LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling

TL;DR

This paper introduces a fully automated forecast pooling method combining LASSO and PCA, which improves electricity price prediction accuracy across multiple markets by reducing tuning parameter sensitivity.

Contribution

It presents a novel LASSO Principal Component Averaging (LPCA) approach that automates forecast pooling and outperforms existing methods in electricity price forecasting.

Findings

LPCA reduces forecast error effectively.

PCA component is robust to parameter choices.

LPCA outperforms simple averaging, LASSO, and PCA alone.

Abstract

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.