An empirical evidence for generalized shrinkage methods: application of bagging in day-ahead electricity price forecasting and factor augmentation .

2020
Özen, Kadir
Fundamental dynamics behind electricity prices are multi-dimensional and elaborate. A popular approach to forecasting electricity price is to utilize large number of predictors. In this study, using the day-ahead electricity price data from commonly studied markets of five major series and GEFCom2014 data, a variant of shrinkage method, Bootstrap Aggregation (bagging) is proposed to incorporate information from available predictors. Bagging manifests itself as a computationally simpler alternative to commonly used Least Absolute Shrinkage and Selection Operator (lasso) in multivariate EPF context and even shows superior forecasting ability in some markets. Moreover, considering the significant dependence of intra-day electricity prices, we also propose factor augmentation to exploit this dependence. The inclusion of latent factors, selected via Bayesian Information Criterion, improves ability to forecast in multivariate modeling framework and in some cases even outperform sophisticated shrinkage methods as measured by the Diebold-Mariano test.
Citation Formats
K. Özen, “An empirical evidence for generalized shrinkage methods: application of bagging in day-ahead electricity price forecasting and factor augmentation .,” Thesis (M.S.) -- Graduate School of Social Sciences. Economics., Middle East Technical University, 2020.