Home » date » 2010 » May » 13 »

B521,steven,coomans,thesis,ETS,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 13:11:30 +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/t1273756326nmowia96jees1rm.htm/, Retrieved Thu, 13 May 2010 15:12:09 +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/t1273756326nmowia96jees1rm.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:

B521,steven,coomans,thesis,ETS,per2maand

Dataseries X:

» Textbox « » Textfile « » CSV «

341.25 303.6875 357.5 295.075 386.5755 455.6625 424.926 506.751 433.9 466.3375 496.7 464.45 385.375 381.875 219.6375 268.975 292.2875 181.025 277.625 166.75 266 189.25 226.35 158.75 218.8125

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	7 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
26	204.960652278393	77.6260842593891	121.701053489901	288.220251066886	332.295220297397
27	204.960652278393	54.3913716649274	106.508691520352	303.412613036434	355.529932891859
28	204.960652278393	34.2910172628848	93.3657762141894	316.555528342597	375.630287293902
29	204.960652278393	16.3204116927826	81.6154288181103	328.305875738676	393.600892864004
30	204.960652278393	-0.0811883787451109	70.8909989740408	339.030305582746	410.002492935532
31	204.960652278393	-15.2646243633618	60.9630838133966	348.95822074339	425.185928920148
32	204.960652278393	-29.4667107230443	51.6768386824577	358.244465874329	439.388015279831
33	204.960652278393	-42.8562248217712	42.921905994686	366.999398562101	452.777529378558
34	204.960652278393	-55.5584844995952	34.6163448188313	375.304959737955	465.479789056382
35	204.960652278393	-67.6695673954692	26.6973333634805	383.223971193306	477.590871952256
36	204.960652278393	-79.2650550939144	19.1154514868688	390.805853069918	489.186359650701
37	204.960652278393	-90.4056775277627	11.8309900203026	398.090314536484	500.326982084549
38	204.960652278393	-101.141103409203	4.81147233851374	405.109832218273	511.06240796599
39	204.960652278393	-111.512570738815	-1.97006563774505	411.891370194532	521.433875295601
40	204.960652278393	-121.554763802945	-8.53630274432021	418.457607301107	531.476068359732
41	204.960652278393	-131.297184791079	-14.9065294260454	424.827833982832	541.218489347866
42	204.960652278393	-140.765177017756	-21.0973167855944	431.018621342381	550.686481574542
43	204.960652278393	-149.98070216538	-27.1230247952366	437.044329352023	559.902006722167
44	204.960652278393	-158.96294017784	-32.9961945461918	442.917499102978	568.884244734626
45	204.960652278393	-167.728758891445	-38.7278553237938	448.64915988058	577.650063448232
46	204.960652278393	-176.293086391788	-44.3277680785565	454.249072635343	586.214390948575
47	204.960652278393	-184.669209644254	-49.8046206901678	459.725925246954	594.59051420104
48	204.960652278393	-192.869016491712	-55.1661862012753	465.087490758062	602.790321048498
49	204.960652278393	-200.903193617742	-60.4194522586833	470.34075681547	610.824498174528

Actuals and Interpolation
Time	Actual	Forecast
1	341.25	333.775928269088
2	303.6875	338.492504687478
3	357.5	316.528510995532
4	295.075	342.383916967292
5	386.5755	312.529223276755
6	455.6625	359.256754833345
7	424.926	420.094418776242
8	506.751	423.143429126151
9	433.9	475.904696528962
10	466.3375	449.397276177252
11	496.7	460.087548237949
12	464.45	483.19214675098
13	385.375	471.364755928644
14	381.875	417.100190422978
15	219.6375	394.871034955396
16	268.975	284.288425042335
17	292.2875	274.624758163045
18	181.025	285.770981350566
19	277.625	219.670140557742
20	166.75	256.243046627923
21	266	199.767699784603
22	189.25	241.56415512763
23	226.35	208.550863403060
24	158.75	219.783159661820
25	218.8125	181.267666104251

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273756326nmowia96jees1rm/1qhdr1273756283.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273756326nmowia96jees1rm/1qhdr1273756283.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273756326nmowia96jees1rm/21qcu1273756283.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273756326nmowia96jees1rm/21qcu1273756283.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 = ETS ; 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 <- 'Croston'

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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