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Type 'q()' to quit R. > x <- c(192,212.25,191.8,163.7625,272.025,284.575,301.6635,287.5375,220.4375,178.3,284.8875,283.9875,238,216.275,162.875,185.95,193.7875,128.3275,83.925,177.15,142.3,120.5375,269.25,167.625,243.275) > par10 = '0.1' > par9 = '3' > par8 = 'dumresult' > par7 = 'dum' > par6 = '12' > par5 = 'ZZZ' > par4 = 'NA' > par3 = 'NA' > par2 = 'Croston' > par1 = 'Input box' > par10 <- '0.1' > par9 <- '3' > 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 26 193.9820 114.6274 273.3367 72.61955 315.3445 27 193.9820 114.2316 273.7325 72.01425 315.9498 28 193.9820 113.8378 274.1263 71.41194 316.5521 29 193.9820 113.4458 274.5182 70.81257 317.1515 30 193.9820 113.0558 274.9082 70.21610 317.7479 31 193.9820 112.6677 275.2963 69.62250 318.3415 32 193.9820 112.2814 275.6826 69.03171 318.9323 33 193.9820 111.8969 276.0671 68.44371 319.5203 34 193.9820 111.5142 276.4498 67.85844 320.1056 35 193.9820 111.1333 276.8307 67.27588 320.6882 $period Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 26 1 1 1 1 1 27 1 1 1 1 1 28 1 1 1 1 1 29 1 1 1 1 1 30 1 1 1 1 1 31 1 1 1 1 1 32 1 1 1 1 1 33 1 1 1 1 1 34 1 1 1 1 1 35 1 1 1 1 1 In-sample error measures: ME RMSE MAE MPE MAPE MASE 0.8258428 60.6226495 51.2143111 -9.9272791 28.8047284 1.0458997 Forecasts: Point Forecast 26 193.9820 27 193.9820 28 193.9820 29 193.9820 30 193.9820 31 193.9820 32 193.9820 33 193.9820 34 193.9820 35 193.9820 > postscript(file="/var/www/html/rcomp/tmp/1xisk1273756478.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,1)) > if (par2=='Croston') plot(m) > if ((par2=='ARIMA') | par2=='ETS') plot(forecast(m)) > plot(mydemand$resid,type='l',main='Residuals', ylab='residual value', xlab='time') > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2prr51273756478.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$upper[i,2])) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/3lj7w1273756478.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Actuals and Interpolation',3,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Time',header=TRUE) > a<-table.element(a,'Actual',header=TRUE) > a<-table.element(a,'Forecast',header=TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i] - as.numeric(m$resid[i])) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/4wsoh1273756478.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/5p2n21273756478.tab") > try(system("convert tmp/1xisk1273756478.ps tmp/1xisk1273756478.png",intern=TRUE)) character(0) > try(system("convert tmp/2prr51273756478.ps tmp/2prr51273756478.png",intern=TRUE)) character(0) > > #-SERVER-wessa.org > > > > proc.time() user system elapsed 1.679 0.337 1.873