Home » date » 2010 » May » 13 »

B11A,steven,coomans,ETS,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 11:53:59 +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/t12737517037o5w3km0c070qr4.htm/, Retrieved Thu, 13 May 2010 13:55:06 +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/t12737517037o5w3km0c070qr4.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

Dataseries X:

» Textbox « » Textfile « » CSV «

62 30 31 50 33 12 20 30 21,5 23 13,5 0,5 12 10 70,5 30 20,5 12 20 45 11,505 10 5,5 27,5 0,5 7 0 2,5 0 0 6,025 1 0 0 0 0 2 0 6 20 0 0 0 7 35 0 0 0 1

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	5 seconds
R Server	wessa.org @ wessa.org
R Framework error message	Warning: there are blank lines in the 'Data' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
50	1.87956721447306	-23.8069620759050	-14.9159515221716	18.6750859511178	27.5660965048511
51	1.78551613369694	-23.9026361864282	-15.0110638450572	18.5820961124510	27.4736684538221
52	1.69738751072888	-23.9935030665959	-15.1009829181706	18.4957579396283	27.3882780880536
53	1.61480840454364	-24.0801282797117	-15.1862076314752	18.4158244405625	27.3097450887990
54	1.53742935845524	-24.1630093555852	-15.2671842614684	18.3420429783789	27.2378680724957
55	1.46492292129280	-24.2425839255521	-15.3443123022585	18.2741581448441	27.1724297681377
56	1.39698226169893	-24.3192368829885	-15.4179496272134	18.2119141506112	27.1132014063863
57	1.33331986968662	-24.3933066896779	-15.4884170619091	18.1550568012823	27.0599464290511
58	1.27366633996000	-24.4650909321828	-15.5560024383813	18.1033351183013	27.0124236121028
59	1.21776923185015	-24.5348512187125	-15.6209641916313	18.0565026553316	26.9703896824128
60	1.16539200104145	-24.6028174953832	-15.6835345516441	18.0143185537270	26.9336014974661
61	1.11631299856790	-24.6691918508646	-15.7439223775954	17.9765483747311	26.9018178480004
62	1.07032453284323	-24.7341518698986	-15.8023156752640	17.9429647409504	26.8748009355851
63	1.02723199075563	-24.7978535888315	-15.8588838337706	17.9133478152818	26.8523175703428
64	0.986853014107674	-24.8604340999252	-15.9137796135082	17.8874856417235	26.8341401281406
65	0.949016727916398	-24.9220138456636	-15.9671409134212	17.865174369254	26.8200473014964
66	0.913563017307794	-24.9826986394163	-16.0190923425383	17.8462183771539	26.8098246740319
67	0.88034184994575	-25.0425814445669	-16.0697466178132	17.8304303177047	26.8032651444584
68	0.849212641128065	-25.1017439404718	-16.1192058078129	17.8176310900690	26.8001692227280
69	0.820043658862796	-25.1602579003239	-16.1675624395778	17.8076497573034	26.8003452180494
70	0.792711466407323	-25.2181864030832	-16.2149004840178	17.8003234168324	26.8036093358978
71	0.767100399911097	-25.2755848990752	-16.2612962334757	17.7954970332979	26.8097856988974
72	0.743102078951566	-25.3325021465803	-16.3068190835507	17.7930232414539	26.8187063044834
73	0.720614947891971	-25.3889810347315	-16.3515322299147	17.7927621256986	26.8302109305154

Actuals and Interpolation
Time	Actual	Forecast
1	62	46.1194685774281
2	30	43.4480414653215
3	31	40.7881739074814
4	50	38.2714612582087
5	33	36.0352904013211
6	12	33.8753635916337
7	20	31.7001481726198
8	30	29.6536182209427
9	21.5	27.7804909323141
10	23	25.9775089806221
11	13.5	24.2885116537528
12	0.5	22.6365631330849
13	12	20.9632090618188
14	10	19.4081667572549
15	70.5	17.9134401231166
16	30	16.9364640176437
17	20.5	15.9292589572117
18	12	14.9725132511041
19	20	14.0375826266982
20	45	13.2163924250250
21	11.505	12.6611764445858
22	10	12.0183570907735
23	5.5	11.4051988134497
24	27.5	10.7941085340861
25	0.5	10.3665380157253
26	7	9.83282348253668
27	0	9.34710124886339
28	2.5	8.83282469026795
29	0	8.33750394871454
30	0	7.83427153956744
31	6.025	7.33454658875676
32	1	6.88467853344068
33	0	6.42420593705413
34	0	5.96620540681794
35	0	5.51585199610374
36	0	5.07436049676882
37	2	4.64283092786269
38	0	4.23708022894056
39	6	3.83494429914164
40	20	3.48937965938113
41	0	3.28022228474214
42	0	3.00069944464174
43	0	2.72829545282175
44	7	2.46357914613309
45	35	2.25895100269802
46	0	2.2936847778023
47	0	2.19179810602918
48	0	2.08830344842662
49	1	1.98370396190309

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737517037o5w3km0c070qr4/1znkc1273751633.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737517037o5w3km0c070qr4/1znkc1273751633.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737517037o5w3km0c070qr4/29w1f1273751633.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737517037o5w3km0c070qr4/29w1f1273751633.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = ETS ; 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 <- 'ETS'

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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