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[Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever].
Zhonghua Liu Xing Bing Xue Za Zhi. 2009 Sep; 30(9):964-8.ZL

Abstract

Application of the 'single auto regressive integrated moving average (ARIMA) model' and the 'ARIMA-generalized regression neural network (GRNN) combination model' in the research of the incidence of scarlet fever. Establish the auto regressive integrated moving average model based on the data of the monthly incidence on scarlet fever of one city, from 2000 to 2006. The fitting values of the ARIMA model was used as input of the GRNN, and the actual values were used as output of the GRNN. After training the GRNN, the effect of the single ARIMA model and the ARIMA-GRNN combination model was then compared. The mean error rate (MER) of the single ARIMA model and the ARIMA-GRNN combination model were 31.6%, 28.7% respectively and the determination coefficient (R(2)) of the two models were 0.801, 0.872 respectively. The fitting efficacy of the ARIMA-GRNN combination model was better than the single ARIMA, which had practical value in the research on time series data such as the incidence of scarlet fever.

Authors+Show Affiliations

Department of Epidemiology and Health Statistics, Anhui Medical University, Hefei 230032, China.No affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article

Language

chi

PubMed ID

20193238

Citation

Zhu, Yu, et al. "[Comparison of Predictive Effect Between the Single Auto Regressive Integrated Moving Average (ARIMA) Model and the ARIMA-generalized Regression Neural Network (GRNN) Combination Model On the Incidence of Scarlet Fever]." Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi, vol. 30, no. 9, 2009, pp. 964-8.
Zhu Y, Xia JL, Wang J. [Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever]. Zhonghua Liu Xing Bing Xue Za Zhi. 2009;30(9):964-8.
Zhu, Y., Xia, J. L., & Wang, J. (2009). [Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever]. Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi, 30(9), 964-8.
Zhu Y, Xia JL, Wang J. [Comparison of Predictive Effect Between the Single Auto Regressive Integrated Moving Average (ARIMA) Model and the ARIMA-generalized Regression Neural Network (GRNN) Combination Model On the Incidence of Scarlet Fever]. Zhonghua Liu Xing Bing Xue Za Zhi. 2009;30(9):964-8. PubMed PMID: 20193238.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - [Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever]. AU - Zhu,Yu, AU - Xia,Jie-lai, AU - Wang,Jing, PY - 2010/3/3/entrez PY - 2010/3/3/pubmed PY - 2010/5/29/medline SP - 964 EP - 8 JF - Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi JO - Zhonghua Liu Xing Bing Xue Za Zhi VL - 30 IS - 9 N2 - Application of the 'single auto regressive integrated moving average (ARIMA) model' and the 'ARIMA-generalized regression neural network (GRNN) combination model' in the research of the incidence of scarlet fever. Establish the auto regressive integrated moving average model based on the data of the monthly incidence on scarlet fever of one city, from 2000 to 2006. The fitting values of the ARIMA model was used as input of the GRNN, and the actual values were used as output of the GRNN. After training the GRNN, the effect of the single ARIMA model and the ARIMA-GRNN combination model was then compared. The mean error rate (MER) of the single ARIMA model and the ARIMA-GRNN combination model were 31.6%, 28.7% respectively and the determination coefficient (R(2)) of the two models were 0.801, 0.872 respectively. The fitting efficacy of the ARIMA-GRNN combination model was better than the single ARIMA, which had practical value in the research on time series data such as the incidence of scarlet fever. SN - 0254-6450 UR - https://www.unboundmedicine.com/medline/citation/20193238/[Comparison_of_predictive_effect_between_the_single_auto_regressive_integrated_moving_average__ARIMA__model_and_the_ARIMA_generalized_regression_neural_network__GRNN__combination_model_on_the_incidence_of_scarlet_fever]_ L2 - http://journal.yiigle.com/LinkIn.do?linkin_type=pubmed&issn=0254-6450&year=2009&vol=30&issue=9&fpage=964 DB - PRIME DP - Unbound Medicine ER -