文章基本信息

标题：An Application of Genetic Programming in Nonlinear Combining Forecasting
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作者：Yingxiao Zhou ; Peng Zhao
期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：6
页码：443-454
DOI：10.14257/ijhit.2016.9.6.39
出版社：SERSC
摘要：It has been deemed as an effective tool of forecasting performance improvement to combine different component forecasting models. However, current nonlinear combining models are not able to meet the requirement of high forecasting accuracy in practice. To tackle this challenge, this paper constructs a hybrid, named genetic programming and least squared estimation based nonlinear combining method (GPLSE-NC), of a standard genetic programming (GP) algorithm and the least square estimation (LSE) method, based on which a new nonlinear combined forecasting model is proposed. To verify the feasibility of the proposed model, based on the container throughput data of Shanghai Port from January 2004 to November 2015, 4 different forecasting models are constructed and compared with the proposed GPLSE-NC combining model in terms of three forecasting performance evaluation criteria. The empirical results show significant superiority of the GPLSE-NC model over its rivals, which reveals that the proposed model has a great potential to be a powerful nonlinearly combine forecasting approach.
关键词：Container throughput forecasting; nonlinear combining forecasting; genetic ; programming; least square estimation