## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software ModulePatrick.Wessarwasp_demand_forecasting_croston.wasp
Title produced by softwareCroston Forecasting
Date of computationSun, 18 Apr 2010 10:20:29 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Apr/18/t1271586087gtb9plywafn15vv.htm/, Retrieved Thu, 08 Aug 2024 13:35:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=74662, Retrieved Thu, 08 Aug 2024 13:35:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsmissingvalue,B22,steven,coomans,thesis
Estimated Impact262
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Croston Forecasting] [missingvalue,B22,...] [2010-04-18 10:20:29] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
840,875
1051,8
854,835
849,2
625,1
964,775
887,225
1010,45
1131,25
718,2
1088,5
1271,375
1098,375
1098,375
1195,05
1361,375
1361,375
1100,625
1190,375
1250,485
1087,575
992,05
810
1064,95
937,25
1039
1122,956
1079,5
1079,5
889,5
784,5
793,75
924,5
762,43
811,5
942,94
812,615
911,735
1009,25
1116,01
1116,01
988,16
1067,52
1082,53
1043,215
871,83
904,485
689,56
1082,78
1098,85
713,5
704,5
0
652,25
563
586
538,75
353,6
321,275
388,4
329,6
323
520,25
607,725
803,45
677,25
711
962,5
935,6
722,255
594,25
853,75
766,5
758,05
756,85
685,4
696,525
610,025
708,325
619,1
740,525
730,5
489,75
766,525

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server wessa.org @ wessa.org

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & wessa.org @ wessa.org \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74662&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]wessa.org @ wessa.org[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74662&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74662&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server wessa.org @ wessa.org

 Demand Forecast Point Forecast 95% LB 80% LB 80% UB 95% UB 85 690.881799688875 317.257512089300 446.582009245998 935.181590131752 1064.50608728845 86 690.881799688875 315.394037748312 445.363548867194 936.400050510556 1066.36956162944 87 690.881799688875 313.539765918483 444.151105687009 937.61249369074 1068.22383345927 88 690.881799688875 311.694561595584 442.944591430924 938.819007946826 1070.06903778217 89 690.881799688875 309.858293044388 441.743919961904 940.019679415846 1071.90530633336 90 690.881799688875 308.030831688918 440.549007208634 941.214592169115 1073.73276768883 91 690.881799688875 306.212052007378 439.359771096826 942.403828280924 1075.55154737037 92 690.881799688875 304.401831431541 438.176131483423 943.587467894327 1077.36176794621 93 690.881799688875 302.600050250361 436.998010093580 944.76558928417 1079.16354912739 94 690.881799688875 300.806591517584 435.82533046025 945.9382689175 1080.95700786017

\begin{tabular}{lllllllll}
\hline
Demand Forecast \tabularnewline
Point & Forecast & 95% LB & 80% LB & 80% UB & 95% UB \tabularnewline
85 & 690.881799688875 & 317.257512089300 & 446.582009245998 & 935.181590131752 & 1064.50608728845 \tabularnewline
86 & 690.881799688875 & 315.394037748312 & 445.363548867194 & 936.400050510556 & 1066.36956162944 \tabularnewline
87 & 690.881799688875 & 313.539765918483 & 444.151105687009 & 937.61249369074 & 1068.22383345927 \tabularnewline
88 & 690.881799688875 & 311.694561595584 & 442.944591430924 & 938.819007946826 & 1070.06903778217 \tabularnewline
89 & 690.881799688875 & 309.858293044388 & 441.743919961904 & 940.019679415846 & 1071.90530633336 \tabularnewline
90 & 690.881799688875 & 308.030831688918 & 440.549007208634 & 941.214592169115 & 1073.73276768883 \tabularnewline
91 & 690.881799688875 & 306.212052007378 & 439.359771096826 & 942.403828280924 & 1075.55154737037 \tabularnewline
92 & 690.881799688875 & 304.401831431541 & 438.176131483423 & 943.587467894327 & 1077.36176794621 \tabularnewline
93 & 690.881799688875 & 302.600050250361 & 436.998010093580 & 944.76558928417 & 1079.16354912739 \tabularnewline
94 & 690.881799688875 & 300.806591517584 & 435.82533046025 & 945.9382689175 & 1080.95700786017 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74662&T=1

