A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputs
Read the original on arXiv Machine Learning →The paper introduces a Hilbert‑valued functional decomposition framework that extends feature‑based explanations to time‑dependent outputs, such as trajectories in demand forecasting. It generalizes functional decomposition to Hilbert‑valued prediction functions and incorporates kernel‑based output representations, allowing explanations at multiple temporal granularities—time‑specific, time‑resolved, and time‑aggregated. The authors validate the framework on synthetic data and real‑world applications, including intraday financial market volatility prediction and energy demand forecasting.
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