XAI Methods for Neural Time Series Classification: A Brief Review

Abstract

Deep learning models have recently demonstrated remarkable results in a variety of tasks, which is why they are being increasingly applied in high-stake domains, such as industry, medicine, and finance. Considering that automatic predictions in these domains might have a substantial impact on the well-being of a person, as well as considerable financial and legal consequences to an individual or a company, all actions and decisions that result from applying these models have to be accountable. Given that a substantial amount of data that is collected in high-stake domains are in the form of time series, in this paper we examine the current state of eXplainable AI (XAI) methods with a focus on approaches for opening up deep learning black boxes for the task of time series classification. Finally, our contribution also aims at deriving promising directions for future work, to advance XAI for deep learning on time series data.

arXiv comment8 pages, 0 figures, Accepted as a poster presentation

References in corpus (6)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Deep learning for time series classification: a review
• Towards A Rigorous Science of Interpretable Machine Learning
• Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
• Explaining Deep Classification of Time-Series Data with Learned Prototypes
• Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization