R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(203.5,168.85,295.75,312.2,335.25,261.5,305.75,230,230,247.25,276.25,356.3,320.5,188.5,372.75,296,329.5,376.53,281.5,390,0,203.25,337,214.775,270,280,309.25,347,214.575,213.62,231.75,224.3,278,226.525,360.302,263.25,263.75,269.775,283.25,286.75,230.25,200.5,297.95,329.5,289.75,223.775,281.78,265.8,256.75,89.275,225.5,124.25,230,286.525,227,218.3,334.525,128.95,195.5,106.056,173.525,114.75,131.05,141.25,160.25,145.5,297.5,179.25,137,158.6,55.6,15.25,67.75,93,126.75,160,150.525,239.25,165.05,215.81,166,79.05,204.25,102,87.025,72.175,176.75,188.975)
> par10 = '0.1'
> par9 = '3'
> par8 = 'B382crostonm'
> par7 = 'dum'
> par6 = '12'
> par5 = 'ZZZ'
> par4 = 'NA'
> par3 = 'NA'
> par2 = 'Croston'
> par1 = 'Input box'
> par10 <- '0.1'
> par9 <- '3'
> par8 <- 'B382crostonm'
> 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
88       146.8849 60.29961 233.4702 14.464133 279.3057
89       146.8849 59.86777 233.9020 13.803676 279.9661
90       146.8849 59.43805 234.3318 13.146481 280.6233
91       146.8849 59.01043 234.7594 12.492499 281.2773
92       146.8849 58.58489 235.1849 11.841685 281.9281
93       146.8849 58.16138 235.6084 11.193992 282.5758
94       146.8849 57.73989 236.0299 10.549376 283.2204
95       146.8849 57.32038 236.4494  9.907793 283.8620
96       146.8849 56.90283 236.8670  9.269202 284.5006
97       146.8849 56.48721 237.2826  8.633560 285.1362

$period
   Point Forecast     Lo 80    Hi 80     Lo 95    Hi 95
88       1.000096 0.8574806 1.142710 0.7819849 1.218206
89       1.000096 0.8567693 1.143422 0.7808970 1.219294
90       1.000096 0.8560615 1.144129 0.7798146 1.220376
91       1.000096 0.8553572 1.144834 0.7787374 1.221454
92       1.000096 0.8546563 1.145535 0.7776654 1.222526
93       1.000096 0.8539587 1.146232 0.7765986 1.223592
94       1.000096 0.8532645 1.146926 0.7755369 1.224654
95       1.000096 0.8525735 1.147617 0.7744801 1.225711
96       1.000096 0.8518858 1.148305 0.7734283 1.226763
97       1.000096 0.8512012 1.148990 0.7723813 1.227810


In-sample error measures:
        ME       RMSE        MAE        MPE       MAPE       MASE 
-6.8844821 74.0441673 57.0328369       -Inf        Inf  0.8564055 

Forecasts:
   Point Forecast
89       146.8709
90       146.8709
91       146.8709
92       146.8709
93       146.8709
94       146.8709
95       146.8709
96       146.8709
97       146.8709
98       146.8709
> postscript(file="/var/www/html/rcomp/tmp/1bmib1272278703.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$resid,type='l',main='Residuals', ylab='residual value', xlab='time')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2bmib1272278703.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow=c(2,2))
> acf(mydemand$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/37efk1272278703.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/4aewq1272278703.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/5l5dt1272278703.tab") 
> try(system("convert tmp/1bmib1272278703.ps tmp/1bmib1272278703.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bmib1272278703.ps tmp/2bmib1272278703.png",intern=TRUE))
character(0)
> 
> #-SERVER-wessa.org
> 
> 
> 
> proc.time()
   user  system elapsed 
  4.120   0.354   4.298