B511,steven,coomans,thesis,croston,per3maand

*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 14:02:06 +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/t1273759393tfgqa3lteu46f8s.htm/, Retrieved Thu, 13 May 2010 16:03:16 +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/t1273759393tfgqa3lteu46f8s.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:

B511,steven,coomans,thesis,croston,per3maand

Dataseries X:

» Textbox « » Textfile « » CSV «

66 58,66666667 66 51,33333333 73,16666667 65,83333333 65,16666667 80 88 86 94,67433333 66,16666667 94,83333333 72,33333333 86,33333333 64,33333333 62,66666667

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 18 73.1411724536216 47.498040693459 56.3740298367562 89.908315070487 98.7843042137841 19 73.1411724536216 47.3701439810567 56.2904026715123 89.9919422357308 98.9122009261864 20 73.1411724536216 47.2428788689384 56.2071884874962 90.0751564197469 99.0394660383047 21 73.1411724536216 47.1162360912953 56.1243812261212 90.1579636811219 99.1661088159478 22 73.1411724536216 46.9902066066816 56.0419749755036 90.2403699317395 99.2921383005615 23 73.1411724536216 46.8647815904815 55.9599639655373 90.3223809417058 99.4175633167616 24 73.1411724536216 46.7399524276985 55.8783425631789 90.4040023440643 99.5423924795446 25 73.1411724536216 46.6157107060498 55.7971052679323 90.4852396393109 99.6666342011933 26 73.1411724536216 46.4920482093507 55.716246707523 90.5660981997201 99.7902966978924 27 73.1411724536216 46.3689569111740 55.6357616337525 90.6465832734907 99.9133879960692 Actuals and Interpolation Time Actual Forecast 1 66 NA 2 58.66666667 66 3 66 65.266666667 4 51.33333333 65.3400000003 5 73.16666667 63.93933333327 6 65.83333333 64.862066666943 7 65.16666667 64.9591933332487 8 80 64.9799406669238 9 88 66.4819466002315 10 86 68.6337519402083 11 94.67433333 70.3703767461875 12 66.16666667 72.8007724045687 13 94.83333333 72.1373618311119 14 72.33333333 74.4069589810007 15 86.33333333 74.1995964159006 16 64.33333333 75.4129701073106 17 62.66666667 74.3050064295795 What is next? Simulate Time Series Generate Forecasts Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273759393tfgqa3lteu46f8s/1qow11273759322.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273759393tfgqa3lteu46f8s/1qow11273759322.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273759393tfgqa3lteu46f8s/2qow11273759322.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273759393tfgqa3lteu46f8s/2qow11273759322.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):

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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