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Type 'q()' to quit R. > x <- c(250.75,314.5125,449.3885,305.7,162.375,352.025,379.125,327.125,423.6625,152.25,183.8125,153.8875,245.625,108.9,291.625,284.875,192.25,45.2625,205.375,301.25,165.375,281.6375,140.5875,331.75,232.625) > 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 26 234.129 101.06024 367.1978 30.61789 437.6401 27 234.129 100.39655 367.8615 29.60286 438.6552 28 234.129 99.73614 368.5219 28.59285 439.6652 29 234.129 99.07896 369.1791 27.58778 440.6703 30 234.129 98.42496 369.8331 26.58757 441.6705 31 234.129 97.77410 370.4839 25.59217 442.6659 32 234.129 97.12633 371.1317 24.60149 443.6565 33 234.129 96.48161 371.7764 23.61547 444.6426 34 234.129 95.83989 372.4181 22.63405 445.6240 35 234.129 95.20114 373.0569 21.65716 446.6009 $period Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 26 1 1 1 1 1 27 1 1 1 1 1 28 1 1 1 1 1 29 1 1 1 1 1 30 1 1 1 1 1 31 1 1 1 1 1 32 1 1 1 1 1 33 1 1 1 1 1 34 1 1 1 1 1 35 1 1 1 1 1 In-sample error measures: ME RMSE MAE MPE MAPE MASE -6.9254103 101.8835352 86.9188492 -33.0116266 55.5697428 0.7501487 Forecasts: Point Forecast 26 234.129 27 234.129 28 234.129 29 234.129 30 234.129 31 234.129 32 234.129 33 234.129 34 234.129 35 234.129 > postscript(file="/var/www/html/rcomp/tmp/1hn141273755473.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,1)) > if (par2=='Croston') plot(m) > if ((par2=='ARIMA') | par2=='ETS') plot(forecast(m)) > plot(mydemand$resid,type='l',main='Residuals', ylab='residual value', xlab='time') > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/29e071273755473.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$upper[i,2])) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/3noyy1273755473.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Actuals and Interpolation',3,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Time',header=TRUE) > a<-table.element(a,'Actual',header=TRUE) > a<-table.element(a,'Forecast',header=TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i] - as.numeric(m$resid[i])) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/4yxfi1273755473.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/5r7wl1273755473.tab") > try(system("convert tmp/1hn141273755473.ps tmp/1hn141273755473.png",intern=TRUE)) character(0) > try(system("convert tmp/29e071273755473.ps tmp/29e071273755473.png",intern=TRUE)) character(0) > > #-SERVER-wessa.org > > > > proc.time() user system elapsed 1.745 0.348 1.923