B11A,steven,coomans,croston,thesis,per3maand

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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 13:50:16 +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/t1273758654jhql7sphssvw9jk.htm/, Retrieved Thu, 13 May 2010 15:50:56 +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/t1273758654jhql7sphssvw9jk.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:

B11A,steven,coomans,croston,thesis,per3maand

Dataseries X:

» Textbox « » Textfile « » CSV «

41 31,66666667 23,83333333 12,33333333 30,83333333 20,83333333 25,50166667 5,166666667 11,66666667 0,833333333 2,341666667 0 0,666666667 8,666666667 2,333333333 11,66666667 0,275

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 15.7071131333506 0.847624018779348 5.99101544574916 25.4232108209521 30.5666022479219 19 15.7071131333506 0.773511393866512 5.94255580504512 25.4716704616561 30.6407148728348 20 15.7071131333506 0.699764763935608 5.89433547559669 25.5198907911046 30.7144615027657 21 15.7071131333506 0.626378759705416 5.84635094661957 25.5678753200817 30.7878475069959 22 15.7071131333506 0.553348141906921 5.79859879233991 25.6156274743614 30.8608781247944 23 15.7071131333506 0.480667796918176 5.75107566914001 25.6631505975613 30.9335584697831 24 15.7071131333506 0.408332732585798 5.70377831282625 25.7104479538750 31.0058935341155 25 15.7071131333506 0.336338074223429 5.65670353601254 25.7575227306887 31.0778881924778 26 15.7071131333506 0.26467906077815 5.60984822561369 25.8043780410876 31.1495472059231 27 15.7071131333506 0.193351041156228 5.56320934044299 25.8510169262583 31.2208752255450 Actuals and Interpolation Time Actual Forecast 1 41 NA 2 31.66666667 41 3 23.83333333 40.066666667 4 12.33333333 38.4433333333 5 30.83333333 35.83233333297 6 20.83333333 35.332433332673 7 25.50166667 33.8825233324057 8 5.166666667 33.0444376661651 9 11.66666667 30.2566605662486 10 0.833333333 28.3976611766238 11 2.341666667 25.6412283922614 12 0 23.3112722197352 13 0.666666667 23.3112722197352 14 8.666666667 19.1334651495107 15 2.333333333 18.1732084079959 16 11.66666667 16.7079100661832 17 0.275 16.2380393050513 What is next? Simulate Time Series Generate Forecasts Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273758654jhql7sphssvw9jk/1vnn61273758613.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273758654jhql7sphssvw9jk/1vnn61273758613.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273758654jhql7sphssvw9jk/25em91273758613.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273758654jhql7sphssvw9jk/25em91273758613.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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