B611,steven,coomans,thesis,ETS,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 14:17:58 +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/t12737603081qo0y914a3m4muc.htm/, Retrieved Thu, 13 May 2010 16:18:31 +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/t12737603081qo0y914a3m4muc.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:

B611,steven,coomans,thesis,ETS,per3maand

Dataseries X:

» Textbox « » Textfile « » CSV «

42.13333333 43.68333333 9.016666667 33.7 19.09866667 10.06733333 18.81666667 40.85 51.575 16.625 42.6 25.66666667 49.325 39.40833333 37.55333333 57.05 29.575

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 4 seconds R Server wessa.org @ wessa.org Demand Forecast Point Forecast 95% LB 80% LB 80% UB 95% UB 18 14.5239057624741 8.8451132752524 10.8107429391737 18.2370685857745 20.2026982496958 19 26.4836378104018 15.1212956251028 19.0542015908172 33.9130740299865 37.8459799957009 20 56.2750441920772 30.1508538185412 39.1933542549803 73.356734129174 82.3992345656131 21 66.9463679910563 33.6622916611225 45.1830802649593 88.7096557171532 100.23044432099 22 20.5047364199225 9.6718980693779 13.4215241481981 27.5879486916469 31.3375747704670 23 52.3882311082712 23.1592103232476 33.2764016859703 71.5000605305721 81.6172518932948 24 29.6279067421743 12.2578937246599 18.2702657080970 40.9855477762515 46.9979197596887 25 52.9210052376578 20.4533776278400 31.6915646351329 74.1504458401827 85.3886328474756 26 46.3325876515888 16.689426535232 26.9499664218049 65.7152088813727 75.9757487679456 27 34.4238957481409 11.5242246531866 19.4506056399137 49.3971858563681 57.3235668430952 28 56.3649442464111 17.4781036334643 30.9382058676953 81.7916826251269 95.2517848593578 29 29.0263942706806 8.3036592680552 15.4765261636844 42.5762623776768 49.749129273306 30 14.5239328059224 3.81470732584107 7.52154664575575 21.5263189660891 25.2331582860037 31 26.4836871228183 6.35037130906046 13.3192195079809 39.6481547376557 46.6170029365762 32 56.2751489759605 12.2351095722754 27.478915019813 85.0713829321081 100.315188379646 33 66.9464926448974 13.0881579041405 31.7304204368972 102.162564852898 120.804827385654 34 20.504774599651 3.56792463294432 9.43036366075785 31.5791855385441 37.4416245663577 35 52.3883286549258 8.00971864013027 23.3707152754121 81.4059420344395 96.7669386697213 36 29.6279619092056 3.91482055802684 12.8150424812980 46.4408813371132 55.3411032603844 37 52.9211037763354 5.91142240536914 22.1831253073828 83.659082245288 99.9307851473018 38 46.3326739226621 4.24303893316189 18.8117408649799 73.8536069803442 88.4223089121623 39 34.4239598452825 2.46947132079097 13.5300428561144 55.3178768344506 66.378448369774 40 56.365049197688 2.94008379871995 21.4323419712943 91.2977564240816 109.790014596656 41 29.0264483176983 0.953050538285584 10.6702401637992 47.3826564715975 57.0998460971111 Actuals and Interpolation Time Actual Forecast 1 42.13333333 42.1754999382236 2 43.68333333 43.5274555841281 3 9.016666667 9.71630430766763 4 33.7 33.7064299229627 5 19.09866667 19.0021118404607 6 10.06733333 9.95953230744406 7 18.81666667 18.7328988002079 8 40.85 40.7823631786921 9 51.575 51.4748790463977 10 16.625 16.5163611830858 11 42.6 42.5391741934841 12 25.66666667 25.5661618094048 13 49.325 49.1914327687304 14 39.40833333 39.4316835376926 15 37.55333333 37.287564195407 16 57.05 56.9993272182539 17 29.575 29.5405454920193 What is next? Simulate Time Series Generate Forecasts Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737603081qo0y914a3m4muc/1w1jj1273760273.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t12737603081qo0y914a3m4muc/1w1jj1273760273.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t12737603081qo0y914a3m4muc/2w1jj1273760273.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t12737603081qo0y914a3m4muc/2w1jj1273760273.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 = ETS ; 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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