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

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

104 Citations2021
Thomas Rojat, Raphael Puget, David Filliat
ArXiv

This paper presents an overview of existing explainable AI (XAI) methods applied on time series and illustrates the type of explanations they produce and provides a reflection on the impact of these explanation methods to provide confidence and trust in the AI systems.

Abstract

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.

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