Forecasting Airline Demand by Using Hybric Bayesian Method and Time Series

Shokouhi Seta, Hamid Reza | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51947 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Refie, Majid
  7. Abstract:
  8. Using revenue management in any industry can increase the profit. In aviation industries, due to the huge number of requests and travels for each airline, a revenue management system can lead to a good profit for the airlines. The first step in revenue management system is predicting the demand.In this article two models are developed using time series techniques, based on the information taken from one of the Iranian airlines in Tehran-Mashhad fly route.The first model is developed using ARIMA and seasonal-ARIMA models and the second one is based on the demand and price history, price in the day of prediction and the ARIMA model. The second model which is a combination of price, prior price and demands shows less error.After that according to the properties of Bayesian prediction, it is used to estimate the parameters of the developed models. For both models, Bayesian prediction improves the result of the models and decreases the error. Finally, Bayesian model consisting of price and demand provides better results and is introduced as the final model
  9. Keywords:
  10. Time Series ; Bayesian Decision Making Theory ; Demand Forecasting ; Revenue Management ; Airline Management

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