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(429.75,247.325,395.15,293.905,299.125,211.375,275.25,253.25,250.5,285.8,276,300.75,309.25,309.25,336.25,331.25,331.25,308.75,230.725,330.5,224.5,197.125,306.25,235.125,322.25,280.625,232.25,317.75,317.75,232.25,319.5,265.25,203.5,168.85,295.75,312.2,335.25,261.5,305.75,230,230,247.25,276.25,356.3,320.5,188.5,372.75,296,329.5,376.53,281.5,390,0,203.25,337,214.775,270,280,309.25,347,214.575,213.62,231.75,224.3,278,226.525,360.302,263.25,263.75,269.775,283.25,286.75,230.25,200.5,297.95,329.5,289.75,223.775,281.78,265.8,256.75,89.275,225.5,124.25)
> par10 = '0.1'
> par9 = '1'
> par8 = 'dumresult'
> par7 = ''
> par6 = '1'
> par5 = 'ZZZ'
> par4 = 'NA'
> par3 = 'NA'
> par2 = 'Croston'
> par1 = 'Input box'
> par10 <- '0.1'
> par9 <- '1'
> par8 <- 'dumresult'
> par7 <- ''
> par6 <- '1'
> 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       236.2511 155.5303 316.9720 112.7992 359.7030
85       236.2511 155.1277 317.3746 112.1835 360.3188
86       236.2511 154.7271 317.7752 111.5708 360.9314
87       236.2511 154.3284 318.1739 110.9611 361.5411
88       236.2511 153.9317 318.5706 110.3544 362.1479
89       236.2511 153.5369 318.9654 109.7506 362.7517
90       236.2511 153.1439 319.3583 109.1496 363.3526
91       236.2511 152.7528 319.7494 108.5515 363.9508
92       236.2511 152.3636 320.1387 107.9562 364.5461
93       236.2511 151.9761 320.5262 107.3636 365.1387

$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 
-23.884478  74.146049  54.816610       -Inf        Inf   0.853365 

Forecasts:
   Point Forecast
85       235.2539
86       235.2539
87       235.2539
88       235.2539
89       235.2539
90       235.2539
91       235.2539
92       235.2539
93       235.2539
94       235.2539
> postscript(file="/var/www/html/rcomp/tmp/1itmk1270549863.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$resid,type='l',main='Residuals', ylab='residual value', xlab='time')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2itmk1270549863.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow=c(2,2))
> acf(mydemand$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/3e32b1270549863.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/46c1d1270549863.tab") 
> try(system("convert tmp/1itmk1270549863.ps tmp/1itmk1270549863.png",intern=TRUE))
character(0)
> try(system("convert tmp/2itmk1270549863.ps tmp/2itmk1270549863.png",intern=TRUE))
character(0)
> 
> #-SERVER-wessa.org
> 
> 
> 
> proc.time()
   user  system elapsed 
  3.800   0.374   3.973