Air Traffic Forecasting Using Time Series Models: New Perspectives

  • Manohar Dingari Department of Mathematics, GITAM University, Hyderabad-502329, India.
  • D. Mallikarjuna Reddy Department of Mathematics, GITAM University, Hyderabad-502329, India.
  • V. Sumalatha Department of Statistics, Osmania University, Hyderabad-500007, India.
Keywords: Holt-Winters� additive, ARIMA, air traffic, time series analysis, forecasts

Abstract

In this chapter, Holt-Winters Additive model is fitted to the data regarding Domestic Air traffic in Air India flights. The investigation was done using dataset on number of passengers travelling by Air India domestic flights during January 2012 to November 2018. To prepare a tool to analyze the traffic flow monthly wise this helps Air India to revise their services. ARIMA model also has been fitted to the data, and compared with Holt-Winters Additive model. Finally, the results, findings and analysis proved that the Holt-Winters Additive model is superior to the ARIMA model for this data. This kind of analysis is very useful for forecasting the Air traffic.

Published
2020-06-11
How to Cite
Dingari, M., Reddy, D. M., & Sumalatha, V. (2020). Air Traffic Forecasting Using Time Series Models: New Perspectives. Emerging Trends in Engineering Research and Technology Vol. 4, 51-60. Retrieved from https://stm1.bookpi.org/index.php/etert-v4/article/view/1436
Issue
Emerging Trends in Engineering Research and Technology Vol. 4
Section
Chapters