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B11A,steven,coomans,Arima,thesis,per2maand

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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 12:55:47 +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/t1273755383viam73hwfhzejyr.htm/, Retrieved Thu, 13 May 2010 14:56:29 +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/t1273755383viam73hwfhzejyr.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:

B11A,steven,coomans,Arima,thesis,per2maand

Dataseries X:

» Textbox « » Textfile « » CSV «

46 40.5 22.5 25 22.25 7 11 50.25 16.25 32.5 5.7525 7.75 14 3.5 1.25 3.0125 0.5 0 0.875 3.125 10 0 21 0 0.4125

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	2 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
26	-15.1982691889396	-45.9521218500289	-35.3071325839963	4.91059420611706	15.5555834721496
27	-17.4482691889396	-48.2021218500289	-37.5571325839963	2.66059420611705	13.3055834721496
28	-15.6857691889396	-46.4396218500289	-35.7946325839963	4.42309420611705	15.0680834721496
29	-18.1982691889396	-48.9521218500289	-38.3071325839963	1.91059420611705	12.5555834721496
30	-18.6982691889396	-49.4521218500289	-38.8071325839963	1.41059420611706	12.0555834721496
31	-17.8232691889396	-48.5771218500289	-37.9321325839963	2.28559420611706	12.9305834721496
32	-15.5732691889396	-46.3271218500289	-35.6821325839963	4.53559420611706	15.1805834721496
33	-8.69826918893965	-39.4521218500289	-28.8071325839963	11.4105942061171	22.0555834721496
34	-18.6982691889396	-49.4521218500289	-38.8071325839963	1.41059420611705	12.0555834721496
35	2.30173081106036	-28.4521218500289	-17.8071325839963	22.4105942061171	33.0555834721496
36	-18.6982691889396	-49.4521218500289	-38.8071325839963	1.41059420611706	12.0555834721496
37	-18.2857691889396	-49.0396218500289	-38.3946325839963	1.82309420611706	12.4680834721496
38	-33.8965383778793	-77.3890539064156	-62.3347657150763	-5.45831104068222	9.59597715065708
39	-36.1465383778793	-79.6390539064156	-64.5847657150763	-7.70831104068222	7.34597715065708
40	-34.3840383778793	-77.8765539064156	-62.8222657150763	-5.94581104068222	9.10847715065707
41	-36.8965383778793	-80.3890539064156	-65.3347657150763	-8.45831104068222	6.59597715065708
42	-37.3965383778793	-80.8890539064156	-65.8347657150763	-8.95831104068222	6.09597715065708
43	-36.5215383778793	-80.0140539064156	-64.9597657150763	-8.08331104068222	6.97097715065708
44	-34.2715383778793	-77.7640539064156	-62.7097657150763	-5.83331104068222	9.22097715065708
45	-27.3965383778793	-70.8890539064157	-55.8347657150764	1.04168895931777	16.0959771506571
46	-37.3965383778793	-80.8890539064157	-65.8347657150763	-8.95831104068223	6.09597715065707
47	-16.3965383778793	-59.8890539064156	-44.8347657150764	12.0416889593178	27.0959771506571
48	-37.3965383778793	-80.8890539064157	-65.8347657150763	-8.95831104068223	6.09597715065707
49	-36.9840383778793	-80.4765539064156	-65.4222657150763	-8.54581104068222	6.50847715065707

Actuals and Interpolation
Time	Actual	Forecast
1	46	45.95244183468
2	40.5	40.45638364361
3	22.5	22.4728254462900
4	25	24.9687672592201
5	22.25	22.2199590695251
6	7	6.98365087358009
7	11	10.9780926872601
8	50.25	50.1872845185651
9	16.25	16.2197263132451
10	32.5	32.4519181330501
11	5.7525	5.72960743135642
12	7.75	7.72355174403519
13	14	27.3017308111177
14	3.5	21.8017308111392
15	1.25	3.80173081107135
16	3.0125	6.30173081107452
17	0.5	3.5517308110735
18	0	-11.6982691889900
19	0.875	-7.69826918897657
20	3.125	31.5517308111828
21	10	-2.44826918899326
22	0	13.8017308111198
23	21	-12.9457691890859
24	0	-10.9482691889868
25	0.4125	-4.69826918893965

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273755383viam73hwfhzejyr/1yoin1273755344.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273755383viam73hwfhzejyr/1yoin1273755344.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273755383viam73hwfhzejyr/2yoin1273755344.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273755383viam73hwfhzejyr/2yoin1273755344.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):

par10 <- '0.1'

par9 <- '3'

par8 <- 'dumresult'

par7 <- 'dum'

par6 <- '12'

par5 <- 'ZZZ'

par4 <- 'NA'

par3 <- 'NA'

par2 <- 'ETS'

par1 <- 'Input box'

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')

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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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