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Type 'q()' to quit R. > x <- c(341.25,303.6875,357.5,295.075,386.5755,455.6625,424.926,506.751,433.9,466.3375,496.7,464.45,385.375,381.875,219.6375,268.975,292.2875,181.025,277.625,166.75,266,189.25,226.35,158.75,218.8125) > 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 26 275.695 148.9026 402.4875 81.78272 469.6073 27 275.695 148.2702 403.1198 80.81557 470.5745 28 275.695 147.6409 403.7491 79.85320 471.5368 29 275.695 147.0148 404.3753 78.89553 472.4945 30 275.695 146.3916 404.9984 77.94250 473.4475 31 275.695 145.7714 405.6186 76.99405 474.3960 32 275.695 145.1542 406.2358 76.05009 475.3400 33 275.695 144.5399 406.8501 75.11058 476.2795 34 275.695 143.9285 407.4616 74.17545 477.2146 35 275.695 143.3198 408.0702 73.24464 478.1454 $period Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 26 1 1 1 1 1 27 1 1 1 1 1 28 1 1 1 1 1 29 1 1 1 1 1 30 1 1 1 1 1 31 1 1 1 1 1 32 1 1 1 1 1 33 1 1 1 1 1 34 1 1 1 1 1 35 1 1 1 1 1 In-sample error measures: ME RMSE MAE MPE MAPE MASE -27.314574 100.631490 87.960300 -20.760803 33.996371 1.343066 Forecasts: Point Forecast 26 275.695 27 275.695 28 275.695 29 275.695 30 275.695 31 275.695 32 275.695 33 275.695 34 275.695 35 275.695 > postscript(file="/var/www/html/rcomp/tmp/1w1031273756227.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,1)) > if (par2=='Croston') plot(m) > if ((par2=='ARIMA') | par2=='ETS') plot(forecast(m)) > plot(mydemand$resid,type='l',main='Residuals', ylab='residual value', xlab='time') > par(op) > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2w1031273756227.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$upper[i,2])) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/3ssgu1273756227.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Actuals and Interpolation',3,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Time',header=TRUE) > a<-table.element(a,'Actual',header=TRUE) > a<-table.element(a,'Forecast',header=TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i] - as.numeric(m$resid[i])) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/4wtxh1273756227.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/5p2w21273756227.tab") > try(system("convert tmp/1w1031273756227.ps tmp/1w1031273756227.png",intern=TRUE)) character(0) > try(system("convert tmp/2w1031273756227.ps tmp/2w1031273756227.png",intern=TRUE)) character(0) > > #-SERVER-wessa.org > > > > proc.time() user system elapsed 1.670 0.358 1.846