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http://dx.doi.org/10.5394/KINPR.2011.35.1.83

A Study on Application of ARIMA and Neural Networks for Time Series Forecasting of Port Traffic  

Shin, Chang-Hoon (Department of Logistics Engineering, National Korea Maritime University)
Jeong, Su-Hyun (Graduate school of National Korea Maritime University)
Publication Information
Journal of Navigation and Port Research / v.35, no.1, 2011 , pp. 83-91 More about this Journal
Abstract
The accuracy of forecasting is remarkably important to reduce total cost or to increase customer services, so it has been studied by many researchers. In this paper, the artificial neural network (ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. It uses a hybrid methodology that combines both the linear ARIAM and the nonlinear ANN model to improve forecasting performance. Also, it compares the methodology with other models in performance for prediction. In designing network structure, this work specially applies the genetic algorithm which is known as the effectively optimal algorithm in the huge and complex sample space. It includes the time delayed neural network (TDNN) as well as multi-layer perceptron (MLP) which is the most popular neural network model. Experimental results indicate that both ANN and Hybrid models outperform ARIMA model.
Keywords
Container traffic; forecasting; ARIMA model; ANN model; Hybrid model; MLP; TDNN; GA;
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Times Cited By KSCI : 2  (Citation Analysis)
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