提出可解释的量化神经基模型，提升电价预测透明度。
From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
- 用共享基分解与权重因子化构建可解释神经网络
- 在一天内电价预测中性能媲美主流方法
- 揭示输入特征到预测结果的非线性映射关系
尽管神经网络在多时步概率预测中已实现高精度，但理解其输出如何依赖于输入特征仍是挑战。本文引入量化神经基模型（Quantile Neural Basis Model），将分位数广义加法模型的可解释性原则融入端到端神经网络训练框架。通过共享基分解和权重因子化，弥补传统神经模型在位置、尺度、形状建模中的不足，同时避免参数化分布假设。在一天内电力价格预测任务中验证，该方法性能与分布式及分位数回归神经网络相当，且能通过学习到的非线性映射揭示模型行为机制。
原文摘要 · Abstract (English)
While neural networks are achieving high predictive accuracy in multi-horizon probabilistic forecasting, understanding the underlying mechanisms that lead to feature-conditioned outputs remains a significant challenge for forecasters. In this work, we take a further step toward addressing this critical issue by introducing the Quantile Neural Basis Model, which incorporates the interpretability principles of Quantile Generalized Additive Models into an end-to-end neural network training framework. To this end, we leverage shared basis decomposition and weight factorization, complementing Neural Models for Location, Scale, and Shape by avoiding any parametric distributional assumptions. We validate our approach on day-ahead electricity price forecasting, achieving predictive performance comparable to distributional and quantile regression neural networks, while offering valuable insights into model behavior through the learned nonlinear mappings from input features to output predictions across the horizon.
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