Home » date » 2010 » Jun » 03 »

B11A,steven,coomans,thesis,per maand,aangepaste bron code

*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, 03 Jun 2010 11:37:31 +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/Jun/03/t12755651133qd40t3tc6rqubt.htm/, Retrieved Thu, 03 Jun 2010 13:38:36 +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/Jun/03/t12755651133qd40t3tc6rqubt.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,thesis,per maand,aangepaste bron code

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 0 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	2 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	-0.409614355842933	-27.7703048794371	-18.2998082836268	17.4805795719409	26.9510761677513
52	-1.13305993616309	-28.7559726322014	-19.1947117265745	16.9285918542483	26.4898527598752
53	-1.85650551648325	-29.7391744292328	-20.0880027677358	16.3749917347693	26.0261633962663
54	-2.57995109680341	-30.7199785591756	-20.9797260586551	15.8198238650483	25.5600763655688
55	-3.30339667712357	-31.6984502158672	-21.8699242272914	15.2631308730443	25.0916568616201
56	-4.02684225744372	-32.6746516912063	-22.758638004128	14.7049534892406	24.6209671763188
57	-4.75028783776388	-33.6486425528377	-23.6459063383543	14.1453306628266	24.1480668773099
58	-5.47373341808403	-34.6204798080893	-24.5317665050585	13.5842996688904	23.6730129719212
59	-6.19717899840419	-35.5902180554384	-25.416254204266	13.0218962074576	23.1958600586300
60	-6.92062457872435	-36.5579096246491	-26.2994036525703	12.4581544951216	22.7166604672004
61	-7.6440701590445	-37.5236047065981	-27.1812476680217	11.8931073499327	22.2354643885091
62	-8.36751573936466	-38.4873514737027	-28.0618177488709	11.3267862701416	21.7523199949733
63	-9.09096131968482	-39.4491961917679	-28.9411441467018	10.7592215073322	21.2672735523982
64	-9.81440690000498	-40.4091833239898	-29.8192559344355	10.1904421344256	20.7803695239798
65	-10.5378524803251	-41.3673556277758	-30.6961810696381	9.62047610898787	20.2916506671255
66	-11.2612980606453	-42.3237542449802	-31.5719464535221	9.04935033223157	19.8011581236896
67	-11.9847436409654	-43.2784187860931	-32.4465779859949	8.47709070406404	19.3089315041622
68	-12.7081892212856	-44.2313874088718	-33.3201006170729	7.90372217450168	18.8150089663006
69	-13.4316348016058	-45.1826968918555	-34.1925383949502	7.3292687917387	18.3194272886439
70	-14.1550803819259	-46.1323827031654	-35.0639145109849	6.75375374713302	17.8222219393135
71	-14.8785259622461	-47.080479064954	-35.9342513418396	6.17719941734741	17.3234271404619
72	-15.6019715425662	-48.0270190138354	-36.8035704889951	5.59962740386265	16.8230759287029
73	-16.3254171228864	-48.9720344575983	-37.6718928158333	5.02105857006048	16.3212002118255
74	-17.0488627032065	-49.9155562284787	-38.5392384824694	4.44151307605634	15.8178308220656

Actuals and Interpolation
Time	Actual	Forecast
1	62	61.9372766090424
2	30	53.6990754678702
3	31	42.9796103883129
4	50	40.114525849871
5	33	40.9963731977086
6	12	37.8577796120601
7	20	32.4404613343960
8	30	29.7292961955551
9	21.5	28.9511508961465
10	23	27.0735186847747
11	13.5	25.6690895351921
12	0.5	23.0728543902197
13	12	19.0939579445544
14	10	17.3437687869929
15	70.5	15.6935748408667
16	30	22.6810295141470
17	20.5	22.9798879626833
18	12	21.9019533689876
19	20	19.7975575336011
20	45	19.1137900736460
21	11.505	21.9892472510832
22	0	19.8030163895833
23	10	16.3260288698821
24	5.5	14.7224436931518
25	27.5	12.7195192553912
26	0.5	14.0475626980797
27	7	11.4428960881276
28	0	10.1022510897504
29	2.5	7.97630676854496
30	0	6.4925776516702
31	0	4.86789260599094
32	6.025	3.46884701988977
33	1	3.1001501018465
34	0	2.08521695385015
35	0	1.07236924189216
36	0	0.200095795363322
37	0	-0.551118035579414
38	2	-1.19807208691188
39	0	-1.47768404171922
40	6	-1.99604696781898
41	20	-1.60978211908969
42	0	0.665774884855646
43	0	-0.150066662923903
44	0	-0.85268576842576
45	7	-1.45779395065992
46	35	-1.00746539230566
47	0	3.26618032876242
48	0	2.08945636701586
49	0	1.07603751099060
50	1	0.203260318391797

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/03/t12755651133qd40t3tc6rqubt/1lpcl1275565048.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jun/03/t12755651133qd40t3tc6rqubt/1lpcl1275565048.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jun/03/t12755651133qd40t3tc6rqubt/2lpcl1275565048.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jun/03/t12755651133qd40t3tc6rqubt/2lpcl1275565048.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = ETS ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B11AEM ; par9 = 3 ; par10 = 0.1 ;

Parameters (R input):

par1 = Input box ; par2 = ETS ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B11AEM ; 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 <- 'ARIMA'

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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