Home » date » 2010 » May » 13 »

B580,steven,coomans,thesis,Arima

*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 12:10:42 +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/t1273752685m1u77qlii4i0esj.htm/, Retrieved Thu, 13 May 2010 14:11:28 +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/t1273752685m1u77qlii4i0esj.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:

B580,steven,coomans,thesis,Arima

Dataseries X:

» Textbox « » Textfile « » CSV «

209 175 247.5 177 188.775 194.825 182.275 145.25 286.3 257.75 335 234.15 276.275 327.052 375.325 199.75 215.875 225 228.1 128.5 242.5 327.275 346.8 221.175 245.275 230.725 335.3 97.25 254.5 71.25 273.575 98.325 184.55 203.025 121.655 135 98.75 69.1 256.525 97.775 202.7 81.9 165.25 75.825 300 238.5 194.5 140.75 211.75 274.8

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	290.092600011906	164.711430125015	208.110259608816	372.074940414995	415.473769898796
52	250.182660915376	118.321277649685	163.963136734334	336.402185096417	382.044044181066
53	256.128240351882	118.090524378032	165.870229283377	346.386251420387	394.165956325732
54	254.748706073570	110.799417552184	160.625325481500	348.87208666564	398.697994594956
55	227.421225705653	77.793740595633	129.585071927426	325.257379483880	377.048710815672
56	221.559691878803	66.461752063714	120.146600030886	322.972783726721	376.657631693893
57	344.337146831027	183.955235228063	239.469051538935	449.205242123119	504.719058433992
58	320.349189950263	154.851926516825	212.136345877905	428.562034022621	485.846453383702
59	321.405086669725	150.945910212448	209.947822060074	432.862351279375	491.864263127001
60	261.401619369206	86.1209376373154	146.791741914777	376.011496823635	436.682301101096
61	325.611364128963	145.929840264083	208.123930673641	443.098797584284	505.292887993842
62	365.487347271551	181.250797659097	245.021542568622	485.95315197448	549.723896884005
63	320.748588365127	98.7753658236304	175.608099508750	465.889077221504	542.721810906624
64	320.748588365127	88.6462658592474	168.985037034488	472.512139695766	552.850910871007
65	320.748588365127	78.9410932377222	162.639165750575	478.858010979679	562.556083492532
66	320.748588365127	69.6106953920504	156.53834652094	484.958830209314	571.886481338204
67	320.748588365127	60.614742289452	150.656208954567	490.840967775687	580.882434440802
68	320.748588365127	51.9196567723955	144.970798128234	496.526378602021	589.577519957859
69	320.748588365127	43.4971302112408	139.463604023939	502.033572706315	598.000046519013
70	320.748588365127	35.3230330348051	134.118849163133	507.378327567121	606.174143695449
71	320.748588365127	27.3765988658068	128.922955141557	512.574221588698	614.120577864447
72	320.748588365127	19.6398026913181	123.864136036754	517.6330406935	621.857374038936
73	320.748588365127	12.0968794809507	118.932083649426	522.565093080829	629.400297249304
74	320.748588365127	4.73394632414676	114.117720432279	527.379456297975	636.763230406108

Actuals and Interpolation
Time	Actual	Forecast
1	209	208.791000220112
2	175	198.426779339075
3	247.5	203.378258089298
4	177	206.916502799148
5	188.775	198.493885113137
6	194.825	196.263512901256
7	182.275	193.640749579656
8	145.25	183.618255052768
9	286.3	194.973766357325
10	257.75	219.947196737977
11	335	245.338121076719
12	234.15	257.514432415318
13	276.275	258.505048267576
14	327.052	255.53357353954
15	375.325	315.322448001021
16	199.75	293.272867505843
17	215.875	268.44574756024
18	225	254.391240106182
19	228.1	239.544311419872
20	128.5	213.489073070484
21	242.5	254.053084952268
22	327.275	243.25421812959
23	346.8	305.330568389782
24	221.175	267.255114086196
25	245.275	270.024693610743
26	230.725	298.681762847649
27	335.3	287.508494249009
28	97.25	214.89352137417
29	254.5	186.707631098946
30	71.25	207.601739355676
31	273.575	173.554668621071
32	98.325	154.336231589396
33	184.55	166.278743842984
34	203.025	228.807116636272
35	121.655	209.003839800871
36	135	135.659535200178
37	98.75	139.943125963237
38	69.1	92.208844377748
39	256.525	147.122521391435
40	97.775	84.4553816480669
41	202.7	184.750081665235
42	81.9	71.8893513924464
43	165.25	199.790371621642
44	75.825	107.963672206709
45	300	136.157711472038
46	238.5	164.294723220295
47	194.5	142.880720140488
48	140.75	196.613502921185
49	211.75	152.099346335145
50	274.8	175.013624568627

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273752685m1u77qlii4i0esj/1m0bd1273752636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752685m1u77qlii4i0esj/1m0bd1273752636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752685m1u77qlii4i0esj/2f9sg1273752636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752685m1u77qlii4i0esj/2f9sg1273752636.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 = ARIMA ; 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

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