用神经基函数建模电价分布，既准又可解释。
NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shape
- 用神经基函数分解位置、尺度和形状参数，提升可解释性
- 多市场实验中性能媲美传统神经网络，且能追踪特征影响路径
- 适合关注模型决策过程的电力市场研究者
在多时域分布回归中，使用灵活神经网络的预测器常难以深入理解导致预测分布参数变化的内在机制。本文提出一种用于位置、尺度和形状的神经基函数模型（NBMLSS），融合GAMLSS的严谨可解释性与可计算扩展的共享基分解，通过线性投影支持逐步和参数级的特征形状函数聚合。在多个市场区域进行实验，其概率预测性能与分布神经网络相当，同时通过学习到的非线性特征映射至分布参数，提供了对模型行为更深入的洞察。
原文摘要 · Abstract (English)
Forecasters using flexible neural networks (NN) in multi-horizon distributional regression setups often struggle to gain detailed insights into the underlying mechanisms that lead to the predicted feature-conditioned distribution parameters. In this work, we deploy a Neural Basis Model for Location, Scale and Shape, that blends the principled interpretability of GAMLSS with a computationally scalable shared basis decomposition, combined by linear projections supporting dedicated stepwise and parameter-wise feature shape functions aggregations. Experiments have been conducted on multiple market regions, achieving probabilistic forecasting performance comparable to that of distributional neural networks, while providing more insights into the model behavior through the learned nonlinear feature level maps to the distribution parameters across the prediction steps.
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