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B611,steven,coomans,croston,thesis

*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 11:41:59 +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/t1273750955fvljrnzhjvzz7v4.htm/, Retrieved Thu, 13 May 2010 13:42:37 +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/t1273750955fvljrnzhjvzz7v4.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:

B611,steven,coomans,croston,thesis

Dataseries X:

» Textbox « » Textfile « » CSV «

10,65 34 81,75 106,5 0,525 24,025 5,25 9 12,8 25,05 0,3 75,75 54,75 1,526 1,02 3,752 17,25 9,2 50,25 2,25 3,95 60 55,8 6,75 61,95 7,025 85,75 18,525 6 25,35 46,775 51,025 30 3 30 44 80,75 27,5 39,725 29,25 32,725 56,25 28,65 51,75 32,26 72 65,4 33,75 77,85 10,875

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	42.0959719500054	-14.0381416645725	5.39184791983723	78.8000959801737	98.2300855645834
52	42.0959719500054	-14.3181140428322	5.20878382146695	78.983160078544	98.5100579428431
53	42.0959719500054	-14.5967038160953	5.02662375991164	79.1653201400992	98.7886477161062
54	42.0959719500054	-14.8739312676864	4.84535447261817	79.3465894273927	99.0658751676972
55	42.0959719500054	-15.149816189788	4.66496301817394	79.526980881837	99.3417600897989
56	42.0959719500054	-15.4243778999308	4.48543676552462	79.7065071344863	99.6163217999417
57	42.0959719500054	-15.6976352567783	4.30676338365294	79.885180516358	99.8895791567892
58	42.0959719500054	-15.9696066752433	4.12893083169456	80.0630130683163	100.161550575254
59	42.0959719500054	-16.2403101409702	3.9519273494688	80.2400165505421	100.432254040981
60	42.0959719500054	-16.5097632242148	3.77574144840292	80.416202451608	100.701707124226

Actuals and Interpolation
Time	Actual	Forecast
1	10.65	NA
2	34	10.65
3	81.75	12.985
4	106.5	19.8615
5	0.525	28.52535
6	24.025	25.725315
7	5.25	25.5552835
8	9	23.52475515
9	12.8	22.072279635
10	25.05	21.1450516715
11	0.3	21.53554650435
12	75.75	19.411991853915
13	54.75	25.0457926685235
14	1.526	28.0162134016712
15	1.02	25.3671920615040
16	3.752	22.9324728553536
17	17.25	21.0144255698183
18	9.2	20.6379830128364
19	50.25	19.4941847115528
20	2.25	22.5697662403975
21	3.95	20.5377896163578
22	60	18.879010654722
23	55.8	22.9911095892498
24	6.75	26.2719986303248
25	61.95	24.3197987672923
26	7.025	28.0828188905631
27	85.75	25.9770370015068
28	18.525	31.9543333013561
29	6	30.6113999712205
30	25.35	28.1502599740985
31	46.775	27.8702339766886
32	51.025	29.7607105790198
33	30	31.8871395211178
34	3	31.698425569006
35	30	28.8285830121054
36	44	28.9457247108949
37	80.75	30.4511522398054
38	27.5	35.4810370158248
39	39.725	34.6829333142424
40	29.25	35.1871399828181
41	32.725	34.5934259845363
42	56.25	34.4065833860827
43	28.65	36.5909250474744
44	51.75	35.796832542727
45	32.26	37.3921492884543
46	72	36.8789343596088
47	65.4	40.391040923648
48	33.75	42.8919368312832
49	77.85	41.9777431481549
50	10.875	45.5649688333394

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273750955fvljrnzhjvzz7v4/1oclq1273750916.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273750955fvljrnzhjvzz7v4/1oclq1273750916.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273750955fvljrnzhjvzz7v4/2hllb1273750916.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273750955fvljrnzhjvzz7v4/2hllb1273750916.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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