Data on forecasting energy prices using machine learning.

Clicks: 256
ID: 42536
2019
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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.
Reference Key
herrera2019datadata Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Herrera, Gabriel Paes;Constantino, Michel;Tabak, Benjamin Miranda;Pistori, Hemerson;Su, Jen-Je;Naranpanawa, Athula;
Journal Data in brief
Year 2019
DOI
10.1016/j.dib.2019.104122
URL
Keywords Keywords not found

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