Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

Patrick.Wessarwasp_demand_forecasting_croston.wasp

Title produced by software

Croston Forecasting

Date of computation

Thu, 13 May 2010 12:52:45 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/May/13/t1273755209dg77s3ds6q2y0me.htm/, Retrieved Mon, 06 May 2024 03:17:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=75909, Retrieved Mon, 06 May 2024 03:17:36 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

B11A,steven,coomans,croston,thesis,per2maand

Estimated Impact

161

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Croston Forecasting] [B11A,steven,cooma...] [2010-05-13 12:52:45] [d41d8cd98f00b204e9800998ecf8427e] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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

\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 & 2 seconds \tabularnewline
R Server & wessa.org @ wessa.org \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75909&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]wessa.org @ wessa.org[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75909&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75909&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	2 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
26	13.8322811470584	-9.16879650593073	-1.20731514548192	28.8718774395987	36.8333588000475
27	13.8322811470584	-9.28351580936999	-1.28232606613171	28.9468883602485	36.9480781034868
28	13.8322811470584	-9.39766858734293	-1.3569665556899	29.0215288498067	37.0622308814597
29	13.8322811470584	-9.51126315098685	-1.43124204851707	29.0958043426339	37.1758254451036
30	13.8322811470584	-9.6243076101925	-1.50515784738576	29.1697201415026	37.2888699043093
31	13.8322811470584	-9.73680988036091	-1.57871912789852	29.2432814220153	37.4013721744777
32	13.8322811470584	-9.84877768887134	-1.65193094271705	29.3164932368338	37.5133399829881
33	13.8322811470584	-9.9602185812752	-1.72479822561223	29.3893605197290	37.624780875392
34	13.8322811470584	-10.0711399272302	-1.79732579534422	29.461888089461	37.7357022213470
35	13.8322811470584	-10.1815489261875	-1.86951835938117	29.534080653498	37.8461112203043

