Data on forecasting energy prices using machine learning
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Constantino, Michel
Tabak, Benjamin Miranda
Pistori, Hemerson
Su, Jen-Je
Naranpanawa, Athula
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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.
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Data in Brief
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25
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© The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) License, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
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Subject
Environment and resource economics
Applied economics
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Oil
Natural gas
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Herrera, GP; Constantino, M; Tabak, BM; Pistori, H; Su, J-J; Naranpanawa, A, Data on forecasting energy prices using machine learning, Data in Brief, 2019, 25