of this multidisciplinary project not willing to design complete analysis for each level of is to design and test a storytelling humanoid robot. Ideally, interest but rather to design a multilevel analysis able to the robot would be able to process automatically a given point out the interesting parts of the tale. Based on the tale or short story, and to play it for a children audience.