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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: Fri, 28 May 2010 08:36:05 +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/28/t1275035804lmi5y2wzws6x2yi.htm/, Retrieved Fri, 28 May 2010 10:36:47 +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/28/t1275035804lmi5y2wzws6x2yi.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

266.25 235.25 323.775 305.25 383.527 515.25 496.15 115.25 170.5 154.25 170 534.05 193.75 564.5 346 308.25 437.05 410.275 149.75 154.75 240.1 127.525 222.25 85.525 427.75 63.5 118.3 99.5 182.25 401 119.5 450.25 147.5 237 80.025 10.5 176.75 234 282.5 320 167.5 163.25 238.15 325.125 126.3 154.875 327.25 336.25 188 277.25

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	3 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	232.63050011459	-33.3978896257670	58.683885768475	406.577114460705	498.658889854947
52	232.63050011459	-34.7247227431761	57.8163162252361	407.444684003944	499.985722972356
53	232.63050011459	-36.0450034781348	56.9530310545947	408.307969174586	501.306003707315
54	232.63050011459	-37.3588279565075	56.0939674032264	409.167032825954	502.619828185688
55	232.63050011459	-38.6662899765594	55.2390639397422	410.021936289438	503.92729020574
56	232.63050011459	-39.967481087105	54.3882608035898	410.872739425591	505.228481316285
57	232.63050011459	-41.2624906623158	53.5414995561396	411.719500673041	506.523490891496
58	232.63050011459	-42.5514059733587	52.6987231338426	412.562277095338	507.812406202539
59	232.63050011459	-43.8343122570301	51.8598758033536	413.401124425827	509.095312486211
60	232.63050011459	-45.1112927815342	51.0249031185202	414.23609711066	510.372293010715

Actuals and Interpolation
Time	Actual	Forecast
1	266.25	NA
2	235.25	266.25
3	323.775	263.15
4	305.25	269.2125
5	383.527	272.81625
6	515.25	283.887325
7	496.15	307.0235925
8	115.25	325.93623325
9	170.5	304.867609925
10	154.25	291.4308489325
11	170	277.71276403925
12	534.05	266.941487635325
13	193.75	293.652338871793
14	564.5	283.662104984613
15	346	311.745894486152
16	308.25	315.171305037537
17	437.05	314.479174533783
18	410.275	326.736257080405
19	149.75	335.090131372364
20	154.75	316.556118235128
21	240.1	300.375506411615
22	127.525	294.347955770454
23	222.25	277.665660193408
24	85.525	272.124094174068
25	427.75	253.464184756661
26	63.5	270.892766280995
27	118.3	250.153489652895
28	99.5	236.968140687606
29	182.25	223.221326618845
30	401	219.124193956961
31	119.5	237.311774561265
32	450.25	225.530597105138
33	147.5	248.002537394624
34	237	237.952283655162
35	80.025	237.857055289646
36	10.5	222.073849760681
37	176.75	200.916464784613
38	234	198.499818306152
39	282.5	202.049836475537
40	320	210.094852827983
41	167.5	221.085367545185
42	163.25	215.726830790666
43	238.15	210.479147711600
44	325.125	213.246232940440
45	126.3	224.434109646396
46	154.875	214.620698681756
47	327.25	208.646128813580
48	336.25	220.506515932222
49	188	232.080864339
50	277.25	227.672777905100

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/28/t1275035804lmi5y2wzws6x2yi/1yz331275035761.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/28/t1275035804lmi5y2wzws6x2yi/1yz331275035761.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/28/t1275035804lmi5y2wzws6x2yi/298l61275035761.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/28/t1275035804lmi5y2wzws6x2yi/298l61275035761.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = b28acm ; par9 = 3 ; par10 = 0.1 ;

Parameters (R input):

par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = b28acm ; 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')

-SERVER-wessa.org

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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