本文關(guān)鍵詞：基于GRNN的變權(quán)重組合預(yù)測模型在傳染病發(fā)病率預(yù)測中的應(yīng)用，，由筆耕文化傳播整理發(fā)布。

基于GRNN的變權(quán)重組合預(yù)測模型在傳染病發(fā)病率預(yù)測中的應(yīng)用

基于GRNN的變權(quán)重組合預(yù)測模型在傳染病發(fā)病率預(yù)測中的應(yīng)用

影響傳染病發(fā)生發(fā)展的因素眾多且相互關(guān)系復(fù)雜,預(yù)測傳染病發(fā)生發(fā)展的模型也是種類繁多。目前對傳染病的預(yù)測策略可分為兩類：一類是線性回歸預(yù)測、時(shí)間序列淺析淺析、灰色模型、人工神經(jīng)網(wǎng)絡(luò)等單純預(yù)測模型,另一類是通過將兩種或多種預(yù)測模型以一定策略組合得到的組合預(yù)測模型,組合預(yù)測模型可以分成定權(quán)重組合預(yù)測模型和變權(quán)重組合預(yù)測模型兩種。研究目的運(yùn)用灰色模型(Grey Model,簡記為GM)、差分自回歸移動平均模型(Autoregressive Integrated Moving Average Model,簡記為ARIMA模型)為基礎(chǔ),構(gòu)建基于廣義回歸神經(jīng)網(wǎng)絡(luò)(Generalized Regression Neural Network,簡記為GRNN)的變權(quán)重組合預(yù)測模型,分別擬合傳染病發(fā)病率的情況,對基于GRNN的變權(quán)重組合預(yù)測模型的擬合效果進(jìn)行評價(jià),提出模型的優(yōu)越性和不足,為變權(quán)重組合預(yù)測模型的研究提供依據(jù)。資料和策略本研究以浙中某市1998-2008年的肺結(jié)核發(fā)病率為研究資料,分別在matlab7.11.0軟件和SAS9.2軟件中構(gòu)建了灰色模型和ARIMA模型,通過預(yù)測2009年的發(fā)病率來比較具體模型的精度,并以這兩種模型為基礎(chǔ),在matlab軟件中構(gòu)建了基于GRNN預(yù)測算法的變權(quán)重組合預(yù)測模型,以簡單平均組合預(yù)測模型、加權(quán)平均組合預(yù)測模型為對照,來評價(jià)變權(quán)重組合預(yù)測模型在預(yù)測中的精度。主要結(jié)果1.灰色模型利用浙中某市1998-2008年肺結(jié)核月發(fā)病率數(shù)據(jù)構(gòu)建了GM(1,1)模型和殘差修正GM(1,1)模型,并對模型進(jìn)行評估,發(fā)現(xiàn)GM(1,1)模型和殘差修正GM(1,1)模型的后驗(yàn)差比值C分別是0.7687和0.6187.結(jié)果顯示殘差修正GM(1,1)模型各項(xiàng)指標(biāo)較小,認(rèn)為殘差修正GM(1,1)模型的擬合效果優(yōu)于GM(1,1)模型。2.ARIMA模型利用浙中某市1998-2008年肺結(jié)核月發(fā)病率數(shù)據(jù)構(gòu)建了ARIMA(1,0,0)模型和ARIMA(1,0,1)*(1,1,0)12模型,兩個(gè)模型的殘差值白噪聲檢驗(yàn)顯示：在延遲12階后,ARIMA(1,0,1)*(1,1,0)12模型有統(tǒng)計(jì)學(xué)意 義,而ARIMA(1,0,0)模型則無統(tǒng)計(jì)學(xué)意 義,且前者的AIC值為627.6154,SBC值為630.4982；后者的AIC值為587.4054,SBC值為595.7679,認(rèn)為后者的擬合效果優(yōu)于前者。故選擇ARIMA(1,0,1)*(1,1,0)12模型來建立組合預(yù)測模型。3.組合預(yù)測模型以灰色模型和ARIMA模型為基礎(chǔ),構(gòu)建了基于GRNN的組合預(yù)測模型,將此模型和灰色模型、ARIMA模型、簡單平均組合預(yù)測模型和加權(quán)平均組合預(yù)測模型比較,發(fā)現(xiàn)殘差修正GM(1,1)模型的MSE=37.451,MAE=5.692, MAPE=53.69％,MER=48.51％；ARIMA(1,0,1).(1,1,0)12模型的MSE=18.509,MAE=3.761,MAPE=35.13％,MER=32.05％；簡單平均組合預(yù)測模型的MSE=28.984,MAE=4.736,MAPE=45.4％,MER=40.4％；加權(quán)平均組合預(yù)測模型的MSE=24.649,MAE=4.274,MAPE=41.0％,MER=36.4％；基于GRNN的組合預(yù)測模型的MSE=9.961,MAE=2.571,MAPE=25.6％,MER=21.9％；各項(xiàng)評價(jià)指標(biāo)都滿足：基于GRNN的組杏預(yù)測模型

