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IJSTR >> Volume 8 - Issue 10, October 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Time Series Analysis For Long Memory Process Of Air Traffic Using Arfima

[Full Text]

 

AUTHOR(S)

Manohar Dingari, D. Mallikarjuna Reddy, V. Sumalatha

 

KEYWORDS

Air Traffic, ARFIMA, ARIMA, Long Range dependence, Quality of Service, Self-Similarity and Time Series Models.

 

ABSTRACT

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.

 

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