用于电价概率预测的 Expectile 回归平均方法
Expectile regression averaging method for probabilistic forecasting of electricity prices
AI总结：
本文提出一种结合点预测平均与 expectile 回归的电价概率预测新方法，应用于德国日前市场并验证其优于 quantile 方法，同时指出建模前需进行方差稳定变换。
摘要：
本文提出了一种新的电价概率预测方法。该方法基于将不同模型的点预测与 expectile 回归相结合。我们证明了在某些情况下，用 expectiles 推导预测分布可能优于常用的 quantiles。我们将该方法应用于德国市场的日前电价，并将其准确性与 Quantile Regression Averaging 方法以及基于 quantile 和 expectile 的历史模拟法进行了比较。结果表明，使用 expectile 回归提高了电价概率预测的准确性，但建模前应先进行方差稳定变换。
英文摘要：
In this paper we propose a new method for probabilistic forecasting of electricity prices. It is based on averaging point forecasts from different models combined with expectile regression. We show that deriving the predicted distribution in terms of expectiles, might be in some cases advantageous to the commonly used quantiles. We apply the proposed method to the day-ahead electricity prices from the German market and compare its accuracy with the Quantile Regression Averaging method and quantile -- as well as expectile-based historical simulation. The obtained results indicate that using the expectile regression improves the accuracy of the probabilistic forecasts of electricity prices, but a variance stabilizing transformation should be applied prior to modelling.
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