面向时变输出的Hilbert值函数分解解释框架
A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputs
浏览论文内容
中文总结 AI 辅助
针对时变输出解释中忽略输出分量依赖的问题，提出将函数分解推广至Hilbert值预测函数的统一解释框架，引入基于核的输出表示以支持多时间粒度解释，并在金融波动率与能源需求预测中验证。
中文摘要 AI 辅助
基于特征的解释方法量化特征对模型预测的影响，但主要针对标量输出设计。然而，在许多应用中，输出是函数型或多变量的，例如需求预测中的时变轨迹。因此，现有方法通常独立解释每个输出位置，忽略了输出分量之间的依赖关系。我们通过开发一个统一的框架来解决这一局限性，该框架用于时变输出的基于特征的解释。具体而言，我们将函数分解推广到Hilbert值预测函数，并将现有的基于特征的解释框架扩展到这一设置。我们的框架引入了基于核的输出表示，能够在多个时间粒度级别上实现考虑时间依赖性的解释，包括时间特定、时间分辨和时间聚合，同时提供统一视角，使现有方法成为特例。我们在合成数据和真实数据上验证了该框架，包括日内金融市场波动率预测和能源需求预测。
英文摘要
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 introduces kernel-based output representations that enable time-dependency-aware explanations at multiple levels of temporal granularity, including time-specific, time-resolved, and time-aggregated, while providing a unified view in which existing methods arise as special cases. We validate our framework on synthetic and real-world data, including intraday financial market volatility prediction and energy demand forecasting.
发表机构
- Leibniz Institute for Prevention Research and Epidemiology – BIPS（莱布尼茨预防研究与流行病学研究所——BIPS）
- University of Bremen（不来梅大学）
机构由 AI 辅助整理，请以论文原文为准。
