Volume 6, Issue 3, May 2017, Page: 161-169
Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing
Caspah Lidiema, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Received: Apr. 5, 2017;       Accepted: Apr. 13, 2017;       Published: May 25, 2017
DOI: 10.11648/j.ajtas.20170603.15      View  2436      Downloads  139
In this paper, two models of forecasting are used the Box-Jenkins procedure employing the SARIMA and the Holt-Winters triple exponential smoothing. Published Consumer Price Index Data from Kenya National Bureau of Statistics (KNBS) for the period November 2011 to October 2016 was used. This paper we equate the forecasted values of both the models and we choose the best model based on the least mean Absolute square error (MASE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The three step model building for Box-Jenkins was first employed, followed by the Hold-Winters triple exponential smoothing. The study found the SARIMA Model was a better model than the Holt-winters triple exponential smoothing as per the obtained results using MASE, MAE and MAPE.
Inflation, CPI, Holt-Winters, Triple Exponential Smoothing, ARIMA, SARIMA
To cite this article
Caspah Lidiema, Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing, American Journal of Theoretical and Applied Statistics. Vol. 6, No. 3, 2017, pp. 161-169. doi: 10.11648/j.ajtas.20170603.15
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