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Improving Forecasting Performance by Exploiting Expert Knowledge: Evidence from Guangzhou Port

Author

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  • Anqiang Huang

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, P. R. China)

  • Han Qiao

    (#x2020;School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China)

  • Shouyang Wang

    (#x2021;Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China)

  • John Liu

    (#xA7;College of Business, City University of Hong Kong, Kowloon, Hong Kong (SAR))

Abstract

Expert knowledge has been proved by substantial studies to be contributory to higher forecasting performance; meanwhile, its application is criticized and opposed by some groups for biases and inconsistency inherent in experts’ subjective judgment. This paper proposes a new approach to improving forecasting performance, which takes advantage of expert knowledge by constructing a constraint equation rather than directly adjusting the predicted values by experts. For the comparison purpose, the proposed approach, together with several widely used models including ARIMA, BP-ANN and the judgment model (JM), is applied to forecasting the container throughput of Guangzhou Port, which is one of the most important ports of China. Forecasting performances of the above models are compared and the results clearly show superiority of the proposed approach over its rivals, which implies that expert knowledge will make positive contribution as long as it is used in a right way.

Suggested Citation

  • Anqiang Huang & Han Qiao & Shouyang Wang & John Liu, 2016. "Improving Forecasting Performance by Exploiting Expert Knowledge: Evidence from Guangzhou Port," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 387-401, March.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:02:n:s0219622016500085
    DOI: 10.1142/S0219622016500085
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    References listed on IDEAS

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    Cited by:

    1. Li, Hongtao & Bai, Juncheng & Li, Yongwu, 2019. "A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

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