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Journal of Systems Science and Complexity

, Volume 32, Issue 2, pp 615–633 | Cite as

A Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average: An Application in Air Travel Demand Forecasting

  • Xinyu Zhang
  • Yafei Zheng
  • Shouyang Wang
Article
  • 34 Downloads

Abstract

Demand forecasting is often difficult due to the unobservability of the applicable historical demand series. In this study, the authors propose a demand forecasting method based on stochastic frontier analysis (SFA) models and a model average technique. First, considering model uncertainty, a set of alternative SFA models with various combinations of explanatory variables and distribution assumptions are constructed to estimate demands. Second, an average estimate from the estimated demand values is obtained using a model average technique. Finally, future demand forecasts are achieved, with the average estimates used as historical observations. An empirical application of air travel demand forecasting is implemented. The results of a forecasting performance comparison show that in addition to its ability to estimate demand, the proposed method outperforms other common methods in terms of forecasting passenger traffic.

Keywords

Air travel demand demand forecasting model average model uncertainty stochastic frontier analysis 

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Copyright information

© The Editorial Office of JSSC & Springer-Verlag GmbH Germany 2019

Authors and Affiliations

  • Xinyu Zhang
    • 1
    • 2
  • Yafei Zheng
    • 1
    • 3
  • Shouyang Wang
    • 1
    • 2
  1. 1.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  2. 2.Center for Forecasting ScienceChinese Academy of SciencesBeijingChina
  3. 3.Postdoctoral Working StationShenwan Hongyuan Securities Co., Ltd.ShanghaiChina

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