Диссертация (1151123), страница 19
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P. Neural Networks in Business Forecasting. Georgia, USA: IdeaGroup Publishing, 2004. 350 p.135Приложение 1. Реализация алгоритма оцениванияхарактера одного внешнего события с использованиемпроцедуры auto-arima в пакете Rrm(list=ls(all=TRUE))data <- read.table("cars.csv", header=T, sep=";", dec=",")lenta <- data$saleslenta_ts <- ts(log(lenta), frequency=12, start=c(2007,1))acf_all<-acf(fit$residuals,plot=FALSE,lag.max=72)$acfacf_per<-acf_all[seq(1,length(acf_all),13)]fit<-arima(lenta_ts, order=c(0,1,1), seasonal=c(1,0,0))library(forecast)cwp <- function (object){## cwp <--> ``coefficients with p-values''#coef <- coef(object)if (length(coef) > 0) {mask <- object$masksdev <- sqrt(diag(vcov(object)))t.rat <- rep(NA, length(mask))t.rat[mask] <- coef[mask]/sdevpt <- 2 * pnorm(-abs(t.rat))setmp <- rep(NA, length(mask))setmp[mask] <- sdevsum <- rbind(coef, setmp, t.rat, pt)dimnames(sum) <- list(c("coef", "s.e.", "t ratio", "pvalue"),names(coef))return(sum)} else return(NA)}## The next step is so on to put into the function for theincrease of effect, and then decline## So a1 is for grow and a2 for slow down.interv_data<-rep(0,nrow(data))interv<-data.frame(row(as.matrix(interv_data)),interv_data)names(interv)<-c('n','var1')crisis_value<-21c1.zmin<-1000000000a1<-seq(0.05, 1, 0.05)a2<-seq(0.05, 1, 0.05)136for(k in crisis_value:nrow(interv)) {interv$var1<-0interv$var1[k:nrow(interv)]<-1for(i in a1) {interv$cb<-interv$var1interv$cb[crisis_value:interv$n[min(interv$n[interv$var1>0])]]<1-i*(min(interv$n[interv$var1>0])interv$n[crisis_value:interv$n[min(interv$n[interv$var1>0])]])/(interv$n[min(interv$n[interv$var1>0])]-crisis_value+1)for(j in a2) {interv$cb[interv$n>min(interv$n[interv$var1>0])]<-1j*(interv$n[interv$n>min(interv$n[interv$var1>0])]min(interv$n[interv$var1>0]))/(max(interv$n)min(interv$n[interv$var1>0])+1)res.1<-arima(lenta_ts,xreg=data.frame(interv$cb),order=c(0,1,0), seasonal=c(1,0,0))c1.z<sum(res.1$residuals*res.1$residuals)/length(res.1$residuals)if(c1.z<c1.zmin){c1.zmin<-c1.zc1.a_out<-ic2.a_out<-jmodelmin<-res.1$armamodel<-res.1interv_cc<-data.frame(interv$cb)}}}}c1.zminc1.a_outc2.a_outmodelminwrite.table(interv_cc,'interv_20130827_manual.csv',sep=";",dec=",")##Autointerv<-data.frame(row(as.matrix(data_new[,3])),data_new[,3])names(interv)<-c('n','var1')c1.zmin<-1000000000a1<-seq(0.05, 1, 0.05)a2<-seq(0.05, 1, 0.05)for(k in 34:nrow(interv)) {interv$var1<-0interv$var1[k:nrow(interv)]<-1for(i in a1) {interv$cb<-interv$var1interv$cb[34:interv$n[min(interv$n[interv$var1>0])]]<-1i*(min(interv$n[interv$var1>0])interv$n[34:interv$n[min(interv$n[interv$var1>0])]])/(interv$n[min(interv$n[interv$var1>0])]-34+1)137for(j in a2) {interv$cb[interv$n>min(interv$n[interv$var1>0])]<-1j*(interv$n[interv$n>min(interv$n[interv$var1>0])]min(interv$n[interv$var1>0]))/(max(interv$n)min(interv$n[interv$var1>0])+1)res.1<-auto.arima(lenta_ts, xreg=data.frame(interv$cb),max.p =4, max.q = 4,max.P = 4, max.Q = 4)c1.z<sum(res.1$residuals*res.1$residuals)/length(res.1$residuals)if(c1.z<c1.zmin){c1.zmin<-c1.zc1.a_out<-ic2.a_out<-jmodelmin<-res.1$armamodel<-res.1interv_cc<-data.frame(interv$cb)}}}}zminc1.a_outc2.a_outmodelminwrite.table(interv_cc,'interv_20130827_auto.csv',sep=";",dec=",")write.table(data_new,'lenta_10_interp.csv',sep=';',dec=',')138Приложение 2.
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