Title: Fuel price forecasting combining wavelet neural network and adaptive differential evolution

Authors: Carlos Eduardo Klein; Wesley Vieira Da Silva; Claudimar Pereira Da Veiga; Viviana Cocco Mariani; Leandro Dos Santos Coelho

Addresses: Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, PUCPR, 1155 Imaculada Conceição, 80215-901, Curitiba, PR, Brazil ' Federal Rural University of Semi-Arid (UFERSA), 572 Francisco Mota Ave, 59.625-900, Mossoro, Brazil ' Postgraduate Program in Organizations Management, Leadership and Decision (PPGOLD), Federal University of Parana, 632 Lothário Meissner Ave., Jardim Botânico, 80210-170, Curitiba. PR, Brazil ' Department of Electrical Engineering, Federal University of Parana, UFPR, Polytechnic Center, 81531-970, Curitiba, Parana, Brazil; Department of Mechanical Engineering, Pontifical Catholic University of Parana, PUCPR, Rua Imaculada Conceição, 1155, 80215-901, Curitiba, PR, Brazil ' Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, PUCPR, 1155 Imaculada Conceição Ave, 80215-901, Curitiba, PR, Brazil; Department of Electrical Engineering, Federal University of Parana, UFPR, Polytechnic Center, 81531-970, Curitiba, Parana, Brazil

Abstract: Once economies are not linearly changing, significant research efforts have been devoted to developing efficient forecasting methods. Artificial neural network (ANN) has been widely applied in forecasting and pattern recognition tasks. Recently, the wavelet neural networks have become a promising tool for nonlinear mapping. In this context, the main of this paper is to forecast the future price for gasoline, diesel, liquid petroleum gas (LGP), liquid natural gas (LNG), and finally sugar cane ethanol. This study differs from previous contributing in literature with three aspects: 1) integration of wavelet analysis and computational intelligence techniques, which are limited in the fuels price forecasting area and are required for assessing the forecasting model for real-life applications; 2) to rank six different neural network structures among the fuels to point the best ones; 3) encourage a discussion about the role of oil price forecasting in wider economic analysis.

Keywords: artificial neural network; wavenet; differential evolution; oil price; gasoline price; forecasting.

DOI: 10.1504/IJBFMI.2020.111370

International Journal of Business Forecasting and Marketing Intelligence, 2020 Vol.6 No.3, pp.167 - 185

Received: 16 Feb 2020
Accepted: 21 Jun 2020
Published online: 23 Nov 2020 *

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