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Forecasting Diabetes Patients Attendance at Al-Baha Hospitals Using Autoregressive Fractional Integrated Moving Average (ArFIMA) Models

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Journal of Data Analysis and Information Processing 2020年第3期8卷 183-194页

作者： salem Al Zahrani Fath Al rahman Al sameeh Abdulaziz C. M. Musa Ashaikh A. A. shokeralla Department of Mathematics College of Science and Arts Al-Baha University Al-Mandag KSA Department of Mathematics College of Science & Arts Al-Baha University Baljursy KSA Department of statistics & Mathematics College of Mathematical Sciences & Statistics Al-Neelain University Khartoum Sudan Department of Mathematics College of Science and Arts Al-Baha University Al-Makhwah KSA

Diabetes has become a concern in the developed and developing countries with its growing number of patients reported to the ministry of health records. This paper discusses the use of the Autoregressive Fractional Moving Average (ArFIMA) technique to modeling the diabetes patient’s attendance at Al-Baha hospitals using monthly time series data. The data used in the analysis of this paper are monthly readings of diabetes patients data covered the period January 2006-December 2016. The data were collected from the General Directorate of Health Affairs, Al-Baha region. The autoregressive fractional moving average approach was applied to the data through the model identification, estimation, diagnostic checking and forecasting. Hurst test results and ACF confirmed that there is a long memory behavior in diabetic patient’s data. Also, the fractional difference to diabetes series data revealed that (d = 0.44). Moreover, unit root tests indicated that the fractional difference of diabetes series level is stationary. Furthermore, according to AIC and BIC of model selection criteria ArFIMA (1, 0.44, 0) model shown the smallest values, hence this model was chosen as an adequate represents the data. Also, a diagnostic check confirmed that ArFIMA was appropriate and highly recommended in modeling and forecasting this type of data.

关键词： Long Memory ArFIMA rescaled range r/s method Diabetes Patients

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