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Wang, C. (2008) Prediction of Civil Aviation Passenger Traffic Volume on Grey Theory and RBF Neural Network. Beijing Jiaotong University, Beijing.

has been cited by the following article:

• TITLE: Prediction of Civil Aviation Passenger Transportation Based on ARIMA Model

  AUTHORS: Xinxin Tang, Guangming Deng

  KEYWORDS: Passenger Transportation, ARIMA Model, Seasonal Trend, Forecast

  JOURNAL NAME: Open Journal of Statistics, Vol.6 No.5, October 21, 2016

  ABSTRACT: The passenger transportation, as an important index to describe the scale of aviation passenger transport, prediction and research, can let us understand the future trend of the aviation passenger transport, according to it, the airline can make corresponding marketing strategy adjustment. Combining with the knowledge of time series let us understand the characteristics of passenger transportation change, the R software is used to fit the data, so as to establish the ARIMA(1,1,8) model to describe the civil aviation passenger transport developing trend in the future and to make reasonable predictions.