Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm

Payman Eslami (), Kihyo Jung, Daewon Lee and Amir Tjolleng
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Payman Eslami: School of Industrial Engineering, University of Ulsan
Kihyo Jung: School of Industrial Engineering, University of Ulsan
Daewon Lee: School of Industrial Engineering, University of Ulsan
Amir Tjolleng: School of Industrial Engineering, University of Ulsan

Maritime Economics & Logistics, 2017, vol. 19, issue 3, No 7, 538-550

Abstract: Abstract Short-term prediction of tanker freight rates (TFRs) is strategically important to stakeholders in the oil shipping industry. This study develops a hybrid TFR prediction model based on an artificial neural network (ANN) and an adaptive genetic algorithm (AGA). The AGA adaptively searches satisficing network parameters such as input delay size. The ANN iteratively optimizes a prediction network considering parsimonious variables and time-lag effects as predictors. Three parsimonious variables (crude oil price, fleet productivity and bunker price) are selected by a stepwise regression of TFR variables. The article compares the performance of its hybrid model with two traditional approaches (regression and moving average), as well as with the findings of existing ANN studies. The results of our model (root mean squared error (RMSE)=11.2 WS) are not only significantly superior to the regression approach (RMSE=21.6 WS) and the moving average approach (RMSE=17.5 WS), but are even slightly superior to the results of existing ANN studies (RMSE=14.6 WS–15.8 WS).

Keywords: tanker freight rate; parsimonious variable; time-lag effect; artificial neural network; adaptive genetic algorithm; hybrid prediction model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1057/mel.2016.1

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