Interpretable Neural Networks for Time Series Regression: A New Approach

This research focuses on improving the interpretability of neural networks applied to time series data, a critical area for understanding and trusting AI predictions. The paper's approach of learning to mask and aggregate data offers a potentially valuable method for revealing the decision-making process within complex models.

• •The paper explores interpretable neural networks for time series regression.
• •The method involves learning to mask and aggregate data.
• •The research offers insights into model explainability.