When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate
arXiv:2512.03578v3 Announce Type: replace Abstract: Time series extrinsic regression (TSER) refers to the task of predicting a continuous target variable from an input time series. It appears in many domains, including healthcare, finance, environmental monitoring, and engineering. In these settings, accurate predictions and trustworthy reasoning are both essential. Although state-of-the-art TSER models achieve strong predictive performance, they typically operate as black boxes, making it difficult to understand which temporal patterns drive their decisions. Post-hoc interpretability techniqu
The increasing complexity and deployment of AI in critical sectors demand greater transparency and trust, driving ongoing research into interpretable AI models.
Improving the interpretability of time series AI models is crucial for their adoption in high-stakes environments like healthcare and finance, where understanding decision-making is paramount for regulatory compliance and user trust.
This research provides a methodology for building more transparent time series regression models, potentially accelerating their trusted integration into sensitive applications beyond black-box predictive tasks.
- · AI developers
- · Healthcare sector
- · Financial services
- · Regulatory bodies
- · Black-box AI model providers
Increased confidence and adoption of AI in applications requiring explainable predictions.
New regulatory frameworks may emerge to mandate or standardize interpretability for AI systems in critical infrastructure.
Interpretable AI could democratize access to advanced analytical tools by making them more understandable and auditable for non-experts.
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