Abstract
In the study we model and forecast South African airline passengers, using yearly time series data from 1970 to 2019. Box-Jenkins ARIMA methodology is used to forecast airline passenger data for the next five years (2020–2024). The raw data used was not stationary and as a result, was differenced once to make it stationary. In this study, an ARIMA (3,1,2) model was proposed. In addition, diagnostic tests demonstrated that the proposed model is, in fact, adequate and could be utilised to make predictions on the number of passengers for the South African airline from 2020 to 2024.
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Abbreviations
- AFC:
-
Autocorrelation Function
- AIC:
-
Akaike’s Information Criterion
- AR:
-
Autoregression
- ARIMA:
-
Autoregressive Integrated Moving Average
- ESACF:
-
Extended Sample Autocorrelation Function
- GDP:
-
Gross Domestic Product
- I:
-
Integrated
- MA:
-
Average
- PACF:
-
Partial Autocorrelation Function
- Q-Q:
-
Quantile-Quantile plot
- SAA:
-
South African Airline
- SBC:
-
Schwarz’s Bayesian Criterion
- TBATS:
-
Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trends, Seasonal components
- WDI:
-
World Development Indicators
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Maruping, T.T., Seaketso, P., Mdlongwa, P., Munapo, E. (2023). Modelling and Forecasting South African Airline Passengers Using ARIMA. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-031-50151-7_15
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DOI: https://doi.org/10.1007/978-3-031-50151-7_15
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