Data on forecasting energy prices using machine learning
Gabriel Paes Herrera,
Michel Constantino,
Benjamin Miranda Tabak,
Hemerson Pistori,
Jen-Je Su,
Athula Naranpanawa
Affiliations
Gabriel Paes Herrera
Department of Accounting, Finance and Economics, Griffith University, Nathan Campus, Queensland 4111, Australia; Department of Environmental Sciences and Sustainability, Dom Bosco Catholic University, Campo Grande, MS, Brazil; Corresponding author. Department of Accounting, Finance and Economics, Griffith University, Nathan Campus, Queensland 4111, Australia.
Michel Constantino
Department of Environmental Sciences and Sustainability, Dom Bosco Catholic University, Campo Grande, MS, Brazil
Benjamin Miranda Tabak
School of Public Policy and Government, Getulio Vargas Foundation (EPPG/FGV), Brasilia, DF, Brazil
Hemerson Pistori
Department of Environmental Sciences and Sustainability, Dom Bosco Catholic University, Campo Grande, MS, Brazil; Department of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande, MS, Brazil
Jen-Je Su
Department of Accounting, Finance and Economics, Griffith University, Nathan Campus, Queensland 4111, Australia
Athula Naranpanawa
Department of Accounting, Finance and Economics, Griffith University, Nathan Campus, Queensland 4111, Australia
This article contains the data related to the research article “Long-term forecast of energy commodities price using machine learning” (Herrera et al., 2019). The datasets contain monthly prices of six main energy commodities covering a large period of nearly four decades. Four methods are applied, i.e. a hybridization of traditional econometric models, artificial neural networks, random forests, and the no-change method. Data is divided into 80-20% ratio for training and test respectively and RMSE, MAPE, and M-DM test used for performance evaluation. Other methods can be applied to the dataset and used as a benchmark. Keywords: Oil, Natural gas, Coal, ANN