\begin{tabular}{lllllllll}
\hline
Demand Forecast \tabularnewline
Point & Forecast & 95% LB & 80% LB & 80% UB & 95% UB \tabularnewline
26 & 13.8322811470584 & -9.16879650593073 & -1.20731514548192 & 28.8718774395987 & 36.8333588000475 \tabularnewline
27 & 13.8322811470584 & -9.28351580936999 & -1.28232606613171 & 28.9468883602485 & 36.9480781034868 \tabularnewline
28 & 13.8322811470584 & -9.39766858734293 & -1.3569665556899 & 29.0215288498067 & 37.0622308814597 \tabularnewline
29 & 13.8322811470584 & -9.51126315098685 & -1.43124204851707 & 29.0958043426339 & 37.1758254451036 \tabularnewline
30 & 13.8322811470584 & -9.6243076101925 & -1.50515784738576 & 29.1697201415026 & 37.2888699043093 \tabularnewline
31 & 13.8322811470584 & -9.73680988036091 & -1.57871912789852 & 29.2432814220153 & 37.4013721744777 \tabularnewline
32 & 13.8322811470584 & -9.84877768887134 & -1.65193094271705 & 29.3164932368338 & 37.5133399829881 \tabularnewline
33 & 13.8322811470584 & -9.9602185812752 & -1.72479822561223 & 29.3893605197290 & 37.624780875392 \tabularnewline
34 & 13.8322811470584 & -10.0711399272302 & -1.79732579534422 & 29.461888089461 & 37.7357022213470 \tabularnewline
35 & 13.8322811470584 & -10.1815489261875 & -1.86951835938117 & 29.534080653498 & 37.8461112203043 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75909&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]26[/C][C]13.8322811470584[/C][C]-9.16879650593073[/C][C]-1.20731514548192[/C][C]28.8718774395987[/C][C]36.8333588000475[/C][/ROW]
[ROW][C]27[/C][C]13.8322811470584[/C][C]-9.28351580936999[/C][C]-1.28232606613171[/C][C]28.9468883602485[/C][C]36.9480781034868[/C][/ROW]
[ROW][C]28[/C][C]13.8322811470584[/C][C]-9.39766858734293[/C][C]-1.3569665556899[/C][C]29.0215288498067[/C][C]37.0622308814597[/C][/ROW]
[ROW][C]29[/C][C]13.8322811470584[/C][C]-9.51126315098685[/C][C]-1.43124204851707[/C][C]29.0958043426339[/C][C]37.1758254451036[/C][/ROW]
[ROW][C]30[/C][C]13.8322811470584[/C][C]-9.6243076101925[/C][C]-1.50515784738576[/C][C]29.1697201415026[/C][C]37.2888699043093[/C][/ROW]
[ROW][C]31[/C][C]13.8322811470584[/C][C]-9.73680988036091[/C][C]-1.57871912789852[/C][C]29.2432814220153[/C][C]37.4013721744777[/C][/ROW]
[ROW][C]32[/C][C]13.8322811470584[/C][C]-9.84877768887134[/C][C]-1.65193094271705[/C][C]29.3164932368338[/C][C]37.5133399829881[/C][/ROW]
[ROW][C]33[/C][C]13.8322811470584[/C][C]-9.9602185812752[/C][C]-1.72479822561223[/C][C]29.3893605197290[/C][C]37.624780875392[/C][/ROW]
[ROW][C]34[/C][C]13.8322811470584[/C][C]-10.0711399272302[/C][C]-1.79732579534422[/C][C]29.461888089461[/C][C]37.7357022213470[/C][/ROW]
[ROW][C]35[/C][C]13.8322811470584[/C][C]-10.1815489261875[/C][C]-1.86951835938117[/C][C]29.534080653498[/C][C]37.8461112203043[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75909&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75909&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
26	13.8322811470584	-9.16879650593073	-1.20731514548192	28.8718774395987	36.8333588000475
27	13.8322811470584	-9.28351580936999	-1.28232606613171	28.9468883602485	36.9480781034868
28	13.8322811470584	-9.39766858734293	-1.3569665556899	29.0215288498067	37.0622308814597
29	13.8322811470584	-9.51126315098685	-1.43124204851707	29.0958043426339	37.1758254451036
30	13.8322811470584	-9.6243076101925	-1.50515784738576	29.1697201415026	37.2888699043093
31	13.8322811470584	-9.73680988036091	-1.57871912789852	29.2432814220153	37.4013721744777
32	13.8322811470584	-9.84877768887134	-1.65193094271705	29.3164932368338	37.5133399829881
33	13.8322811470584	-9.9602185812752	-1.72479822561223	29.3893605197290	37.624780875392
34	13.8322811470584	-10.0711399272302	-1.79732579534422	29.461888089461	37.7357022213470
35	13.8322811470584	-10.1815489261875	-1.86951835938117	29.534080653498	37.8461112203043

Actuals and Interpolation
Time	Actual	Forecast
1	46	NA
2	40.5	46
3	22.5	45.45
4	25	43.155
5	22.25	41.3395
6	7	39.43055
7	11	36.187495
8	50.25	33.6687455
9	16.25	35.32687095
10	32.5	33.419183855
11	5.7525	33.3272654695
12	7.75	30.56978892255
13	14	28.287810030295
14	3.5	26.8590290272655
15	1.25	24.5231261245390
16	3.0125	22.1958135120851
17	0.5	20.2774821608766
18	0	18.2997339447889
19	0.875	18.2997339447889
20	3.125	15.0520550457364
21	10	13.9578298121826
22	0	13.5917030950519
23	21	13.5917030950519
24	0	13.0645135486197
25	0.4125	13.0645135486197

