Contrastive explanations for a deep learning model on time-series data

Abstract

In the last decade, with the irruption of Deep Learning (DL), artificial intelligence has risen a step concerning previous years. Although Deep Learning models have gained strength in many fields like image classification, speech recognition, time-series anomaly detection, etc. these models are often difficult to understand because of their lack of interpretability. In recent years an effort has been made to understand DL models, creating a new research area called Explainable Artificial Intelligence (XAI). Most of the research in XAI has been done for image data, and little research has been done in the time-series data field. In this paper, a model-agnostic method called Contrastive Explanation Method (CEM) is used for interpreting a DL model for time-series classification. Even though CEM has been validated in tabular data and image data, the obtained experimental results show that CEM is also suitable for interpreting deep learning models that work with time-series data.