  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.
