Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey

Understand

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.