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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 14:02:06 +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/t1273759393tfgqa3lteu46f8s.htm/, Retrieved Tue, 07 May 2024 11:21:25 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=75948, Retrieved Tue, 07 May 2024 11:21:25 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

B511,steven,coomans,thesis,croston,per3maand

Estimated Impact

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

-       [Croston Forecasting] [B511,steven,cooma...] [2010-05-13 14:02:06] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Histogram

Boxplots

66
58,66666667
66
51,33333333
73,16666667
65,83333333
65,16666667
80
88
86
94,67433333
66,16666667
94,83333333
72,33333333
86,33333333
64,33333333
62,66666667

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=75948&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=75948&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75948&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
18	73.1411724536216	47.498040693459	56.3740298367562	89.908315070487	98.7843042137841
19	73.1411724536216	47.3701439810567	56.2904026715123	89.9919422357308	98.9122009261864
20	73.1411724536216	47.2428788689384	56.2071884874962	90.0751564197469	99.0394660383047
21	73.1411724536216	47.1162360912953	56.1243812261212	90.1579636811219	99.1661088159478
22	73.1411724536216	46.9902066066816	56.0419749755036	90.2403699317395	99.2921383005615
23	73.1411724536216	46.8647815904815	55.9599639655373	90.3223809417058	99.4175633167616
24	73.1411724536216	46.7399524276985	55.8783425631789	90.4040023440643	99.5423924795446
25	73.1411724536216	46.6157107060498	55.7971052679323	90.4852396393109	99.6666342011933
26	73.1411724536216	46.4920482093507	55.716246707523	90.5660981997201	99.7902966978924
27	73.1411724536216	46.3689569111740	55.6357616337525	90.6465832734907	99.9133879960692

\begin{tabular}{lllllllll}
\hline
Demand Forecast \tabularnewline
Point & Forecast & 95% LB & 80% LB & 80% UB & 95% UB \tabularnewline
18 & 73.1411724536216 & 47.498040693459 & 56.3740298367562 & 89.908315070487 & 98.7843042137841 \tabularnewline
19 & 73.1411724536216 & 47.3701439810567 & 56.2904026715123 & 89.9919422357308 & 98.9122009261864 \tabularnewline
20 & 73.1411724536216 & 47.2428788689384 & 56.2071884874962 & 90.0751564197469 & 99.0394660383047 \tabularnewline
21 & 73.1411724536216 & 47.1162360912953 & 56.1243812261212 & 90.1579636811219 & 99.1661088159478 \tabularnewline
22 & 73.1411724536216 & 46.9902066066816 & 56.0419749755036 & 90.2403699317395 & 99.2921383005615 \tabularnewline
23 & 73.1411724536216 & 46.8647815904815 & 55.9599639655373 & 90.3223809417058 & 99.4175633167616 \tabularnewline
24 & 73.1411724536216 & 46.7399524276985 & 55.8783425631789 & 90.4040023440643 & 99.5423924795446 \tabularnewline
25 & 73.1411724536216 & 46.6157107060498 & 55.7971052679323 & 90.4852396393109 & 99.6666342011933 \tabularnewline
26 & 73.1411724536216 & 46.4920482093507 & 55.716246707523 & 90.5660981997201 & 99.7902966978924 \tabularnewline
27 & 73.1411724536216 & 46.3689569111740 & 55.6357616337525 & 90.6465832734907 & 99.9133879960692 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75948&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]18[/C][C]73.1411724536216[/C][C]47.498040693459[/C][C]56.3740298367562[/C][C]89.908315070487[/C][C]98.7843042137841[/C][/ROW]
[ROW][C]19[/C][C]73.1411724536216[/C][C]47.3701439810567[/C][C]56.2904026715123[/C][C]89.9919422357308[/C][C]98.9122009261864[/C][/ROW]
[ROW][C]20[/C][C]73.1411724536216[/C][C]47.2428788689384[/C][C]56.2071884874962[/C][C]90.0751564197469[/C][C]99.0394660383047[/C][/ROW]
[ROW][C]21[/C][C]73.1411724536216[/C][C]47.1162360912953[/C][C]56.1243812261212[/C][C]90.1579636811219[/C][C]99.1661088159478[/C][/ROW]
[ROW][C]22[/C][C]73.1411724536216[/C][C]46.9902066066816[/C][C]56.0419749755036[/C][C]90.2403699317395[/C][C]99.2921383005615[/C][/ROW]
[ROW][C]23[/C][C]73.1411724536216[/C][C]46.8647815904815[/C][C]55.9599639655373[/C][C]90.3223809417058[/C][C]99.4175633167616[/C][/ROW]
[ROW][C]24[/C][C]73.1411724536216[/C][C]46.7399524276985[/C][C]55.8783425631789[/C][C]90.4040023440643[/C][C]99.5423924795446[/C][/ROW]
[ROW][C]25[/C][C]73.1411724536216[/C][C]46.6157107060498[/C][C]55.7971052679323[/C][C]90.4852396393109[/C][C]99.6666342011933[/C][/ROW]
[ROW][C]26[/C][C]73.1411724536216[/C][C]46.4920482093507[/C][C]55.716246707523[/C][C]90.5660981997201[/C][C]99.7902966978924[/C][/ROW]
[ROW][C]27[/C][C]73.1411724536216[/C][C]46.3689569111740[/C][C]55.6357616337525[/C][C]90.6465832734907[/C][C]99.9133879960692[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75948&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75948&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
18	73.1411724536216	47.498040693459	56.3740298367562	89.908315070487	98.7843042137841
19	73.1411724536216	47.3701439810567	56.2904026715123	89.9919422357308	98.9122009261864
20	73.1411724536216	47.2428788689384	56.2071884874962	90.0751564197469	99.0394660383047
21	73.1411724536216	47.1162360912953	56.1243812261212	90.1579636811219	99.1661088159478
22	73.1411724536216	46.9902066066816	56.0419749755036	90.2403699317395	99.2921383005615
23	73.1411724536216	46.8647815904815	55.9599639655373	90.3223809417058	99.4175633167616
24	73.1411724536216	46.7399524276985	55.8783425631789	90.4040023440643	99.5423924795446
25	73.1411724536216	46.6157107060498	55.7971052679323	90.4852396393109	99.6666342011933
26	73.1411724536216	46.4920482093507	55.716246707523	90.5660981997201	99.7902966978924
27	73.1411724536216	46.3689569111740	55.6357616337525	90.6465832734907	99.9133879960692

