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(268.5,247,250.25,196.5,200.85,192.75,161,270.55,270.55,308,286.2,301.95,364.825,279,261.246,306,268.075,402.05,225.525,359.25,0,250,400.3,432.5,347.2,422.5,330.5,339.175,205.8,377.535,320,356.55,314.9,282.125,440.5,378.1,391.85,292.775,387,295.5,343.35,264.025,322.5,392.5,315.75,274.4,361.875,411.276,518.775,392.55,467,382.852,449.25,564.252,417,450.8,538.675,394,532,461.4,523,405.9,386.25,384.5,382,381.75,151.5,287.775,247.6,290.35,266.55,318.025,213.3,148.75,273,282.25,191.25,142.25,259.25,272.75,173.75,204.75,185.525,267.175,190.25,127.25,183.5,254.125)
> par10 = '0.1'
> par9 = '3'
> par8 = 'B521crostonm'
> par7 = 'dum'
> par6 = '12'
> par5 = 'ZZZ'
> par4 = 'NA'
> par3 = 'NA'
> par2 = 'Croston'
> par1 = 'Input box'
> par10 <- '0.1'
> par9 <- '3'
> par8 <- 'B521crostonm'
> 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       233.6465 128.2031 339.0898 72.38478 394.9082
89       233.6465 127.6772 339.6157 71.58048 395.7125
90       233.6465 127.1539 340.1390 70.78015 396.5128
91       233.6465 126.6332 340.6598 69.98373 397.3092
92       233.6465 126.1149 341.1780 69.19117 398.1018
93       233.6465 125.5992 341.6938 68.40241 398.8905
94       233.6465 125.0859 342.2071 67.61740 399.6756
95       233.6465 124.5750 342.7179 66.83608 400.4569
96       233.6465 124.0665 343.2264 66.05841 401.2345
97       233.6465 123.5604 343.7326 65.28432 402.0086

$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 
-3.8080209 88.8397269 69.5537646       -Inf        Inf  0.9236108 

Forecasts:
   Point Forecast
89       233.6242
90       233.6242
91       233.6242
92       233.6242
93       233.6242
94       233.6242
95       233.6242
96       233.6242
97       233.6242
98       233.6242
> postscript(file="/var/www/html/rcomp/tmp/1csx21272288879.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$resid,type='l',main='Residuals', ylab='residual value', xlab='time')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2njw51272288879.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow=c(2,2))
> acf(mydemand$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/31tud1272288879.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/4tktg1272288879.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/5mts11272288879.tab") 
> try(system("convert tmp/1csx21272288879.ps tmp/1csx21272288879.png",intern=TRUE))
character(0)
> try(system("convert tmp/2njw51272288879.ps tmp/2njw51272288879.png",intern=TRUE))
character(0)
> 
> #-SERVER-wessa.org
> 
> 
> 
> proc.time()
   user  system elapsed 
  3.988   0.384   4.190