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(840.875,1051.8,854.835,849.2,625.1,964.775,887.225,1010.45,1131.25,718.2,1088.5,1271.375,1098.375,1098.375,1195.05,1361.375,1361.375,1100.625,1190.375,1250.485,1087.575,992.05,810,1064.95,937.25,1039,1122.956,1079.5,1079.5,889.5,784.5,793.75,924.5,762.43,811.5,942.94,812.615,911.735,1009.25,1116.01,1116.01,988.16,1067.52,1082.53,1043.215,871.83,904.485,689.56,1082.78,1098.85,713.5,704.5,0,652.25,563,586,538.75,353.6,321.275,388.4,329.6,323,520.25,607.725,803.45,677.25,711,962.5,935.6,722.255,594.25,853.75,766.5,758.05,756.85,685.4,696.525,610.025,708.325,619.1,740.525,730.5,489.75,766.525)
> par10 = '0.1'
> par9 = '1'
> par8 = 'dumresult'
> par7 = 'dum'
> par6 = '12'
> par5 = 'ZZZ'
> par4 = 'NA'
> par3 = 'NA'
> par2 = 'Croston'
> par1 = 'Input box'
> par10 <- '0.1'
> par9 <- '1'
> par8 <- 'dumresult'
> 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
84       690.8818 446.5820 935.1816 317.2575 1064.506
85       690.8818 445.3635 936.4001 315.3940 1066.370
86       690.8818 444.1511 937.6125 313.5398 1068.224
87       690.8818 442.9446 938.8190 311.6946 1070.069
88       690.8818 441.7439 940.0197 309.8583 1071.905
89       690.8818 440.5490 941.2146 308.0308 1073.733
90       690.8818 439.3598 942.4038 306.2121 1075.552
91       690.8818 438.1761 943.5875 304.4018 1077.362
92       690.8818 436.9980 944.7656 302.6001 1079.164
93       690.8818 435.8253 945.9383 300.8066 1080.957

$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 
-21.133120 207.961150 156.818769       -Inf        Inf   1.147001 

Forecasts:
   Point Forecast
85       687.9654
86       687.9654
87       687.9654
88       687.9654
89       687.9654
90       687.9654
91       687.9654
92       687.9654
93       687.9654
94       687.9654
> postscript(file="/var/www/html/rcomp/tmp/1l6ok1271586024.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$resid,type='l',main='Residuals', ylab='residual value', xlab='time')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2df651271586024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow=c(2,2))
> acf(mydemand$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/3ap4e1271586024.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/4ky3h1271586024.tab") 
> try(system("convert tmp/1l6ok1271586024.ps tmp/1l6ok1271586024.png",intern=TRUE))
character(0)
> try(system("convert tmp/2df651271586024.ps tmp/2df651271586024.png",intern=TRUE))
character(0)
> 
> #-SERVER-wessa.org
> 
> 
> 
> proc.time()
   user  system elapsed 
  3.779   0.352   4.440