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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/25978

Title: A comparison of univariate methods for forecasting container throughput volumes
Authors: Wen-Yi Peng;Ching-Wu Chu
Contributors: NTOU:Department of Shipping and Transportation Management
Keywords: Forecasting accuracy comparison;Univariate forecasting models;Container throughput
Date: 2009-10
Issue Date: 2011-10-20T08:33:21Z
Publisher: Mathematical and Computer Modeling
Abstract: Abstract:In this paper, six univariate forecasting models for the container throughput volumes in Taiwan’s three major ports are presented. The six univariate models include the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey model, the hybrid grey model, and the SARIMA model. The purpose of this paper is to search for a model that can provide the most accurate prediction of container throughput. By applying monthly data to these models and comparing the prediction results based on mean absolute error, mean absolute percent error and root mean squared error, we find that in general the classical decomposition model appears to be the best model for forecasting container throughput with seasonal variations. The result of this study may be helpful for predicting the short-term variation in demand for the container throughput of other international ports.
Relation: 50(7-8), pp.1045-1057
URI: http://ntour.ntou.edu.tw/handle/987654321/25978
Appears in Collections:[航運管理學系] 期刊論文

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