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B11A,steven,coomans,ETS,thesis,per3maand

*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 13:51:35 +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/t127375873810psbybth4v9ruc.htm/, Retrieved Thu, 13 May 2010 15:52:21 +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/t127375873810psbybth4v9ruc.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,ETS,thesis,per3maand

Dataseries X:

» Textbox « » Textfile « » CSV «

41 31.66666667 23.83333333 12.33333333 30.83333333 20.83333333 25.50166667 5.166666667 11.66666667 0.833333333 2.341666667 0 0.666666667 8.666666667 2.333333333 11.66666667 0.275

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
18	-0.354618308979576	-13.4745685128728	-8.9332926398818	8.22405602192264	12.7653318949137
19	-1.20778653470233	-14.3277372953810	-9.78646122966695	7.37088816026229	11.9121642259763
20	-1.98551809456441	-15.1054697751202	-10.5641933910043	6.59315720187552	11.1344335859913
21	-2.69448305712276	-15.8144360675430	-11.2731592231143	5.88419310886881	10.4254699532975
22	-3.34076172713647	-16.4607165068049	-11.9194390499773	5.23791559570433	9.77919305253198
23	-3.92989679215786	-17.0498537961597	-12.5085755694121	4.64878198509637	9.19006021184397
24	-4.46694085835052	-17.5869005468635	-13.0456213909122	4.11173967421119	8.65301883016244
25	-4.95649978321656	-18.0764626133988	-13.5351823700054	3.62218280357225	8.16346304696565
26	-5.40277217686778	-18.5227385967577	-13.9814571108403	3.17591275710473	7.71719424302212
27	-5.80958541061629	-18.9295558546482	-14.3882729758339	2.76910215460128	7.31038503341566
28	-6.18042844170519	-19.3004033275157	-14.7591189112457	2.3982620278353	6.93954644410536
29	-6.51848173569468	-19.6384614619544	-15.0971753702347	2.06021189884537	6.601497990565
30	-6.82664454312722	-19.9466294881816	-15.4053415900537	1.75205250379925	6.29334040192713
31	-7.1075597644049	-20.2275502855488	-15.6862604573403	1.47114092853055	6.01243075673899
32	-7.36363661612802	-20.4836330493705	-15.9423411747769	1.21506794252091	5.75635981711445
33	-7.59707129328852	-20.7170739534929	-16.1757799235290	0.981637336951906	5.5229313669158
34	-7.80986580452356	-20.9298749858293	-16.3885786986830	0.768847089635902	5.31014337678219
35	-8.00384514196633	-21.1238611184188	-16.5825624792334	0.57487219530076	5.11617083448614
36	-8.18067293294808	-21.3006959592754	-16.7593948798806	0.398049013984403	4.93935009337925
37	-8.3418657077853	-21.4618960202758	-16.9205924188838	0.236861003313207	4.7781646047052
38	-8.48880590601696	-21.6088437234593	-17.0675375243397	0.0899257123058117	4.63123191142541
39	-8.62275373263736	-21.7427992572944	-17.2014903904369	-0.0440170748378126	4.49729179201965
40	-8.74485796600747	-21.8649113846005	-17.3235997853744	-0.166116146640571	4.3751954525856
41	-8.85616581013664	-21.9762272948239	-17.4349129036391	-0.277418716634203	4.26389567455067

Actuals and Interpolation
Time	Actual	Forecast
1	41	33.3763873532711
2	31.66666667	29.6246335440404
3	23.83333333	26.2043027371594
4	12.33333333	23.0857558185012
5	30.83333333	20.2411085776598
6	20.83333333	17.6509908453781
7	25.50166667	15.2895321468614
8	5.166666667	13.1385354672161
9	11.66666667	11.1752719285065
10	0.833333333	9.3864204889596
11	2.341666667	7.75405731874686
12	0	6.2657718954017
13	0.666666667	4.90837511570664
14	8.666666667	3.6707589378187
15	2.333333333	2.54391483550720
16	11.66666667	1.51620952168044
17	0.275	0.581334283101121

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t127375873810psbybth4v9ruc/1gg0r1273758692.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t127375873810psbybth4v9ruc/1gg0r1273758692.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t127375873810psbybth4v9ruc/28pzc1273758692.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t127375873810psbybth4v9ruc/28pzc1273758692.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):

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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