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Data on forecasting energy prices using machine learning.

ABSTRACT: 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.

SUBMITTER: Herrera GP 

PROVIDER: S-EPMC6610706 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Data on forecasting energy prices using machine learning.

Herrera Gabriel Paes GP   Constantino Michel M   Tabak Benjamin Miranda BM   Pistori Hemerson H   Su Jen-Je JJ   Naranpanawa Athula A  

Data in brief 20190612

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 R  ...[more]

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