Air Traffic Forecasting using Time Series Models
Manohar Dingari1, D. Mallikarjuna Reddy2, V. Sumalatha3
1Manohar Dingari, Research Scholor, Department of mathematics, GITAM University, Hyderabad, India.
2Dr.D. Mallikarjuna Reddy, Asst Professor, Department of mathematics, GITAM University, Hyderabad, India.
3V.Sumalatha, Research Scholor, Department of Statistics, OSMANIA University, Hyderabad, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1061-1065 | Volume-8 Issue-4, November 2019. | Retrieval Number: C6479098319/2019©BEIESP | DOI: 10.35940/ijrte.C6479.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, 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.
Keywords: Holt-Winters’ Additive, ARIMA, Air Traffic, Time series analysis, Forecasts.
Scope of the Article: Network Traffic Characterization and Measurements.