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A forecasting method for Chinese civil planes attendance rate based on vague sets

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  • Zhou, Shenghan
  • Hu, Chen
  • Qiao, Xiaoduo
  • Chang, Wenbing

Abstract

This paper investigates the feasibility and efficiency of Vague sets to forecast the attendance rate in civil airplanes. Firstly, an overview of the methods of using fuzzy theory for forecasting is made and some problems are pointed out; the concepts of vague sets is then reviewed; and then an improved vague forecasting method is presented to overcome the shortcoming of the previous studies; finally, an comparison between different methods is raised to verify its effectiveness.

Suggested Citation

  • Zhou, Shenghan & Hu, Chen & Qiao, Xiaoduo & Chang, Wenbing, 2016. "A forecasting method for Chinese civil planes attendance rate based on vague sets," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 518-526.
    • Handle: RePEc:eee:chsofr:v:89:y:2016:i:c:p:518-526
    DOI: 10.1016/j.chaos.2016.02.037
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    References listed on IDEAS

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    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Findley, David F. & Wills, Kellie C. & Monsell, Brian C., 2004. "Seasonal adjustment perspectives on "Damping seasonal factors: shrinkage estimators for the X-12-ARIMA program"," International Journal of Forecasting, Elsevier, vol. 20(4), pages 551-556.
    3. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    4. Ping Wang & Kuo-Ming Chao & Chi-Chun Lo, 2015. "Satisfaction-based Web service discovery and selection scheme utilizing vague sets theory," Information Systems Frontiers, Springer, vol. 17(4), pages 827-844, August.
    5. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    6. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    7. S. A. Roberts, 1982. "A General Class of Holt-Winters Type Forecasting Models," Management Science, INFORMS, vol. 28(7), pages 808-820, July.
    8. S. Chatterjee & S. Nigam & J. B. Singh & L. N. Upadhyaya, 2011. "Application of fuzzy time series in prediction of time between failures & faults in software reliability assessment," Fuzzy Information and Engineering, Springer, vol. 3(3), pages 293-309, September.
    9. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
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