XAI Methods for Neural Time Series Classification: A Brief Review

Deep learning models are increasingly used in high-stakes domains (medicine, finance, industry) where predictions directly affect people's lives and have legal/financial consequences, making explainability essential rather than optional.

Time series data is particularly common in high-stakes applications but remains understudied in XAI research—the paper identifies this as a significant gap, since much data collected in critical domains takes the form of time series (medical monitoring, financial transactions, industrial sensors).

Current XAI methods for time series classification are limited—the paper reviews existing approaches but emphasizes that the field lacks mature, standardized techniques for explaining why deep learning models make specific predictions on sequential data.

Accountability requires transparency in automated decision-making—the authors argue that when deep learning models influence consequential decisions about individuals or organizations, stakeholders must be able to understand and audit the reasoning behind those predictions.

The field needs focused development on time series-specific XAI methods—the paper identifies promising directions for future work, suggesting that generic XAI approaches may not adequately address the unique challenges of explaining predictions made from temporal data patterns.