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Does Interval Knowledge Sharpen Forecasting Models? Evidence from China’s Typical Ports

Author

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  • Anqiang Huang

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

  • Kin Keung Lai

    (#x2020;Department of Management Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong, P. R. China)

  • Han Qiao

    (#x2021;School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China)

  • Shouyang Wang

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

  • Zhenji Zhang

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

Abstract

Substantial studies integrating experts’ point knowledge with statistical forecasting modes have been implemented to investigate a long-lasting and disputing issue which is whether or not expert knowledge could improve forecasting performance. However, a large body of current forecasting studies neglect the application of experts’ interval knowledge where experts are expected to be more competent, considering that humans do much better in fuzzy calculation like interval estimation than in accurate computation like point estimation. To fill in this gap, this paper first proposes a novel forecasting paradigm incorporating interval knowledge generated by a Delphi-based expert system into the SARIMA and SVR models. For validation purposes, the proposed paradigm is applied to several representative seaports from the top three dynamic economic regions in China. The empirical results clearly show that interval knowledge, following the proposed paradigm, significantly improves the forecasting performance. This finding implies that the proposed forecasting paradigm has the good potential to be an effective method for sharpening the statistical models for container throughput forecasting.

Suggested Citation

  • Anqiang Huang & Kin Keung Lai & Han Qiao & Shouyang Wang & Zhenji Zhang, 2018. "Does Interval Knowledge Sharpen Forecasting Models? Evidence from China’s Typical Ports," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 467-483, March.
  • Handle: RePEc:wsi:ijitdm:v:17:y:2018:i:02:n:s0219622017500456
    DOI: 10.1142/S0219622017500456
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    as
    1. Daniel Kahneman & Dan Lovallo, 1993. "Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking," Management Science, INFORMS, vol. 39(1), pages 17-31, January.
    2. Sanders, Nada R. & Manrodt, Karl B., 2003. "The efficacy of using judgmental versus quantitative forecasting methods in practice," Omega, Elsevier, vol. 31(6), pages 511-522, December.
    3. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    4. Webby, Richard & O'Connor, Marcus, 1996. "Judgemental and statistical time series forecasting: a review of the literature," International Journal of Forecasting, Elsevier, vol. 12(1), pages 91-118, March.
    5. Fildes, Robert & Stekler, Herman, 2002. "Reply to the comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 503-505, December.
    6. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
    7. Stewart, Thomas R. & Roebber, Paul J. & Bosart, Lance F., 1997. "The Importance of the Task in Analyzing Expert Judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 69(3), pages 205-219, March.
    8. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    9. Fred Collopy & J. Scott Armstrong, 1992. "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," Management Science, INFORMS, vol. 38(10), pages 1394-1414, October.
    10. Armstrong, J. Scott & Collopy, Fred & Yokum, J. Thomas, 2005. "Decomposition by causal forces: a procedure for forecasting complex time series," International Journal of Forecasting, Elsevier, vol. 21(1), pages 25-36.
    11. Fernandez, Viviana, 2007. "Wavelet- and SVM-based forecasts: An analysis of the U.S. metal and materials manufacturing industry," Resources Policy, Elsevier, vol. 32(1-2), pages 80-89.
    12. Clemen, Robert T. & Murphy, Allan H. & Winkler, Robert L., 1995. "Screening probability forecasts: contrasts between choosing and combining," International Journal of Forecasting, Elsevier, vol. 11(1), pages 133-145, March.
    13. Stephen J. Hoch & David A. Schkade, 1996. "A Psychological Approach to Decision Support Systems," Management Science, INFORMS, vol. 42(1), pages 51-64, January.
    14. Wei Huang & K. K. Lai & Y. Nakamori & Shouyang Wang, 2004. "Forecasting Foreign Exchange Rates With Artificial Neural Networks: A Review," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 3(01), pages 145-165.
    15. Sniezek, Janet A., 1989. "An examination of group process in judgmental forecasting," International Journal of Forecasting, Elsevier, vol. 5(2), pages 171-178.
    16. Seifert, Matthias & Hadida, Allègre L., 2013. "On the relative importance of linear model and human judge(s) in combined forecasting," Organizational Behavior and Human Decision Processes, Elsevier, vol. 120(1), pages 24-36.
    17. Goodwin, P & Wright, G, 1994. "Heuristics, biases and improvement strategies in judgmental time series forecasting," Omega, Elsevier, vol. 22(6), pages 553-568, November.
    18. Robert T. Clemen & Robert L. Winkler, 1999. "Combining Probability Distributions From Experts in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 187-203, April.
    19. Fischer, Ilan & Harvey, Nigel, 1999. "Combining forecasts: What information do judges need to outperform the simple average?," International Journal of Forecasting, Elsevier, vol. 15(3), pages 227-246, July.
    20. Harvey, Nigel & Harries, Clare, 2004. "Effects of judges' forecasting on their later combination of forecasts for the same outcomes," International Journal of Forecasting, Elsevier, vol. 20(3), pages 391-409.
    21. Rich, J. & Holmblad, P.M. & Hansen, C.O., 2009. "A weighted logit freight mode-choice model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(6), pages 1006-1019, November.
    22. Yi Xiao & Shouyang Wang & John J. Liu & Jin Xiao & Yi Hu, 2016. "Throughput estimation based port development and management policies analysis," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(1), pages 84-97, January.
    23. Lean Yu & Shouyang Wang & Jie Cao, 2009. "A Modified Least Squares Support Vector Machine Classifier With Application To Credit Risk Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 697-710.
    24. Lean Yu & Kin Keung Lai & Shou-Yang Wang, 2006. "Currency Crisis Forecasting With General Regression Neural Networks," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 437-454.
    25. Gu Pang & Bartosz Gebka, 2017. "Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2454-2469, May.
    26. Robert C. Blattberg & Stephen J. Hoch, 1990. "Database Models and Managerial Intuition: 50% Model + 50% Manager," Management Science, INFORMS, vol. 36(8), pages 887-899, August.
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