Research on Time Series Analysis for Long Memory Process of Air Traffic Using ARFIMA

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Research on Time Series Analysis for Long Memory Process of Air Traffic Using ARFIMA

August 21, 2020 Mathematics and Computer Science 0

In the present study, the time series models ARIMA and ARFIMA or FARIMA models have been fitted to Air
India domestic air passengers, which considered as self similarity and Long Range Dependence (LRD). In such
case ARFIMA model is expected to be superior to ARIMA. We fitted ARIMA and ARFIMA models to air
traffic data and compared. Then the best model has been identified using RMSE, MAE and MAPE values. This
model can be useful to analyze the air traffic flow and revise the services of Air India. The analysis was carried
out using time series data on number of passengers travelling by Air India domestic flights during January 2012
to December 2018.

Author(s) Details

Manohar Dingari

Department of Mathematics, School of Technology, GITAM University, Hyderabad-502329, India.

D. Mallikarjuna Reddy
Department of Mathematics, School of Technology, GITAM University, Hyderabad-502329, India.

V. Sumalatha
Department of Statistics, OSMANIA University, Hyderabad, India.

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