arXiv paper proposes Hilbert-valued framework for explaining time-dependent model outputs

A new preprint introduces a decomposition method that extends feature-attribution explanations from single-number predictions to functional or multivariate outputs, such as demand forecasts that vary over time. The approach works in a Hilbert space so that the influence of each input feature can be separated across the whole output trajectory rather than summarized by one score. The authors position it as a general framework for settings where model predictions are curves or vectors instead of scalars.