Home » date » 2010 » May » 13 »

B611,steven,coomans,thesis,ETS

*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 12:15:19 +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/t12737529767tdocdssrulouwr.htm/, Retrieved Thu, 13 May 2010 14:16:19 +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/t12737529767tdocdssrulouwr.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,thesis,ETS

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	11 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	34.8127646314641	-19.7920260046609	-0.891388851939396	70.5169181148676	89.4175552675891
52	34.8127646314641	-19.7945620893357	-0.893047108576226	70.5185763715044	89.4200913522639
53	34.8127646314641	-19.7970981482013	-0.894705348337332	70.5202346112656	89.4226274111295
54	34.8127646314641	-19.7996341812646	-0.896363571227262	70.5218928341555	89.4251634441928
55	34.8127646314641	-19.8021701885326	-0.898021777250534	70.5235510401787	89.4276994514609
56	34.8127646314641	-19.8047061700123	-0.89967996641169	70.5252092293399	89.4302354329405
57	34.8127646314641	-19.8072421257104	-0.901338138715239	70.5268674016434	89.4327713886386
58	34.8127646314641	-19.8097780556341	-0.902996294165739	70.528525557094	89.4353073185623
59	34.8127646314641	-19.8123139597901	-0.904654432767693	70.5301836956959	89.4378432227183
60	34.8127646314641	-19.8148498381854	-0.906312554525634	70.5318418174539	89.4403791011136
61	34.8127646314641	-19.8173856908269	-0.907970659444096	70.5334999223723	89.4429149537551
62	34.8127646314641	-19.8199215177216	-0.909628747527591	70.5351580104558	89.4454507806498
63	34.8127646314641	-19.8224573188764	-0.911286818780667	70.5368160817089	89.4479865818046
64	34.8127646314641	-19.8249930942982	-0.91294487320782	70.538474136136	89.4505223572264
65	34.8127646314641	-19.8275288439938	-0.914602910813592	70.5401321737418	89.453058106922
66	34.8127646314641	-19.8300645679703	-0.916260931602487	70.5417901945307	89.4555938308986
67	34.8127646314641	-19.8326002662346	-0.917918935579046	70.5434481985073	89.4581295291628
68	34.8127646314641	-19.8351359387935	-0.91957692274778	70.545106185676	89.4606652017217
69	34.8127646314641	-19.8376715856539	-0.921234893113215	70.5467641560414	89.4632008485821
70	34.8127646314641	-19.8402072068229	-0.922892846679865	70.548422109608	89.465736469751
71	34.8127646314641	-19.8427428023072	-0.924550783452247	70.5500800463805	89.4682720652354
72	34.8127646314641	-19.8452783721138	-0.926208703434874	70.5517379663631	89.470807635042
73	34.8127646314641	-19.8478139162496	-0.927866606632271	70.5533958695605	89.4733431791778
74	34.8127646314641	-19.8503494347215	-0.929524493048973	70.5550537559772	89.4758786976497

Actuals and Interpolation
Time	Actual	Forecast
1	10.65	11.3447238275332
2	34	34.0202876898303
3	81.75	80.394008519981
4	106.5	104.461728571947
5	0.525	1.51024885747802
6	24.025	24.3449175147126
7	5.25	6.10102813170674
8	9	9.74297366894733
9	12.8	13.4323962225627
10	25.05	25.3271480416495
11	0.3	1.29132499835047
12	75.75	74.5430992988187
13	54.75	54.1692693680278
14	1.526	2.48213334115502
15	1.02	1.99046641401920
16	3.752	4.64256354643977
17	17.25	17.7434661387314
18	9.2	9.92884171115339
19	50.25	49.7607775635948
20	2.25	3.18356297838536
21	3.95	4.83254096458096
22	60	59.2038839531516
23	55.8	55.1395593527327
24	6.75	7.55013353908689
25	61.95	61.1045586779287
26	7.025	7.817053763127
27	85.75	84.1965045872622
28	18.525	18.9797298623388
29	6	6.8227878984193
30	25.35	25.5966143213555
31	46.775	46.3822925793737
32	51.025	50.5102268038572
33	30	30.1128301008900
34	3	3.91120762158714
35	30	30.1059458610932
36	44	43.6876743593134
37	80.75	79.3472312750332
38	27.5	27.6902669964837
39	39.725	39.5536267539573
40	29.25	29.3886144748858
41	32.725	32.7602726524212
42	56.25	55.591319459741
43	28.65	28.8093454557490
44	51.75	51.2297172974473
45	32.26	32.3159796143125
46	72	70.8921850090827
47	65.4	64.5012300391254
48	33.75	33.7767127288168
49	77.85	76.6044993211423
50	10.875	11.5642347595514

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737529767tdocdssrulouwr/1q0r11273752906.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737529767tdocdssrulouwr/1q0r11273752906.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737529767tdocdssrulouwr/2q0r11273752906.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737529767tdocdssrulouwr/2q0r11273752906.ps (open in new window)

Parameters (Session):

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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by