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Comparative Analysis of Sarima and Setar Models in Predicting Pneumonia Cases in Kenya
International Journal of Data Science and Analysis
Volume 6, Issue 1, February 2020, Pages: 48-57
Received: Feb. 24, 2020; Accepted: Mar. 6, 2020; Published: Mar. 18, 2020
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Fredrick Agwata Nyamato, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Thomas Mageto, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
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Kenya is a country located in Eastern part of Africa with approximate population of 46.5 million, with majority of the population constituting youths under the age of 35 years. The country has experienced increased morbidity rate arising from Pneumonia disease like other countries all over the world. As per recent studies 2 million children lose lives from pneumonia disease [1]. This study applies two models, one is linear model Seasonal autoregressive model (SARIMA) and another is a non-linear model called self-Excited Threshold Autoregressive (SETAR) in projection of cases in Kenya. Data for usage for purpose of this study was obtained Ministry of Health of Kenya of a period of 20 years from January 1999 to December 2018. The data collected is seasonal the number of case from period to period depending on climatic condition. Although both models performs well in pneumonia projection, non-linear SETAR models outperforms linear SARIMA. By carrying out a comparative analysis by use of Diebold-Mariano test, which revealed that there were no significant difference in the forecasting performance of the two models. The best model identified between the two models i.e. SETAR which best fit the data, can be applied in predicting pneumonia cases beyond the period under consideration. Other studies can be carried to come up with a model for every specific region in the country, to assist in resources allocation to specific parts of the country.
Seasonal Autoregressive Integrated Moving Average, Self-excited Threshold Autoregressive, Stationarity and Linearity
To cite this article
Fredrick Agwata Nyamato, Anthony Wanjoya, Thomas Mageto, Comparative Analysis of Sarima and Setar Models in Predicting Pneumonia Cases in Kenya, International Journal of Data Science and Analysis. Vol. 6, No. 1, 2020, pp. 48-57. doi: 10.11648/j.ijdsa.20200601.16
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Black, R., Sazawal, S., Clad, W., (2003). Effect of pneumonia case management on mortality in neonates, infants, and preschool children: a meta-analysis of community-based trials Lancet Infectious Diseases, 3: 547-556.
Zar, H. J., Madhi, S. A., Aston, S. J., Gordon, S. B., (2013). Pneumonia in low and middle income countries: progress and challenges, Thorax, 68: 1052–1056.
WHO (2013). Report on Child Health. Accessed date: 01/26/13.
Dickey, D. A., and W. A. Fuller. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74: 427–431.
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis. New Jersey: Prentice Hall.
Tong, H., (1978). On a threshold model. In Pattern Recognition and Signal Processing, edited by Chen, C. H. Amsterdam: Kluwer.
J. Tong, H., and Lim, K. S. L., (1980). Threshold autoregression limit cycles and cyclical data. Journal of the Royal Statistical Society Series, B 42: 245-292.
Tong, H., (1983). Threshold Models in Non-linear Time Series Analysis, Springer: New York.
Tong, H., (1990). Non-linear Time Series: A Dynamical Systems Approach, Oxford: Oxford University Press.
Boero, G., and Marrocu, E., (2004). The performance of SETAR models: A regime conditional evaluation of point, interval and density forecasts. International Journal of Forecasting, 20: 305-320.
Frances, H. P., and Van Dijk, D., (2000). Nonlinear Time Series in Empirical Finance. Cambridge: Cambridge University Press.
. Keenan, D. M., (1985). A Tukey non-additivity-type test for Time Series Nonlinearity, Biometrika, 72: 39-44.
Ramsey, J. B., (1969). Tests for Specification Errors in Classical Linear Least Squares Regression Analysis. Journal of the Royal Statistical Society Series B 31: 350–371.
Cryer, J. D., and Chan, K. S., (2008). Time Series Analysis with Applications in R. 2 EdSpringer Science+Business Media, LLC, NY, USA.
Box, G. E. P., a n d Jenkins, G. M., (1976). Time Series Analysis Forecasting and Control. Holden – Day, San-Francisco.
J. M. Kihoro, R. O. Otieno, C. Wafula, (2004), “Seasonal Time Series Forecasting: A Comparative Study of ARIMA and ANN Models”, African Journal of Science and Technology (AJST) Science and Engineering Series Vol. 5, No. 2, pages: 41-49.
Durbin, J. (1960). The fitting of time series models. Review of the International Institute of Statistics 28: 233-244.
Wei, William, W. S. (2006). Time series analysis: Univariate and Multivariate Methods 2nd ed. Pearson Addison Wesley.
Ljung, G. M., and Box. G. E. P., (1978). On a Measure of Lack of Fit in Time Series Models. Biometrika, 65: 297–303.
G. P. Zhang, (2003), “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50, pages: 159–175.
Diebold, F. X., and Mariano, R., S., (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13: 253–263.
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