\begin{tabular}{lllllllll}
\hline
Actuals and Interpolation \tabularnewline
Time & Actual & Forecast \tabularnewline
1 & 46 & NA \tabularnewline
2 & 40.5 & 46 \tabularnewline
3 & 22.5 & 45.45 \tabularnewline
4 & 25 & 43.155 \tabularnewline
5 & 22.25 & 41.3395 \tabularnewline
6 & 7 & 39.43055 \tabularnewline
7 & 11 & 36.187495 \tabularnewline
8 & 50.25 & 33.6687455 \tabularnewline
9 & 16.25 & 35.32687095 \tabularnewline
10 & 32.5 & 33.419183855 \tabularnewline
11 & 5.7525 & 33.3272654695 \tabularnewline
12 & 7.75 & 30.56978892255 \tabularnewline
13 & 14 & 28.287810030295 \tabularnewline
14 & 3.5 & 26.8590290272655 \tabularnewline
15 & 1.25 & 24.5231261245390 \tabularnewline
16 & 3.0125 & 22.1958135120851 \tabularnewline
17 & 0.5 & 20.2774821608766 \tabularnewline
18 & 0 & 18.2997339447889 \tabularnewline
19 & 0.875 & 18.2997339447889 \tabularnewline
20 & 3.125 & 15.0520550457364 \tabularnewline
21 & 10 & 13.9578298121826 \tabularnewline
22 & 0 & 13.5917030950519 \tabularnewline
23 & 21 & 13.5917030950519 \tabularnewline
24 & 0 & 13.0645135486197 \tabularnewline
25 & 0.4125 & 13.0645135486197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75909&T=2

[TABLE]
[ROW][C]Actuals and Interpolation[/C][/ROW]
[ROW][C]Time[/C][C]Actual[/C][C]Forecast[/C][/ROW]
[ROW][C]1[/C][C]46[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]40.5[/C][C]46[/C][/ROW]
[ROW][C]3[/C][C]22.5[/C][C]45.45[/C][/ROW]
[ROW][C]4[/C][C]25[/C][C]43.155[/C][/ROW]
[ROW][C]5[/C][C]22.25[/C][C]41.3395[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]39.43055[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]36.187495[/C][/ROW]
[ROW][C]8[/C][C]50.25[/C][C]33.6687455[/C][/ROW]
[ROW][C]9[/C][C]16.25[/C][C]35.32687095[/C][/ROW]
[ROW][C]10[/C][C]32.5[/C][C]33.419183855[/C][/ROW]
[ROW][C]11[/C][C]5.7525[/C][C]33.3272654695[/C][/ROW]
[ROW][C]12[/C][C]7.75[/C][C]30.56978892255[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]28.287810030295[/C][/ROW]
[ROW][C]14[/C][C]3.5[/C][C]26.8590290272655[/C][/ROW]
[ROW][C]15[/C][C]1.25[/C][C]24.5231261245390[/C][/ROW]
[ROW][C]16[/C][C]3.0125[/C][C]22.1958135120851[/C][/ROW]
[ROW][C]17[/C][C]0.5[/C][C]20.2774821608766[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]18.2997339447889[/C][/ROW]
[ROW][C]19[/C][C]0.875[/C][C]18.2997339447889[/C][/ROW]
[ROW][C]20[/C][C]3.125[/C][C]15.0520550457364[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]13.9578298121826[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]13.5917030950519[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]13.5917030950519[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]13.0645135486197[/C][/ROW]
[ROW][C]25[/C][C]0.4125[/C][C]13.0645135486197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75909&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75909&T=2

As an alternative you can also use a QR Code:

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

Actuals and Interpolation
Time	Actual	Forecast
1	46	NA
2	40.5	46
3	22.5	45.45
4	25	43.155
5	22.25	41.3395
6	7	39.43055
7	11	36.187495
8	50.25	33.6687455
9	16.25	35.32687095
10	32.5	33.419183855
11	5.7525	33.3272654695
12	7.75	30.56978892255
13	14	28.287810030295
14	3.5	26.8590290272655
15	1.25	24.5231261245390
16	3.0125	22.1958135120851
17	0.5	20.2774821608766
18	0	18.2997339447889
19	0.875	18.2997339447889
20	3.125	15.0520550457364
21	10	13.9578298121826
22	0	13.5917030950519
23	21	13.5917030950519
24	0	13.0645135486197
25	0.4125	13.0645135486197

What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis

\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=75909&T=3

[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=75909&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75909&T=3

As an alternative you can also use a QR Code:

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

What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

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('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',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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code