Home » date » 2010 » May » 13 »

FM50,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 12:19:31 +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/May/13/t1273753206c14djrempqt4dm8.htm/, Retrieved Thu, 13 May 2010 14:20:09 +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/t1273753206c14djrempqt4dm8.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:

FM50,steven,coomans,thesis,Arima

Dataseries X:

» Textbox « » Textfile « » CSV «

1216.67 1186.17 1217.475 1096.95 1685.6 1758.5 1786.6 2049.895 1845.895 2015.02 1609.63 918.725 1240.96 1671.785 2451.83 1886.14 2110.66 1856.87 1775.765 1569.625 1835.69 2041.46 1667.035 948.25 1365.66 1681.025 1661.9 2194.88 2051.025 2365.845 2398.5 2181.85 2626.77 2529.72 1700.3 605.38 1200.495 1597.02 1174.955 1612.88 1683.55 2260.955 2455.335 2365.62 2417.755 2308.785 1629.94 1053.275 1330.235 1543.85

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

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	1352.90314964095	799.929212822542	991.332926909896	1714.47337237200	1905.87708645936
52	1632.45214360347	971.761606282007	1200.44982155901	2064.45446564794	2293.14268092494
53	1706.13406644674	1023.42970059779	1259.73767561552	2152.53045727796	2388.83843229569
54	2045.89668698965	1362.99125988525	1599.36882926989	2492.42454470940	2728.80211409405
55	2166.02048151567	1477.32137008431	1715.70433723654	2616.3366257948	2854.71959294703
56	2121.63579972379	1418.12945408788	1661.6377254062	2581.63387404138	2825.1421453597
57	2147.29373435019	1426.99975641301	1676.31881728080	2618.26865141957	2867.58771228736
58	2080.88184621203	1348.04875430966	1601.70804321444	2560.05564920962	2813.7149381144
59	1702.40538632711	962.981688898596	1218.92221800621	2185.88855464801	2441.82908375562
60	1380.0278487736	638.393783091448	895.099398336176	1864.95629921103	2121.66191445575
61	1524.38839977378	782.48646638521	1039.28480005748	2009.49199949008	2266.29033316234
62	1636.51533051232	894.568023291878	1151.38206244204	2121.64859858260	2378.46263773276
63	1530.49842208469	719.83537221453	1000.43434494602	2060.56249922337	2341.16147195485
64	1682.61797293894	840.144448357797	1131.75414438231	2233.48180149557	2525.09149752008
65	1723.83170477436	872.13484440893	1166.93706110728	2280.72634844143	2575.52856513978
66	1910.62805845356	1058.24420603745	1353.28421482923	2467.97190207789	2763.01191086967
67	1977.89729089021	1124.91982527661	1420.16530445889	2535.62927732153	2830.87475650380
68	1955.31180300575	1099.46907795249	1395.70632412785	2514.91728188364	2811.15452805901
69	1970.44742605569	1110.58556274429	1408.21397392549	2532.68087818589	2830.30928936709
70	1934.93821345813	1071.61497368108	1370.4414889089	2499.43493800737	2798.26145323519
71	1728.99024613106	863.588574694478	1163.13450816722	2294.84598409491	2594.39191756765
72	1553.27123317061	687.032742577854	986.868328570986	2119.67413777023	2419.50972376337
73	1631.60840473080	765.203811421409	1065.09689140253	2198.11991805906	2498.01299804019
74	1692.30587914206	825.901284561393	1125.79436498255	2258.81739330157	2558.71047372273

Actuals and Interpolation
Time	Actual	Forecast
1	1216.67	1544.42911895378
2	1186.17	1366.3834494132
3	1217.475	1394.74447514204
4	1096.95	1435.70978225614
5	1685.6	1515.17300887526
6	1758.5	1913.84222078324
7	1786.6	1957.10781265414
8	2049.895	2003.16986500382
9	1845.895	2094.36317239399
10	2015.02	1946.73777811992
11	1609.63	1915.17835198758
12	918.725	1514.01237467182
13	1240.96	1137.92569933516
14	1671.785	1425.79213659270
15	2451.83	1747.14542398917
16	1886.14	2134.15076490796
17	2110.66	1997.1087832377
18	1856.87	1923.93219142466
19	1775.765	1716.64848512266
20	1569.625	1805.52638387444
21	1835.69	1484.35743354946
22	2041.46	1879.55614969438
23	1667.035	1735.86760859793
24	948.25	1248.06282806837
25	1365.66	1208.49264795729
26	1681.025	1648.62405416901
27	1661.9	2137.48151321946
28	2194.88	1538.92416659111
29	2051.025	2262.95786264119
30	2365.845	1895.75731478471
31	2398.5	2133.06714654175
32	2181.85	2005.89702658791
33	2626.77	2019.35823239364
34	2529.72	2287.02645105581
35	1700.3	1877.51131114692
36	605.38	1044.00598415714
37	1200.495	856.884940781561
38	1597.02	1388.94763408102
39	1174.955	1582.51626983244
40	1612.88	1625.07070253864
41	1683.55	1708.99836048230
42	2260.955	2015.60437152167
43	2455.335	2312.82789083737
44	2365.62	2274.66272273801
45	2417.755	2485.27099441534
46	2308.785	2262.79978259441
47	1629.94	1758.7604073078
48	1053.275	1007.45449610215
49	1330.235	1365.70594512373
50	1543.85	1566.13002581225

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273753206c14djrempqt4dm8/1x1yu1273753165.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273753206c14djrempqt4dm8/1x1yu1273753165.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273753206c14djrempqt4dm8/2paxf1273753165.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273753206c14djrempqt4dm8/2paxf1273753165.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