【Abstract】 The numerous influence Factors of infectious diseases has complex relationships with each others, so there are various kinds of forecasting model for infectious diseases. There are two kinds of forecasting model:one kind of them are called simple prediction model such as linear regression model, time series models, the gray models, artificial neural network models and so on; the others are called combination forecast model which are comprised of more than one kind of simple prediction models with different ways. The combination forecast model can be divided into fixed weight combination forecasting model and the variable weight combination forecasting model.ObjectiveA forecasting model with variable weight combination based on GRNN was built with the time series models and the gray models for for Infectious Diseases. To provide evidence for variable weight combination, these forecasting models was used to predict the incidence rate of the diseases and evaluated to study the advantages and the weakness of them. Data and MethodsThe monthly incidence rate of tuberculosis between 1998 and 2008 were collected from the Center for Disease Control and Prevention in yiwu.With these data, the grey models and the time series models were built with the software called matlab 7.11.0 and SAS 9.2, respectively. Then these models were used to predict he monthly incidence rate of tuberculosis and evaluated which one is better.A forecasting model with variable weight combination based on GRNN for infectious diseases was made up of the grey models and the time series models in the matlab7.11.0. A simple combination forecasting model and a weighted average combined forecasting model were made up of the grey models and the time series models, respectively. The estimation accuracy of the forecasting model based on GRNN was evaluated by comparing with the other two combination forecasting models.Results1. The grey modelsA GM(1,1) model and a residual-modifying GM(1,1) model were built with the monthly incidence rate of tuberculosis between 1998 and 2008 of yiwu. The value of posterior-variance-test of the GM(1,1) model and the residual-modifying GM(1,1) model was 0.7687,0.4140,respectively. The evaluation index of the residual-modifying GM(1,1) model were smaller than the evaluation index of the GM(1,1) model, so the former have better estimation accuracy.2. The ARIMA modelsAn ARIMA(1,0,0) model and an ARIMA(1,O,1)* (1,1,0)12 model were built with the monthly incidence rate of tuberculosis between 1998 and 2008 of yiwu. The white noise test shows:the residual value of the ARIMA(1,0,1)* (1,1,0)12 model was a white noise sequence after 12 lags, but the residual value of the ARIMA(1,0,0) model was not a white noise sequence after 12 lags. The AIC value and the SBC value of former model was 587.4054,595.7679, The AIC value and the SBC value of later model was 587.4054,595.7679, respectively. The evaluation index of the ARIMA(1,0,1)* (1,1,0)12 model were smaller than the ARIMA(1,0,0) model, so the former have better estimation accuracy.3. The combination forecasting modelA forecasting model with variable weight combination based on GRNN for monthly incidence rate of tuberculosis was made up of the grey models and the ARIMA models. A simple combination forecasting model and a weighted average combined forecasting model were made up of the two simple models. The combination forecasting model based on GRNN was compared with the other four models by comparing the MSE value, the MAE value, the MAPE value and the MER value, the results were as follows:the four values of the residual-modifying GM(1,1) model was 37.451,5.692,53.69%,48.51%, respectively; the four values of the ARIMA(1,0,1)* (1,1,0)12 model was 18.509,3.761,35.13%,32.05%, respectively; the four values of the simple combination forecasting model was 28.984,4.736,45.4%,40.4%, the four values of the weighted average combined forecasting model was 24.649,4.274,41.0%, 36.4%, respectively; the four values of the combination forecasting model based on GRNN was 9.961,2.571,25.6%,21.9%, respectively. The evaluation index of the models showed that the values of the five models are the combination forecasting model based on GRNN< the ARIMA(1,0,1)*(1,1,0)12 model< the weighted average combined forecasting model

  本文關(guān)鍵詞：基于GRNN的變權(quán)重組合預(yù)測模型在傳染病發(fā)病率預(yù)測中的應(yīng)用，由筆耕文化傳播整理發(fā)布。