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Type 'q()' to quit R. > x <- c(66,66,66,76,34,66,66,66,66,66,44,44,66,87.5,66.000,66,66,65.5,65.5,88,42,88,88,64,88,88,88,63,110,85,88,108,88.023,88,66,44.5,88.5,88,108,66,85,66,66,110,83,66,83,44,83,105) > par10 = '0.1' > par9 = '3' > par8 = 'dumresult' > par7 = 'dum' > par6 = '12' > par5 = 'ZZZ' > par4 = 'NA' > par3 = 'NA' > par2 = 'Croston' > par1 = 'Input box' > par10 <- '0.1' > par9 <- '3' > par8 <- 'dumresult' > par7 <- 'dum' > par6 <- '12' > par5 <- 'ZZZ' > par4 <- 'NA' > par3 <- 'NA' > par2 <- 'Croston' > par1 <- 'Input box' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > if(par3!='NA') par3 <- as.numeric(par3) else par3 <- NA > if(par4!='NA') par4 <- as.numeric(par4) else par4 <- NA > par6 <- as.numeric(par6) #Seasonal Period > par9 <- as.numeric(par9) #Forecast Horizon > par10 <- as.numeric(par10) #Alpha > library(forecast) Loading required package: tseries Loading required package: quadprog Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric This is forecast 2.03 > if (par1 == 'CSV') { + xarr <- read.csv(file=paste('tmp/',par7,'.csv',sep=''),header=T) + numseries <- length(xarr[1,])-1 + n <- length(xarr[,1]) + nmh <- n - par9 + nmhp1 <- nmh + 1 + rarr <- array(NA,dim=c(n,numseries)) + farr <- array(NA,dim=c(n,numseries)) + parr <- array(NA,dim=c(numseries,8)) + colnames(parr) = list('ME','RMSE','MAE','MPE','MAPE','MASE','ACF1','TheilU') + for(i in 1:numseries) { + sindex <- i+1 + x <- xarr[,sindex] + if(par2=='Croston') { + if (i==1) m <- croston(x,alpha=par10) + if (i==1) mydemand <- m$model$demand[] + fit <- croston(x[1:nmh],h=par9,alpha=par10) + } + if(par2=='ARIMA') { + m <- auto.arima(ts(x,freq=par6),d=par3,D=par4) + mydemand <- forecast(m) + fit <- auto.arima(ts(x[1:nmh],freq=par6),d=par3,D=par4) + } + if(par2=='ETS') { + m <- ets(ts(x,freq=par6),model=par5) + mydemand <- forecast(m) + fit <- ets(ts(x[1:nmh],freq=par6),model=par5) + } + try(rarr[,i] <- mydemand$resid,silent=T) + try(farr[,i] <- mydemand$mean,silent=T) + if (par2!='Croston') parr[i,] <- accuracy(forecast(fit,par9),x[nmhp1:n]) + if (par2=='Croston') parr[i,] <- accuracy(fit,x[nmhp1:n]) + } + write.csv(farr,file=paste('tmp/',par8,'_f.csv',sep='')) + write.csv(rarr,file=paste('tmp/',par8,'_r.csv',sep='')) + write.csv(parr,file=paste('tmp/',par8,'_p.csv',sep='')) + } > if (par1 == 'Input box') { + numseries <- 1 + n <- length(x) + if(par2=='Croston') { + m <- croston(x) + mydemand <- m$model$demand[] + } + if(par2=='ARIMA') { + m <- auto.arima(ts(x,freq=par6),d=par3,D=par4) + mydemand <- forecast(m) + } + if(par2=='ETS') { + m <- ets(ts(x,freq=par6),model=par5) + mydemand <- forecast(m) + } + summary(m) + } Forecast method: Croston's method Model Information: $demand Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 51 80.05765 57.53778 102.5775 45.61648 114.4988 52 80.05765 57.42546 102.6898 45.44470 114.6706 53 80.05765 57.31369 102.8016 45.27377 114.8415 54 80.05765 57.20248 102.9128 45.10368 115.0116 55 80.05765 57.09180 103.0235 44.93441 115.1809 56 80.05765 56.98165 103.1336 44.76595 115.3493 57 80.05765 56.87202 103.2433 44.59829 115.5170 58 80.05765 56.76291 103.3524 44.43143 115.6839 59 80.05765 56.65431 103.4610 44.26534 115.8500 60 80.05765 56.54621 103.5691 44.10001 116.0153 $period Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 51 1 1 1 1 1 52 1 1 1 1 1 53 1 1 1 1 1 54 1 1 1 1 1 55 1 1 1 1 1 56 1 1 1 1 1 57 1 1 1 1 1 58 1 1 1 1 1 59 1 1 1 1 1 60 1 1 1 1 1 In-sample error measures: ME RMSE MAE MPE MAPE MASE 2.8689077 17.6271450 13.5846432 -2.3396654 20.1640662 0.7677595 Forecasts: Point Forecast 51 80.05765 52 80.05765 53 80.05765 54 80.05765 55 80.05765 56 80.05765 57 80.05765 58 80.05765 59 80.05765 60 80.05765 > postscript(file="/var/www/html/rcomp/tmp/16l6v1273750361.