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(610,544,471.4,696.675,707.9,524.075,471.75,526.625,390.2,451.5,681.875,546.5,488.5,488.5,613.125,439,439,460.625,657.125,570.625,804.575,443,745.775,667.125,590.55,683.125,818.26,679.7,679.7,535.75,579,700.75,567.5,531.25,781.25,572.5,591.5,548.75,744,634.25,634.25,313.25,674.25,769.55,758.25,488.775,690.45,559.5,687.1,796.5,756.65,794.5,0,387.5,683,762.25,742,731.5,643,573.44,574.751,440.025,350.75,562.75,642.251,411,646,558.525,647.15,591,797,642.25,726.275,652.75,678.75,602.25,689.775,393,580.525,462.25,725.65,501,675,691)
> par10 = '0.1'
> par9 = '1'
> par8 = ''
> par7 = ''
> par6 = '12'
> par5 = 'ZZZ'
> par4 = 'NA'
> par3 = 'NA'
> par2 = 'Croston'
> par1 = 'Input box'
> par10 <- '0.1'
> par9 <- '1'
> par8 <- ''
> par7 <- ''
> 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
84        613.496 456.7560 770.2358 373.7829 853.2089
85        613.496 455.9743 771.0176 372.5874 854.4045
86        613.496 455.1964 771.7955 371.3977 855.5942
87        613.496 454.4223 772.5696 370.2138 856.7781
88        613.496 453.6520 773.3399 369.0357 857.9562
89        613.496 452.8853 774.1066 367.8632 859.1287
90        613.496 452.1223 774.8696 366.6963 860.2956
91        613.496 451.3629 775.6290 365.5349 861.4570
92        613.496 450.6071 776.3848 364.3789 862.6130
93        613.496 449.8547 777.1372 363.2282 863.7637

$period
   Point Forecast     Lo 80    Hi 80     Lo 95    Hi 95
84       1.004239 0.8581534 1.150325 0.7808203 1.227658
85       1.004239 0.8574248 1.151053 0.7797060 1.228772
86       1.004239 0.8566998 1.151778 0.7785972 1.229881
87       1.004239 0.8559783 1.152500 0.7774938 1.230984
88       1.004239 0.8552603 1.153218 0.7763957 1.232082
89       1.004239 0.8545458 1.153932 0.7753030 1.233175
90       1.004239 0.8538347 1.154644 0.7742154 1.234263
91       1.004239 0.8531269 1.155351 0.7731329 1.235345
92       1.004239 0.8524224 1.156056 0.7720555 1.236423
93       1.004239 0.8517212 1.156757 0.7709830 1.237495


In-sample error measures:
         ME        RMSE         MAE         MPE        MAPE        MASE 
 -0.6827508 141.7889953 109.2357981        -Inf         Inf   0.8044912 

Forecasts:
   Point Forecast
85       610.9062
86       610.9062
87       610.9062
88       610.9062
89       610.9062
90       610.9062
91       610.9062
92       610.9062
93       610.9062
94       610.9062
> postscript(file="/var/www/html/rcomp/tmp/1xq1b1271329952.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$resid,type='l',main='Residuals', ylab='residual value', xlab='time')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/28i0e1271329952.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow=c(2,2))
> acf(mydemand$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/3mag51271329952.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/4f1x81271329952.tab") 
> try(system("convert tmp/1xq1b1271329952.ps tmp/1xq1b1271329952.png",intern=TRUE))
character(0)
> try(system("convert tmp/28i0e1271329952.ps tmp/28i0e1271329952.png",intern=TRUE))
character(0)
> 
> #-SERVER-wessa.org
> 
> 
> 
> proc.time()
   user  system elapsed 
  3.764   0.361   4.200