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  2274      Downloads  121
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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Otu A. O et al (2014) Application of Sarima Models in Modelling and Forecasting Nigeria’s Inflation Rates. American Journal of Applied Mathematics and Statistics; 2(1): 16-28. doi: 10.12691/ajams-2-1-4.
Hyndman R. J. and Koehler A. B. (2006), "Another look at measures of forecast accuracy," International Journal of Forecasting, vol. 22, no. 4, pg. 679-688.
Virginia Gathingi, 2014. modelling in ation in Kenya using ARIMA and VAR, Project to the School of Mathematics, University of Nairobi.
Ingabire. J and Mung’atu. J. K. (2016) Measuring the Performance of Autoregressive Integrated Moving Average and Vector Autoregressive Models in Forecasting Inflation Rate in Rwanda, International Journal of Mathematics and Physical Sciences Research ISSN 2348-5736 (Online) Vol. 4, Issue 1, pp: (15-25), Month: April 2016 - September 2016, Available at: www.researchpublish.com.
Tim C and Dongkoo C, (1999), Modelling and Forecasting inflation in India, IMF Working Paper, WP/99/119.
Uwilingiyimana C. et al (2015), Forecasting Inflation in Kenya Using Arima - Garch Models, International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 3, Issue 2, pp: (15-27), Month: October 2015 - March 2016, Available at: www.researchpublish.com.
Jere, S. and Siyanga, M. (2016) Forecasting Inflation Rate of Zambia Using Holt’s Exponential.
Hamidreza M. and Leila S. (2012). Using SARFIMA model to study and predict the Iran’s oil supply. International Journal of Energy Economics and Policy. Vol. 2, No. 1, 2012, pp. 41-49.
Puthran D. et al (2014). Comparing SARIMA and Holt-Winters forecasting accuracy with respect to Indian motorcycle industry, Transactions on Engineering and Sciences, Vol. 2, Issue 5, May 2014, ISSN: 2347-1964 Online 2347-1875 Print.
Udom. P and Phumchusri. N (2014). A comparison study between time series model and ARIMA model for sales forecasting of distributor in plastic industry, IOSR Journal of Engineering (IOSRJEN), Vol. 04, Issue 02 (February. 2014), V1, PP 32-38 ISSN (e): 2250-3021, ISSN (p): 2278-8719.
Box G. E. et al(2008),"Time Series Analysis Forecasting and Control, Fourth Edition", John Wiley & Sons, Inc., Hoboken, New Jersey.
Chatfield, C. (2000), "Time Series Forecasting", Chapman & Hall.
Shumway R. H and Stoffer D. S (2006), "Time Series Analysis and Its Applications With R Examples Second Edition" Springer Science+Business Media, LLC Smoothing.
Hyndman et al (2008) "Forecasting with Exponential Smoothing, The State Space Approach", Springer-Verlag Berlin Heidelberg.
Hyndman et al (2002) A state space framework for automatic forecasting using exponential smoothing methods, International Journal of Forecasting.
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