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Type 'q()' to quit R. > x <- c(78.1,66.7,79,65.2,66.5,77.2,80.2,77.9,78,86.8,92.9,185.8,91,79.1,84.2,70.1,71.3,79.6,92.3,78.7,82.5,98.2,115.4,205.6,94,83.2,80.3,70.4,71.1,78.8,86.3,77.5,80.1,89.8,99.9,218,85.4,77.5,78.6,68.8,64.8,79.8,94.3,79.9,87.5,99.1,109.9,273.6,91.3,80.6,80.4,71.8,75.5,86.6,91.5,86.8,84.6,88.6,102.1,260.3,79,70.6,79.3,66.8,61.2,72.5,83.5,75.8,83.4,89.4,104.9,251.6,80,76.3,81.1,63.1,63.5,78.8,91.7,83.8,83.8,95.8,108.9,258.2,88.7,79.5,74.3,70.5,59.1,73.2,81.2,75,74.6,89.5,107,246.4,83.6,72.1,68.7,60.1,61.1,72.7,85.3,71.4,75.2,89.8,100.9,222.7) > par3 = 'additive' > par2 = 'Triple' > par1 = '12' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: Wessa P., (2010), Exponential Smoothing (v1.0.4) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_exponentialsmoothing.wasp/ > #Source of accompanying publication: > #Technical description: > par1 <- as.numeric(par1) > if (par2 == 'Single') K <- 1 > if (par2 == 'Double') K <- 2 > if (par2 == 'Triple') K <- par1 > nx <- length(x) > nxmK <- nx - K > x <- ts(x, frequency = par1) > if (par2 == 'Single') fit <- HoltWinters(x, gamma=F, beta=F) > if (par2 == 'Double') fit <- HoltWinters(x, gamma=F) > if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3) > fit Holt-Winters exponential smoothing with trend and additive seasonal component. Call: HoltWinters(x = x, seasonal = par3) Smoothing parameters: alpha: 0.2026689 beta : 0.03614374 gamma: 1 Coefficients: [,1] a 91.0671787 b -0.2430951 s1 -12.7635615 s2 -24.2000398 s3 -27.5071980 s4 -35.2835439 s5 -35.8360278 s6 -24.7683001 s7 -13.1797225 s8 -26.1539777 s9 -22.2257336 s10 -7.4321756 s11 4.9543511 s12 131.6328213 > myresid <- x - fit$fitted[,'xhat'] > postscript(file="/var/www/wessaorg/rcomp/tmp/106mm1295083321.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,1)) > plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing') > plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors') > par(op) > dev.off() null device 1 > postscript(file="/var/www/wessaorg/rcomp/tmp/27r2d1295083321.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > p <- predict(fit, par1, prediction.interval=TRUE) > np <- length(p[,1]) > plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing') > dev.off() null device 1 > postscript(file="/var/www/wessaorg/rcomp/tmp/3be2h1295083321.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF') > spectrum(myresid,main='Residals Periodogram') > cpgram(myresid,main='Residal Cumulative Periodogram') > qqnorm(myresid,main='Residual Normal QQ Plot') > qqline(myresid) > par(op) > dev.off() null device 1 > > #Note: the /var/www/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'Value',header=TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'alpha',header=TRUE) > a<-table.element(a,fit$alpha) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'beta',header=TRUE) > a<-table.element(a,fit$beta) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'gamma',header=TRUE) > a<-table.element(a,fit$gamma) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/wessaorg/rcomp/tmp/46q0n1295083321.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Interpolation Forecasts of Exponential Smoothing',4,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'t',header=TRUE) > a<-table.element(a,'Observed',header=TRUE) > a<-table.element(a,'Fitted',header=TRUE) > a<-table.element(a,'Residuals',header=TRUE) > a<-table.row.end(a) > for (i in 1:nxmK) { + a<-table.row.start(a) + a<-table.element(a,i+K,header=TRUE) + a<-table.element(a,x[i+K]) + a<-table.element(a,fit$fitted[i,'xhat']) + a<-table.element(a,myresid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/wessaorg/rcomp/tmp/5z5k61295083321.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Extrapolation Forecasts of Exponential Smoothing',4,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'t',header=TRUE) > a<-table.element(a,'Forecast',header=TRUE) > a<-table.element(a,'95% Lower Bound',header=TRUE) > a<-table.element(a,'95% Upper Bound',header=TRUE) > a<-table.row.end(a) > for (i in 1:np) { + a<-table.row.start(a) + a<-table.element(a,nx+i,header=TRUE) + a<-table.element(a,p[i,'fit']) + a<-table.element(a,p[i,'lwr']) + a<-table.element(a,p[i,'upr']) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/wessaorg/rcomp/tmp/677561295083321.tab") > > try(system("convert tmp/106mm1295083321.ps tmp/106mm1295083321.png",intern=TRUE)) character(0) > try(system("convert tmp/27r2d1295083321.ps tmp/27r2d1295083321.png",intern=TRUE)) character(0) > try(system("convert tmp/3be2h1295083321.ps tmp/3be2h1295083321.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 1.150 0.140 1.343