FM22,steven,coomans,thesis,croston,per2maand

*Unverified author*

R Software Module: Patrick.Wessa/rwasp_demand_forecasting_croston.wasp (opens new window with default values)

Title produced by software: Croston Forecasting

Date of computation: Thu, 13 May 2010 13:28:41 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/May/13/t1273757362c7iw78mxqisxs5o.htm/, Retrieved Thu, 13 May 2010 15:29:25 +0200

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/May/13/t1273757362c7iw78mxqisxs5o.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

FM22,steven,coomans,thesis,croston,per2maand

Dataseries X:

» Textbox « » Textfile « » CSV «

724 762,275 721,125 653,275 663,7125 735,5125 628,1375 792,55 636,5 800,825 728,05 618,2625 450,625 767,525 675,65 583,25 690,7875 208,0625 142,5 205,925 462,5625 251,4375 195,725 191,625 137,25

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server wessa.org @ wessa.org Demand Forecast Point Forecast 95% LB 80% LB 80% UB 95% UB 26 417.951769404061 71.6250244704416 191.500882724222 644.4026560839 764.278514337681 27 417.951769404061 69.8976983190075 190.371444861569 645.532093946554 766.005840489115 28 417.951769404061 68.1789023296656 189.247584572110 646.655954236012 767.724636478457 29 417.951769404061 66.4685113617955 188.129220030794 647.774318777329 769.435027446327 30 417.951769404061 64.7664033049416 187.016271393883 648.887267414239 771.137135503181 31 417.951769404061 63.0724589770764 185.908660732440 649.994878075682 772.831079831046 32 417.951769404061 61.3865620272121 184.806311968643 651.09722683948 774.51697678091 33 417.951769404061 59.7085988421389 183.709150814806 652.194387993317 776.194939965984 34 417.951769404061 58.0384584570754 182.617104714959 653.286434093164 777.865080351047 35 417.951769404061 56.3760324700339 181.530102788852 654.37343601927 779.527506338089 Actuals and Interpolation Time Actual Forecast 1 724 NA 2 762.275 724 3 721.125 727.8275 4 653.275 727.15725 5 663.7125 719.769025 6 735.5125 714.1633725 7 628.1375 716.29828525 8 792.55 707.482206725 9 636.5 715.9889860525 10 800.825 708.04008744725 11 728.05 717.318578702525 12 618.2625 718.391720832272 13 450.625 708.378798749045 14 767.525 682.60341887414 15 675.65 691.095576986727 16 583.25 689.551019288054 17 690.7875 678.920917359249 18 208.0625 680.107575623324 19 142.5 632.903068060991 20 205.925 583.862761254892 21 462.5625 546.068985129403 22 251.4375 537.718336616463 23 195.725 509.090252954817 24 191.625 477.753727659335 25 137.25 449.140854893401 What is next? Simulate Time Series Generate Forecasts Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273757362c7iw78mxqisxs5o/1x75n1273757319.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273757362c7iw78mxqisxs5o/1x75n1273757319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273757362c7iw78mxqisxs5o/2qy581273757319.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273757362c7iw78mxqisxs5o/2qy581273757319.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = ARIMA ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = dumresult ; par9 = 3 ; par10 = 0.1 ;

Parameters (R input):

par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = dumresult ; par9 = 3 ; par10 = 0.1 ;

R code (references can be found in the software module):

par10 <- '0.1' par9 <- '3' par8 <- 'dumresult' par7 <- 'dum' par6 <- '12' par5 <- 'ZZZ' par4 <- 'NA' par3 <- 'NA' par2 <- 'Croston' par1 <- 'Input box' 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) 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) } bitmap(file='test1.png') op <- par(mfrow=c(2,1)) if (par2=='Croston') plot(m) if ((par2=='ARIMA') | par2=='ETS') plot(forecast(m)) plot(mydemand$resid,type='l',main='Residuals', ylab='residual value', xlab='time') par(op) dev.off() bitmap(file='pic2.png') op <- par(mfrow=c(2,2)) acf(mydemand$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() load(file='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='mytable.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='mytable1.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='mytable0.tab') -SERVER-wessa.org

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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