FM22,steven,coomans,thesis,Arima;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 13:31:23 +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/t1273757525ersa4g2mglge9fc.htm/, Retrieved Thu, 13 May 2010 15:32:08 +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/t1273757525ersa4g2mglge9fc.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,Arima;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 3 seconds R Server wessa.org @ wessa.org Demand Forecast Point Forecast 95% LB 80% LB 80% UB 95% UB 26 137.817252242887 -139.550119579668 -43.5435239723977 319.178028458171 415.184624065441 27 112.748907964002 -181.930777701299 -79.9317818228467 305.429597750850 407.428593629303 28 87.6805636851168 -223.349298765180 -115.690932066036 291.052059436269 398.710426135413 29 62.6122194062318 -263.950230253707 -150.915489220509 276.139928032972 389.17466906617 30 37.5438751273468 -303.845186970356 -185.678434283499 260.766184538193 378.93293722505 31 12.4755308484619 -343.122486457595 -220.03751499206 244.988576688984 368.073548154519 32 -12.5928134304231 -381.853436979765 -254.039357314079 228.853730453232 356.667810118919 33 -37.6611577093081 -420.096598399757 -287.722251485783 212.399936067167 344.774282981141 34 -62.7295019881931 -457.900761682199 -321.11810020416 195.659096227774 332.441757705813 35 -87.797846267078 -495.307088714654 -354.253817780831 178.658125246674 319.711396180498 36 -112.866190545963 -532.350684743599 -387.152358302675 161.419977210749 306.618303651673 37 -137.934534824848 -569.061777137714 -419.833486383715 143.964416734018 293.192707488018 38 -163.002879103733 -605.466614630551 -452.314365148296 126.30860694083 279.460856423085 39 -188.071223382618 -641.588164023644 -484.610011780761 108.467565015525 265.445717258408 40 -213.139567661503 -677.446657463549 -516.733655370674 90.4545200476682 251.167522140543 41 -238.207911940388 -713.060027690644 -548.697021506713 72.2811976259367 236.644203809868 42 -263.276256219273 -748.444258074861 -580.510561152731 53.9580487141853 221.891745636316 43 -288.344600498158 -783.613666982825 -612.18363658544 35.4944355891236 206.924465986510 44 -313.412944777043 -818.581140934168 -643.724673847121 16.8987842930355 191.755251380082 45 -338.481289055928 -853.358327387113 -675.141288801354 -1.82128931050204 176.395749275258 46 -363.549633334813 -887.955795381697 -706.440392171961 -20.6588744976646 160.856528712071 47 -388.617977613698 -922.383170357466 -737.628277695573 -39.6076775318226 145.147215130071 48 -413.686321892583 -956.64924804578 -768.710696591794 -58.661947193372 129.276604260614 49 -438.754666171468 -990.76209127451 -799.692920860398 -77.8164114825378 113.252758931575 Actuals and Interpolation Time Actual Forecast 1 724 723.250932184245 2 762.275 708.951659916233 3 721.125 709.122505687033 4 653.275 688.027750893318 5 663.7125 650.13316612633 6 735.5125 630.378745649382 7 628.1375 643.244311473 8 792.55 612.847273246156 9 636.5 652.317786039178 10 800.825 621.589491648231 11 728.05 660.842815301877 12 618.2625 659.888667071522 13 450.625 619.88373918091 14 767.525 534.087282375116 15 675.65 592.774139057074 16 583.25 597.440724777523 17 690.7875 567.280936817024 18 208.0625 586.525266208221 19 142.5 425.668945310045 20 205.925 299.002937771023 21 462.5625 240.53933481731 22 251.4375 295.130267176479 23 195.725 254.38547660905 24 191.625 208.270449663097 25 137.25 177.229915639438 What is next? Simulate Time Series Generate Forecasts Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273757525ersa4g2mglge9fc/1a3cq1273757480.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273757525ersa4g2mglge9fc/1a3cq1273757480.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273757525ersa4g2mglge9fc/2a3cq1273757480.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/13/t1273757525ersa4g2mglge9fc/2a3cq1273757480.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 = ARIMA ; 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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by