LASSO主成分平均法——一种全自动的点预测合并方法
LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling
AI总结：
本文提出LASSO-PCA全自动预测合并方法，结合LASSO与主成分平均法，在四个电力市场的650个日前电价预测中表现优于其他方法，且对参数选择不敏感。
摘要：
本文提出了一种新颖的全自动预测平均方案，将LASSO估计方法与主成分平均法（PCA）相结合。LASSO-PCA（LPCA）方法基于单一模型但使用不同长度的校准窗口，对一组预测结果进行整合。该方法利用信息准则选择调节参数，从而减少了研究者临时决策的影响。该方法被应用于对日前小时电价进行平均预测，涵盖使用不同长度校准窗口获得的650个点预测。研究在四个欧洲和美洲市场上进行了评估，样本外时期接近两年半，并与简单平均、AW/WAW、LASSO和PCA等其他半自动和全自动方法进行了比较。结果表明，LASSO平均法在降低预测误差方面非常高效，而PCA方法对设定参数的选择具有稳健性。LPCA继承了两种方法的优点，在MAE指标上优于其他方法，并且对调节参数的选择不敏感。
英文摘要：
This paper develops a novel, fully automated forecast averaging scheme, which combines LASSO estimation method with Principal Component Averaging (PCA). LASSO-PCA (LPCA) explores a pool of predictions based on a single model but calibrated to windows of different sizes. It uses information criteria to select tuning parameters and hence reduces the impact of researchers' at hock decisions. The method is applied to average predictions of hourly day-ahead electricity prices over 650 point forecasts obtained with various lengths of calibration windows. It is evaluated on four European and American markets with almost two and a half year of out-of-sample period and compared to other semi- and fully automated methods, such as simple mean, AW/WAW, LASSO and PCA. The results indicate that the LASSO averaging is very efficient in terms of forecast error reduction, whereas PCA method is robust to the selection of the specification parameter. LPCA inherits the advantages of both methods and outperforms other approaches in terms of MAE, remaining insensitive the the choice of a tuning parameter.
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