This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on combining Gaussian Process Regression (GPR) and Support Vector Regression (SVR). While GPR is a competent model for learning the stochastic pattern within the data and 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 tested German hourly power prices. However, since the out-of-sample prediction depends on the training data, the prediction is vulnerable to noise and outliers. To overcome this issue, a separate prediction is made using SVR, which applies margin-based optimization, having an advantage in dealing with non-linear processes and outliers, since only certain necessary points (support vectors) in the training data are responsible for regression. Both individual predictions are later combined using the performance-based weight assignment method. A test on historic German power prices shows that this approach outperforms its chosen benchmarks such as the autoregressive exogenous model, the naive approach, as well as the long short-term memory approach of prediction.

翻译：本文提出了一种用于预测德国电力价格的新型混合模型。该算法基于高斯过程回归与支持向量回归的组合。虽然GPR能够有效学习数据中的随机模式并实现插值，但其样本外数据的预测性能并不理想。通过选择合适的数据依赖协方差函数，我们可以提升GPR对德国小时级电力价格的测试性能。然而，由于样本外预测依赖于训练数据，该预测易受噪声和异常值影响。为解决此问题，本研究采用基于边界优化的SVR进行独立预测——该方法在处理非线性过程和异常值时具有优势，因为仅训练数据中特定的必要点（支持向量）参与回归计算。最终通过基于性能的权重分配方法将两种独立预测结果进行融合。对德国历史电力价格的测试表明，该方法在预测性能上优于自回归外生模型、朴素预测法以及长短期记忆预测模型等基准方法。