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.
翻译:本文开发了一个新的、完全自动化的预测平均计划,将LASSO估算法与主要成分挥发法(PCA)相结合。LASSO-PCA(LPCA)根据单一模型探索一系列预测,但以不同大小的窗口加以校准。它使用信息标准来选择调试参数,从而减少研究人员在权宜决定中的影响。该方法用于对日均电价平均预测,预测时间超过650点,并使用各种校准窗口。该方法在四个欧洲和美国市场上进行了评估,在模拟期结束后几乎两年半,与其他半自动半的半方法(如AW/WAW、LASSO和CPA)进行比较。结果显示,LASSO平均值在预测减少误差方面非常有效,而常设仲裁法院的方法对选择规格参数十分有力。LPCA继承了方法的优势,在MAE方面超越了其他方法,在选择调试参数方面仍然不敏感。