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http://dx.doi.org/10.5394/KINPR.2008.32.1.081

A study on the forecast of port traffic using hybrid ARIMA-neural network model  

Shin, Chang-Hoon (Department of Korea Maritime University)
Kang, Jeong-Sick (Department of Korea Maritime University)
Park, Soo-Nam (Department of Korea Maritime University)
Lee, Ji-Hoon (Graduate school of Korea Maritime University)
Publication Information
Journal of Navigation and Port Research / v.32, no.1, 2008 , pp. 81-88 More about this Journal
Abstract
The forecast of a container traffic has been very important for port plan and development. Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate that effectiveness can differ according to the characteristics of ports.
Keywords
Container port; Forecast; ARIMA model; ANN model; Hybrid model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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