Forecasting Applied to the Electricity, Energy, Gas and Oil Industries: A Systematic Review

Ivan Borisov Todorov and Fernando Sánchez Lasheras ()
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Ivan Borisov Todorov: Department of Drilling, Oil and Gas Production & Transport, Faculty of Geology & Exploration, University of Mining and Geology St. Ivan Rilski, 1700 Sofia, Bulgaria
Fernando Sánchez Lasheras: Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain

Mathematics, 2022, vol. 10, issue 21, 1-15

Abstract: This paper presents a literature review in which methodologies employed for the forecast of the price of stock companies and raw materials in the fields of electricity, oil, gas and energy are studied. This research also makes an analysis of which data variables are employed for training the forecasting models. Three scientific databases were consulted to perform the present research: The Directory of Open Access Journals, the Multidisciplinary Digital Publishing Institute and the Springer Link. After running the same query in the three databases and considering the period from January 2017 to December 2021, a total of 1683 articles were included in the analysis. Of these, only 13 were considered relevant for the topic under study. The results obtained showed that when compared with other areas, few papers focus on the forecasting of the prices of raw materials and stocks of companies in the field under study. Furthermore, most make use of either machine learning methodologies or time series analysis. Finally, it is also remarkable that some not only make use of existing algorithms but also develop and test new methodologies.

Keywords: stock price forecasting; raw materials price forecasting; electricity companies; energy companies; gas and oil industry; publicly-traded companies; machine learning; time series (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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