用于电力现货价格预测的高维变量选择方法比较
A Comparison of High-Dimensional Variable Selection Procedures for Electricity Spot Price Forecasting
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中文总结 AI 辅助
本文对比了六种变量选择方法在电力现货价格预测中的性能，发现Boosting Multiple Testing（BMT）方法在预测准确性与LASSO、Elastic Net相当的前提下，变量使用量仅为其十分之一，兼具可解释性与计算效率。
中文摘要 AI 辅助
本文研究电力现货价格预测的变量选择问题。LASSO和Elastic Net等高维方法被广泛用于该场景，尽管它们具有很强的预测性能，但倾向于选择参数过度的模型，这引发了对可解释性的质疑。我们使用来自六个区域电力市场的大量数据集，评估六种变量选择方法的性能，包括最近提出的Boosting Multiple Testing（BMT，增强型多重检验）方法。我们从样本外预测准确性和模型简约性两个方面评估它们的性能。研究发现，尽管LASSO和Elastic Net实现了相似的准确性且优于大多数筛选替代方法，但BMT在达到与它们相当的预测性能的同时，使用的变量数量不到其十分之一。我们的结果表明，BMT为研究人员和从业者提供了一种比收缩方法更具可解释性和计算效率的替代方案，且不会损失任何预测准确性。这些发现表明，在电力价格预测中，与正则化方法相关的参数过度化并非预测准确性的必要代价。
英文摘要
The paper considers the problem of variable selection for forecasting electricity spot prices. High-dimensional methods such as LASSO and Elastic Net are widely used for this purpose, and while they exhibit strong predictive performance, their tendency to select over-parameterized models raises questions about interpretability. We evaluate the performance of six variable selection procedures, includingthe recently proposed Boosting Multiple Testing (BMT) method, using an extensive dataset from six regional electricity markets. We assess their performance in terms of both out-of-sample forecasting ac-curacy and model parsimony. We find that, although LASSO and Elastic Net achieve similar accuracy and outperform most screening alternatives, BMT matches their forecasting performance while using less than one-tenth as many variables. Our results reveal that BMT offers researchers and practitioners a substantially more interpretable and computationally efficient alternative to shrinkage methods, without any loss of forecasting accuracy. These findings suggest that the over-parameterization typically associated with regularization methods is not a necessary price for predictive accuracy in electricity price forecasting.
