A Bayesian Time Series Analysis of Annual Inflation Growth Percent in Egypt

Sherif S. Ali; Emad E. A. Soliman

doi:10.64060/JASRv2i42

Authors

Sherif S. Ali Department of Statistics, King Abdulaziz University, Jeddah 21589, Saudi Arabia Author https://orcid.org/0000-0002-1295-232X
Emad E. A. Soliman Department of Statistics, King Abdulaziz University, Jeddah 21589, Saudi Arabia Author https://orcid.org/0000-0001-5001-9330

DOI:

https://doi.org/10.64060/JASRv2i42

Keywords:

Autoregressive moving average processes, Bayesian time series analysis, Inflation rates, Mean absolute error, Mean absolute percentage error

Abstract

The current manuscript aims to employ the Bayesian approach to analyze a real data time series in order to understand its functional form and give accurate forecasts for its future observations. The time series considered in the study is the data set of annual inflation growth percent in Egypt during the period from 1960 to 2025. The series is modeled and its observations are predicted using a Bayesian approach. Some accuracy measures are used such as Root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results showed that, ARIMA(1,1,1) model is fitted by the proposed Bayesian technique and passes all the diagnostic checking tests and can be used efficiently in the forecasting process. The model was used to give point and interval forecasts for the succeeding three years.

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