Data on forecasting energy prices using machine learning.
Ontology highlight
ABSTRACT:
SUBMITTER: Herrera GP
PROVIDER: S-EPMC6610706 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
Publications
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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