[TABLE]
[ROW][C]Demand Forecast[/C][/ROW]
[ROW][C]Point[/C][C]Forecast[/C][C]95% LB[/C][C]80% LB[/C][C]80% UB[/C][C]95% UB[/C][/ROW]
[ROW][C]85[/C][C]690.881799688875[/C][C]317.257512089300[/C][C]446.582009245998[/C][C]935.181590131752[/C][C]1064.50608728845[/C][/ROW]
[ROW][C]86[/C][C]690.881799688875[/C][C]315.394037748312[/C][C]445.363548867194[/C][C]936.400050510556[/C][C]1066.36956162944[/C][/ROW]
[ROW][C]87[/C][C]690.881799688875[/C][C]313.539765918483[/C][C]444.151105687009[/C][C]937.61249369074[/C][C]1068.22383345927[/C][/ROW]
[ROW][C]88[/C][C]690.881799688875[/C][C]311.694561595584[/C][C]442.944591430924[/C][C]938.819007946826[/C][C]1070.06903778217[/C][/ROW]
[ROW][C]89[/C][C]690.881799688875[/C][C]309.858293044388[/C][C]441.743919961904[/C][C]940.019679415846[/C][C]1071.90530633336[/C][/ROW]
[ROW][C]90[/C][C]690.881799688875[/C][C]308.030831688918[/C][C]440.549007208634[/C][C]941.214592169115[/C][C]1073.73276768883[/C][/ROW]
[ROW][C]91[/C][C]690.881799688875[/C][C]306.212052007378[/C][C]439.359771096826[/C][C]942.403828280924[/C][C]1075.55154737037[/C][/ROW]
[ROW][C]92[/C][C]690.881799688875[/C][C]304.401831431541[/C][C]438.176131483423[/C][C]943.587467894327[/C][C]1077.36176794621[/C][/ROW]
[ROW][C]93[/C][C]690.881799688875[/C][C]302.600050250361[/C][C]436.998010093580[/C][C]944.76558928417[/C][C]1079.16354912739[/C][/ROW]
[ROW][C]94[/C][C]690.881799688875[/C][C]300.806591517584[/C][C]435.82533046025[/C][C]945.9382689175[/C][C]1080.95700786017[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74662&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74662&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Demand Forecast Point Forecast 95% LB 80% LB 80% UB 95% UB 85 690.881799688875 317.257512089300 446.582009245998 935.181590131752 1064.50608728845 86 690.881799688875 315.394037748312 445.363548867194 936.400050510556 1066.36956162944 87 690.881799688875 313.539765918483 444.151105687009 937.61249369074 1068.22383345927 88 690.881799688875 311.694561595584 442.944591430924 938.819007946826 1070.06903778217 89 690.881799688875 309.858293044388 441.743919961904 940.019679415846 1071.90530633336 90 690.881799688875 308.030831688918 440.549007208634 941.214592169115 1073.73276768883 91 690.881799688875 306.212052007378 439.359771096826 942.403828280924 1075.55154737037 92 690.881799688875 304.401831431541 438.176131483423 943.587467894327 1077.36176794621 93 690.881799688875 302.600050250361 436.998010093580 944.76558928417 1079.16354912739 94 690.881799688875 300.806591517584 435.82533046025 945.9382689175 1080.95700786017

\begin{tabular}{lllllllll}
\hline
What is next? \tabularnewline
Simulate Time Series \tabularnewline
Generate Forecasts \tabularnewline
Forecast Analysis \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74662&T=2

[TABLE]
[ROW][C]What is next?[/C][/ROW]
[ROW][C]Simulate Time Series[/C][/ROW]
[ROW][C]Generate Forecasts[/C][/ROW]
[ROW][C]Forecast Analysis[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74662&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74662&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

if(par3!='NA') par3 <- as.numeric(par3) else par3 <- NAif(par4!='NA') par4 <- as.numeric(par4) else par4 <- NApar6 <- as.numeric(par6) #Seasonal Periodpar9 <- as.numeric(par9) #Forecast Horizonpar10 <- as.numeric(par10) #Alphalibrary(forecast)if (par1 == 'CSV') {xarr <- read.csv(file=paste('tmp/',par7,'.csv',sep=''),header=T)numseries <- length(xarr[1,])-1n <- length(xarr[,1])nmh <- n - par9nmhp1 <- nmh + 1rarr <- 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+1x <- 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 <- 1n <- 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,'What is next?',1,TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,hyperlink(paste('https://automated.biganalytics.eu/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('https://automated.biganalytics.eu/Patrick.Wessa/rwasp_demand_forecasting_croston.wasp#output',sep=''),'Generate Forecasts','',target=''))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,hyperlink(paste('https://automated.biganalytics.eu/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