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,1)) > if (par2=='Croston') plot(m) > if ((par2=='ARIMA') | par2=='ETS') plot(forecast(m)) > plot(mydemand$resid,type='l',main='Residuals', ylab='residual value', xlab='time') > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2hc5y1273750361.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,2)) > acf(mydemand$resid, lag.max=n/3, main='Residual ACF', ylab='autocorrelation', xlab='time lag') > pacf(mydemand$resid,lag.max=n/3, main='Residual PACF', ylab='partial autocorrelation', xlab='time lag') > cpgram(mydemand$resid, main='Cumulative Periodogram of Residuals') > qqnorm(mydemand$resid); qqline(mydemand$resid, col=2) > par(op) > dev.off() null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Demand Forecast',6,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Point',header=TRUE) > a<-table.element(a,'Forecast',header=TRUE) > a<-table.element(a,'95% LB',header=TRUE) > a<-table.element(a,'80% LB',header=TRUE) > a<-table.element(a,'80% UB',header=TRUE) > a<-table.element(a,'95% UB',header=TRUE) > a<-table.row.end(a) > for (i in 1:length(mydemand$mean)) { + a<-table.row.start(a) + a<-table.element(a,i+n,header=TRUE) + a<-table.element(a,as.numeric(mydemand$mean[i])) + a<-table.element(a,as.numeric(mydemand$lower[i,2])) + a<-table.element(a,as.numeric(mydemand$lower[i,1])) + a<-table.element(a,as.numeric(mydemand$upper[i,1])) + a<-table.element(a,as.numeric(mydemand$upper[i,2])) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/3vm3p1273750361.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Actuals and Interpolation',3,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Time',header=TRUE) > a<-table.element(a,'Actual',header=TRUE) > a<-table.element(a,'Forecast',header=TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i] - as.numeric(m$resid[i])) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/4ov2r1273750361.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'What is next?',1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,hyperlink(paste('http://www.wessa.net/Patrick.Wessa/rwasp_demand_forecasting_simulate.wasp',sep=''),'Simulate Time Series','',target='')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,hyperlink(paste('http://www.wessa.net/Patrick.Wessa/rwasp_demand_forecasting_croston.wasp',sep=''),'Generate Forecasts','',target='')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,hyperlink(paste('http://www.wessa.net/Patrick.Wessa/rwasp_demand_forecasting_analysis.wasp',sep=''),'Forecast Analysis','',target='')) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/5ckc61273750361.tab") > try(system("convert tmp/16l6v1273750361.ps tmp/16l6v1273750361.png",intern=TRUE)) character(0) > try(system("convert tmp/2hc5y1273750361.ps tmp/2hc5y1273750361.png",intern=TRUE)) character(0) > > #-SERVER-wessa.org > > > > proc.time() user system elapsed 2.556 0.346 2.731