Home » date » 2010 » May » 13 »

B521,steven,coomans,thesis,croston

*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 11:35:04 +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/t12737505503yu72c62f4r2qnp.htm/, Retrieved Thu, 13 May 2010 13:35:52 +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/t12737505503yu72c62f4r2qnp.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,croston

Dataseries X:

» Textbox « » Textfile « » CSV «

387 295,5 343,35 264,025 322,5 392,5 315,75 274,4 361,875 411,276 518,775 392,55 467 382,852 449,25 564,252 417 450,8 538,675 394 532 461,4 523 405,9 386,25 384,5 382 381,75 151,5 287,775 247,6 290,35 266,55 318,025 213,3 148,75 273 282,25 191,25 142,25 259,25 272,75 173,75 204,75 185,525 267,175 190,25 127,25 183,5 254,125

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	4 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	233.906554717221	62.4929529129367	121.825224916933	345.987884517508	405.320156521505
52	233.906554717221	61.638016927081	121.266212322949	346.546897111492	406.17509250736
53	233.906554717221	60.787302924741	120.709960335602	347.10314909884	407.0258065097
54	233.906554717221	59.9407489678633	120.156428455775	347.656680978666	407.872360466578
55	233.906554717221	59.0982946181672	119.605577165002	348.207532269439	408.714814816274
56	233.906554717221	58.2598808867905	119.057367892538	348.755741541903	409.553228547651
57	233.906554717221	57.4254501860877	118.511762983846	349.301346450595	410.387659248354
58	233.906554717221	56.5949462834697	117.968725670413	349.844383764028	411.218163150972
59	233.906554717221	55.7683142571807	117.428220040832	350.384889393609	412.044795177261
60	233.906554717221	54.945500453914	116.890211013092	350.922898421350	412.867608980527

Actuals and Interpolation
Time	Actual	Forecast
1	387	NA
2	295.5	387
3	343.35	377.85
4	264.025	374.4
5	322.5	363.3625
6	392.5	359.27625
7	315.75	362.598625
8	274.4	357.9137625
9	361.875	349.56238625
10	411.276	350.793647625
11	518.775	356.8418828625
12	392.55	373.03519457625
13	467	374.986675118625
14	382.852	384.188007606763
15	449.25	384.054406846086
16	564.252	390.573966161478
17	417	407.94176954533
18	450.8	408.847592590797
19	538.675	413.042833331717
20	394	425.606049998546
21	532	422.445444998691
22	461.4	433.400900498822
23	523	436.20081044894
24	405.9	444.880729404046
25	386.25	440.982656463641
26	384.5	435.509390817277
27	382	430.408451735549
28	381.75	425.567606561994
29	151.5	421.185845905795
30	287.775	394.217261315216
31	247.6	383.573035183694
32	290.35	369.975731665325
33	266.55	362.013158498792
34	318.025	352.466842648913
35	213.3	349.022658384022
36	148.75	335.450392545619
37	273	316.780353291058
38	282.25	312.402317961952
39	191.25	309.387086165757
40	142.25	297.573377549181
41	259.25	282.041039794263
42	272.75	279.761935814837
43	173.75	279.060742233353
44	204.75	268.529668010018
45	185.525	262.151701209016
46	267.175	254.489031088114
47	190.25	255.757627979303
48	127.25	249.206865181373
49	183.5	237.011178663235
50	254.125	231.660060796912

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737505503yu72c62f4r2qnp/1j7kb1273750500.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737505503yu72c62f4r2qnp/1j7kb1273750500.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737505503yu72c62f4r2qnp/2j7kb1273750500.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737505503yu72c62f4r2qnp/2j7kb1273750500.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = Croston ; 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):

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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