XAI Methods for Neural Time Series Classification: A Brief Review
It addresses the need for explainability in deep learning for time series data in critical applications, but is incremental as it is a review paper.
This paper reviews current eXplainable AI (XAI) methods for deep learning models applied to time series classification, focusing on making these models accountable in high-stake domains like medicine and finance, and suggests future research directions.
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.