Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
It addresses the interpretability gap in time series AI for critical real-world applications, but it is incremental as a survey rather than introducing new methods.
The paper surveys existing explainable AI (XAI) methods for time series data, highlighting the lack of interpretability in deep learning models used in critical applications like medicine and autonomous driving, and it illustrates the types of explanations these methods produce to build trust in AI systems.
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.