融合GPR与SVR预测德国电力价格，提升外推准确性
Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression
- 用多核GPR学习数据随机模式，结合自适应核函数增强性能
- 引入SVR处理非线性与异常值，其支持向量机制更稳健
- 线性融合两者预测结果，在德国电价数据上优于基准模型
本文提出一种新的混合模型用于预测德国电力价格。该算法结合高斯过程回归（GPR）与基于核的支撑向量回归（SVR）。尽管GPR在学习数据中的随机模式和插值方面表现良好，但其对样本外数据的预测性能有限。通过选择合适的数据依赖协方差函数，可提升GPR在测试的德国小时电价上的表现。然而，由于外推预测依赖训练数据，易受噪声和异常值影响。为此，采用另一种使用基于间隔优化的SVR进行独立预测，该方法在处理非线性过程和异常值时更具优势，因仅部分关键点（支持向量）参与回归。最终，将两种模型的预测结果以均匀权重线性组合。在历史德国电力价格数据上，该方法优于公开基准，包括由LASSO估计的自回归回归模型及[1]中提供的深度神经网络。
原文摘要 · Abstract (English)
This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian Process Regression (GPR) and Support Vector Regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out-of-sample data is not very promising. By choosing a suitable data-dependent covariance function, we can enhance the performance of GPR for the German hourly power prices being tested. However, since the out-of-sample prediction is dependent on the training data, the prediction is vulnerable to noise and outliers. To overcome this issue, a separate prediction is calculated using SVR, which applies margin-based optimization. This method is advantageous when dealing with non-linear processes and outliers, since only certain necessary points (support vectors) in the training data are responsible for regression. The individual predictions are then linearly combined using uniform weights. When tested on historic German power prices, this approach outperforms the publicly available benchmarks, namely the LASSO estimated autoregressive regression model, deep neural network provided in the recent research by [1].
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