Actuals and Interpolation
Time	Actual	Forecast
1	66	NA
2	58.66666667	66
3	66	65.266666667
4	51.33333333	65.3400000003
5	73.16666667	63.93933333327
6	65.83333333	64.862066666943
7	65.16666667	64.9591933332487
8	80	64.9799406669238
9	88	66.4819466002315
10	86	68.6337519402083
11	94.67433333	70.3703767461875
12	66.16666667	72.8007724045687
13	94.83333333	72.1373618311119
14	72.33333333	74.4069589810007
15	86.33333333	74.1995964159006
16	64.33333333	75.4129701073106
17	62.66666667	74.3050064295795

\begin{tabular}{lllllllll}
\hline
Actuals and Interpolation \tabularnewline
Time & Actual & Forecast \tabularnewline
1 & 66 & NA \tabularnewline
2 & 58.66666667 & 66 \tabularnewline
3 & 66 & 65.266666667 \tabularnewline
4 & 51.33333333 & 65.3400000003 \tabularnewline
5 & 73.16666667 & 63.93933333327 \tabularnewline
6 & 65.83333333 & 64.862066666943 \tabularnewline
7 & 65.16666667 & 64.9591933332487 \tabularnewline
8 & 80 & 64.9799406669238 \tabularnewline
9 & 88 & 66.4819466002315 \tabularnewline
10 & 86 & 68.6337519402083 \tabularnewline
11 & 94.67433333 & 70.3703767461875 \tabularnewline
12 & 66.16666667 & 72.8007724045687 \tabularnewline
13 & 94.83333333 & 72.1373618311119 \tabularnewline
14 & 72.33333333 & 74.4069589810007 \tabularnewline
15 & 86.33333333 & 74.1995964159006 \tabularnewline
16 & 64.33333333 & 75.4129701073106 \tabularnewline
17 & 62.66666667 & 74.3050064295795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75948&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]66[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]58.66666667[/C][C]66[/C][/ROW]
[ROW][C]3[/C][C]66[/C][C]65.266666667[/C][/ROW]
[ROW][C]4[/C][C]51.33333333[/C][C]65.3400000003[/C][/ROW]
[ROW][C]5[/C][C]73.16666667[/C][C]63.93933333327[/C][/ROW]
[ROW][C]6[/C][C]65.83333333[/C][C]64.862066666943[/C][/ROW]
[ROW][C]7[/C][C]65.16666667[/C][C]64.9591933332487[/C][/ROW]
[ROW][C]8[/C][C]80[/C][C]64.9799406669238[/C][/ROW]
[ROW][C]9[/C][C]88[/C][C]66.4819466002315[/C][/ROW]
[ROW][C]10[/C][C]86[/C][C]68.6337519402083[/C][/ROW]
[ROW][C]11[/C][C]94.67433333[/C][C]70.3703767461875[/C][/ROW]
[ROW][C]12[/C][C]66.16666667[/C][C]72.8007724045687[/C][/ROW]
[ROW][C]13[/C][C]94.83333333[/C][C]72.1373618311119[/C][/ROW]
[ROW][C]14[/C][C]72.33333333[/C][C]74.4069589810007[/C][/ROW]
[ROW][C]15[/C][C]86.33333333[/C][C]74.1995964159006[/C][/ROW]
[ROW][C]16[/C][C]64.33333333[/C][C]75.4129701073106[/C][/ROW]
[ROW][C]17[/C][C]62.66666667[/C][C]74.3050064295795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75948&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75948&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	66	NA
2	58.66666667	66
3	66	65.266666667
4	51.33333333	65.3400000003
5	73.16666667	63.93933333327
6	65.83333333	64.862066666943
7	65.16666667	64.9591933332487
8	80	64.9799406669238
9	88	66.4819466002315
10	86	68.6337519402083
11	94.67433333	70.3703767461875
12	66.16666667	72.8007724045687
13	94.83333333	72.1373618311119
14	72.33333333	74.4069589810007
15	86.33333333	74.1995964159006
16	64.33333333	75.4129701073106
17	62.66666667	74.3050064295795

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=75948&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=75948&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75948&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