Data on forecasting energy prices using machine learning

被引：12

作者：

Herrera, Gabriel Paes ^{[1
,2
]}

Constantino, Michel ^{[2
]}

Tabak, Benjamin Miranda ^{[3
]}

Pistori, Hemerson ^{[2
,4
]}

Su, Jen-Je ^{[1
]}

Naranpanawa, Athula ^{[1
]}

机构：

[1] Griffith Univ, Dept Accounting Finance & Econ, Nathan Campus, Nathan, Qld 4111, Australia

[2] Univ Catolica Dom Bosco, Dept Environm Sci & Sustainabil, Campo Grande, MS, Brazil

[3] Getulio Vargas Fdn EPPG FGV, Sch Publ Policy & Govt, Brasilia, DF, Brazil

[4] Univ Fed Mato Grosso do Sul, Dept Comp Sci, Campo Grande, MS, Brazil

来源：

DATA IN BRIEF | 2019年 / 25卷

关键词：

Oil; Natural gas; Coal; ANN; PREDICTION;

D O I：

10.1016/j.dib.2019.104122

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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. (C) 2019 The Author(s). Published by Elsevier Inc.

引用

页数：5

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