A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputs
Feature-based explanations quantify features' influence on model predictions, but are primarily designed for scalar outputs. In many applications, however, outputs are functional or multivariate, such as time-dependent trajectories in demand forecasting. Consequently, existing approaches typically explain each output location independently, ignoring dependencies across the output components. We address this limitation by developing a unified framework for feature-based explanations of time-dependent outputs. Specifically, we generalize functional decomposition to Hilbert-valued prediction functions and extend an existing feature-based explanation framework to this setting. Our framework i...
| Authors | Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright et al. |
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| Primary category | stat.ML |
| Published | 10 September 2026 |
| Last updated at source | 14 September 2026 |
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