Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study

Authors:
Gang Xie;Shouyang Wang;Yingxue Zhao;Kin Keung Lai
Affiliations:
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;School of International Trade and Economics, University of International Business and Economics, Beijing 100029, P.R. China;Department of Management Sciences, City University of Hong Kong, Hong Kong
Venue:
Applied Soft Computing
Year:
2013

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Abstract

In this study, three hybrid approaches based on least squares support vector regression (LSSVR) model for container throughput forecasting at ports are proposed. The proposed hybrid approaches are compared empirically with each other and with other benchmark methods in terms of measurement criteria on the forecasting performance. The results suggest that the proposed hybrid approaches can achieve better forecasting performance than individual approaches. It is implied that the description of the seasonal nature and nonlinear characteristics of container throughput series is important for good forecasting performance, which can be realized efficiently by decomposition and the ''divide and conquer'' principle.