Oil Forecasting Using Artificial Intelligence

Andreas Karathanasopoulos , Adam Zaremba , Mohammed Osman , Mateusz Mikutowski

Abstract

The motivation for this research paper is the application of two novel models in the prediction of crude oil index. The first model is a generic deep belief network and the second model is an adaptive neural fuzzy inference system. Furthermore we have to emphasize on the second contribution in this paper which is the use of an extensive number of inputs including mixed and autoregressive inputs. Both proposed methodologies have been used in the past in different problems such as face recognition, prediction of chromosome anomalies etch, providing higher outputs than usual. For comparison purposes, the forecasting statistical and empirical accuracy of models is benchmarked with traditional strategies such as a naive strategy, a moving average convergence divergence model and an autoregressive moving average model. As it turns out, the proposed novel techniques produce higher statistical and empirical results outperforming the other linear models. Concluding first time such research work brings such outstanding outputs in terms of forecasting oil markets.
Author Andreas Karathanasopoulos - Dubai Business School, University of Dubai
Andreas Karathanasopoulos,,
-
, Adam Zaremba (WZ / KIiRK)
Adam Zaremba,,
- Department of Investment and Capital Markets
, Mohammed Osman - Dubai Business School, University of Dubai
Mohammed Osman,,
-
, Mateusz Mikutowski (WZ / KIiRK)
Mateusz Mikutowski,,
- Department of Investment and Capital Markets
Journal seriesTheoretical Economics Letters, ISSN 2162-2078 , e-ISSN 2162-2086, (0 pkt)
Issue year2019
Vol9
No7
Pages2283-2290
Publication size in sheets0.5
Keywords in Polishkontrakty futures, ropa naftowa, model ANFIS
Keywords in EnglishFuture Contract, Crude Oil, Deep Beliefs, ANFIS Model
DOIDOI:10.4236/tel.2019.97144
URL http://www.scirp.org/journal/paperinformation.aspx?paperid=95160
Languageen angielski
Score (nominal)5
Score sourcejournalList
ScoreMinisterial score = 5.0, 16-04-2020, ArticleFromJournal
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