Application of Autoregressive Integrated Moving Average Model and Weighted Markov Chain on Forecasting Under-Five Mortality Rates in Nigeria (Published)
The aim of this study is to obtain an optimal model between the traditional time series model (ARIMA) and Weighted Markov Chain. The historical dataset of U5MR in Nigeria from 1980-2019 is obtained from the official website of World Bank. ARIMA modeling involved differencing of the data to attain stationarity, while WMC involved classification of the datasets into clusters using k-means cluster analysis and transition of states. Two performance measures Theil’s U Statistic and MAPE are used to evaluate the two models based on in-sample and out-sample. The results shows that ARIMA(0,3,2) is a better model to forecast U5MR in Nigeria.
Citation: Ugoh C.I., Osuji G.A., Nwankwo C.H., Nwabueze N.C., Eze T.C., Orji G.O. (2022) Application of Autoregressive Integrated Moving Average Model and Weighted Markov Chain on Forecasting Under-Five Mortality Rates in Nigeria, European Journal of Statistics and Probability, Vol.10, No.2, pp., 39-53,
Keywords: ARIMA, K-Mean Cluster, MAPE, Theil’s U Statistic, U5MR, Weighted Markov Chain (WMC)
Application of SARIMA Model and Simple Seasonal Exponential Smoothing on Diabetes Mellitus: A Case of Enugu State Teaching Hospital, Nigeria (Published)
This paper aims at obtaining better model between seasonal ARIMA and simple seasonal exponential smoothing that will be used for forecasting number of diabetes patients in a given hospital. Monthly dataset from January 2009 to December 2019 from Enugu State Teaching Hospital was used for this research. Seasonal ARIMA was modelled using the techniques of Box-Jenkins, and simple seasonal exponential smoothing modelled using the least squares method. Bayesian Information Criterion (BIC) was employed to obtain the best seasonal ARIMA model, while the Theil’s U statistics and MAPE were used to obtain the best forecast model. ARIMA (1,1,2)(0,0,0)12 was selected as the best SARIMA model with the BIC of 7.873, and simple seasonal exponential smoothing was considered the best forecast model with a Theil’s U Statistic of 0.11241 and MAPE of 23.450. The fitted model was used to make out-sample forecast for the period January 2020-December 2025. The fitted model in this findings will help Enugu state government to plan efficiently, expand public sensitization, and allocate adequate resources for emergencies.
Citation: Christogonus Ifeanyichukwu Ugoh, Anyadiegwu Chinelo Ujunwa, Thomas Chinwe Urama, Ugwu Gibson Chiazortam (2022) Application of SARIMA Model and Simple Seasonal Exponential Smoothing on Diabetes Mellitus: A Case of Enugu State Teaching Hospital, Nigeria, European Journal of Statistics and Probability, Vol.10, No.1, pp., 21-32
Keywords: BIC, Diabetes Mellitus, MAPE, SARIMA, Simple Seasonal Exponential Smoothing, Theil’s U Statistic