Forecasting container throughput of Qingdao port with a hybrid model

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• 作者：Anqiang Huang (1)
  Kinkeung Lai (2)
  Yinhua Li (3)
  Shouyang Wang (4)
  
  1. School of Economy and Management ; Beihang University ; Beijing ; 100191 ; China
  2. Department of Management Sciences ; City University of Hong Kong ; Kowloon ; Hong Kong ; China
  3. Research Center on Fictitious Economy and Data Science ; Chinese Academy of Sciences ; Beijing ; 100080 ; China
  4. Academy of Mathematics and Systems Science ; Chinese Academy of Sciences ; Beijing ; 100190 ; China
  
• 关键词：Container throughput forecast ; genetic programming algorithm ; outlier processing ; projection pursuit regression
• 刊名：Journal of Systems Science and Complexity
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：28
• 期：1
• 页码：105-121
• 全文大小：586 KB
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• 刊物类别：Mathematics and Statistics
• 刊物主题：Systems Theory and Control
  Applied Mathematics and Computational Methods of Engineering
  Operations Research/Decision Theory
  Probability Theory and Stochastic Processes
  
• 出版者：Academy of Mathematics and Systems Science, Chinese Academy of Sciences, co-published with Springer
• ISSN：1559-7067

文摘

This paper proposes a hybrid forecasting method to forecast container throughput of Qingdao Port. To eliminate the influence of outliers, local outlier factor (lof) is extended to detect outliers in time series, and then different dummy variables are constructed to capture the effect of outliers based on domain knowledge. Next, a hybrid forecasting model combining projection pursuit regression (PPR) and genetic programming (GP) algorithm is proposed. Finally, the hybrid model is applied to forecasting container throughput of Qingdao Port and the results show that the proposed method significantly outperforms ANN, SARIMA, and PPR models.