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Forecasting the numbers of cardiac diseases patients by using Box-Jenkins model in time series analysis | ||
International Journal of Nonlinear Analysis and Applications | ||
مقاله 139، دوره 13، شماره 1، خرداد 2022، صفحه 1673-1681 اصل مقاله (377.67 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5783 | ||
نویسنده | ||
Sabah Hasan Jasim Alsaedi* | ||
Mathematic Department - College of Basic Education, Misan University, Iraq | ||
تاریخ دریافت: 12 اردیبهشت 1400، تاریخ بازنگری: 05 مرداد 1400، تاریخ پذیرش: 08 آبان 1400 | ||
چکیده | ||
The aim of this study is analysis time series with using (Box and Jenkins ) method by identification , estimation, diagnosis, checking of model ,forecasting to find the beast forecasting model to the number of patient with cardiac in Misan province by using the monthly data of the period (2005-2016) by using SPSS version (26).The result of data analysis show that the proper and suitable model is Autoregression of order ARIMA (1,1,0) .According to this model the study forecast the numbers of patients with cardiac diseases the next years in monthly , so the forecasting values represented the scours time series data that deal to the efficiency of the model. | ||
کلیدواژهها | ||
Forecasting؛ Cardiac diseases؛ Box Jenkins؛ Time series analysis | ||
مراجع | ||
[1] Cardiovascular Disease Foundation. What is Cardiovascular Disease?, Available athttp://www.cvdf.org/. Date Accessed: September 19, 2011. [2] National Heart, Lung, and Blood Institute. What Is Heart Disease?, Available at https://www.nhlbi.nih.gov/ health/health-topics/topics/hdw/. Date Accessed: September 19, 2011. [3] P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods (2nd ed.). New York: Springer-Verlag,1991. [4] J. Crosbie and C. F. Sharpley, DMITSA: A simplified interrupted time-series analysis program. Behavior Research Methods, Instrum. Comput., 21(6) (1989) 639-642. [5] Bowerman, L. Bruce, R. T. O’Connell and Anne B. Koehler, Forecasting, Time Series, and Regression, 4th ed. Belmont, CA: Thomson Brooks/Cole, 2005. [6] R. l. Anderson, Distribution of the series Analysis Correlation Coefficient, Ann, Mat. Statistic, 13(1942) 113-129. [7] R. Kaiser and A. Maravall, Notes on Time Series Analysis ARIMA Models and Signal Extraction, Benco de Esponaservicio Estudios, 2001. [8] R. H. SHUMWAY, Applied Statistical Time Series Analysis, First Edition, Prentice Hall New Jersey, USA, 1998, P.537. [9] G.E.P Box and G.M. Jenkins, Time Series Analysis Forecasting and control, Holden-Day, London, 1976. [10] R. Kaiser and A. Maravall, Notes on Time series Analysis, ARIMA models and signal Extraction, Banco de Espana. Servicio de Estudios, 2001. | ||
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