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(288.125,331.5,493.025,378.75,458.625,431.25,399,473.5,442,177.425,346,450.25,509.625,509.625,301.375,374,374,311.875,300.375,352.75,454.25,208.7,470.85,490.125,551.125,522.8,577.875,384.25,384.25,552,661.5,457.1,647.5,174,781,277.1,653,435.75,613.775,509.75,509.75,314.5,486,212,503.825,435,563,457.05,451.25,500.75,437.75,470.5,0,313.25,314,454,570.5,485,243,310,421.752,494.5,253.5,417.5,182.826,339.25,199,412.25,438.25,356,266.25,235.25,323.775,305.25,383.527,515.25,496.15,115.25,170.5,154.25,170,534.05,193.75,564.5)
> par10 = '0.1'
> par9 = '1'
> par8 = 'dumresult'
> par7 = ''
> par6 = '1'
> par5 = 'ZZZ'
> par4 = 'NA'
> par3 = 'NA'
> par2 = 'ARIMA'
> par1 = 'Input box'
> par10 <- '0.1'
> par9 <- '1'
> par8 <- 'dumresult'
> par7 <- ''
> par6 <- '1'
> par5 <- 'ZZZ'
> par4 <- 'NA'
> par3 <- 'NA'
> par2 <- 'ARIMA'
> 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)
+ }
Series: ts(x, freq = par6) 
ARIMA(3,1,2)                    

Call: auto.arima(x = ts(x, freq = par6), d = par3, D = par4) 

Coefficients:
         ar1     ar2      ar3      ma1     ma2
      0.4261  0.1790  -0.4777  -1.3904  0.5241
s.e.  0.2921  0.1119   0.1083   0.3284  0.3021

sigma^2 estimated as 15803:  log likelihood = -520.1
AIC = 1052.21   AICc = 1053.31   BIC = 1066.72

In-sample error measures:
         ME        RMSE         MAE         MPE        MAPE        MASE 
 -3.8605043 124.9576375  95.2352725        -Inf         Inf   0.6625272 
> postscript(file="/var/www/html/rcomp/tmp/11sap1270243169.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$resid,type='l',main='Residuals', ylab='residual value', xlab='time')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2c29a1270243169.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow=c(2,2))
> acf(mydemand$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/3qupj1270243169.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/41l641270243169.tab") 
> try(system("convert tmp/11sap1270243169.ps tmp/11sap1270243169.png",intern=TRUE))
character(0)
> try(system("convert tmp/2c29a1270243169.ps tmp/2c29a1270243169.png",intern=TRUE))
character(0)
> 
> #-SERVER-wessa.org
> 
> 
> 
> proc.time()
   user  system elapsed 
  1.839   0.367   2.432