Home » date » 2010 » May » 13 »

B382,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 12:00:41 +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/t12737520872zuu9p6ny05wbew.htm/, Retrieved Thu, 13 May 2010 14:01:30 +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/t12737520872zuu9p6ny05wbew.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:

B382,steven,coomans,thesis,ARIMA

Dataseries X:

» Textbox « » Textfile « » CSV «

283.25 286.75 230.25 200.5 297.95 329.5 289.75 223.775 281.78 265.8 256.75 89.275 225.5 124.25 230 286.525 227 218.3 334.525 128.95 195.5 106.056 173.525 114.75 131.05 141.25 160.25 145.5 297.5 179.25 137 158.6 55.6 15.25 67.75 93 126.75 160 150.525 239.25 165.05 215.81 166 79.05 204.25 102 87.025 72.175 176.75 188.975

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	3 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	149.807810119189	19.1997587728742	64.4077956312295	235.207824607149	280.415861465504
52	149.807810119189	12.2174896670777	59.8423352584018	239.773284979976	287.398130571300
53	149.807810119189	5.57283000774817	55.4976258371605	244.117994401218	294.04279023063
54	149.807810119189	-0.778918025693628	51.3444410335569	248.271179204821	300.394538264072
55	149.807810119189	-6.87338179296694	47.3594853692124	252.256134869166	306.489002031345
56	149.807810119189	-12.7395033149213	43.523834673876	256.091785564502	312.355123553299
57	149.807810119189	-18.4011739163849	39.8218672499153	259.793752988463	318.016794154763
58	149.807810119189	-23.8783882844637	36.2405092741553	263.375110964223	323.494008522842
59	149.807810119189	-29.1880801172800	32.7686883965375	266.846931841841	328.803700355658
60	149.807810119189	-34.3447405750089	29.3969293567227	270.218690881655	333.960360813387
61	149.807810119189	-39.3608847877966	26.1170489509590	273.498571287419	338.976505026175
62	149.807810119189	-44.2474096983806	22.9219220513689	276.693698187009	343.863029936759
63	149.807810119189	-49.0138726623384	19.8052994390386	279.810320799340	348.629492900717
64	149.807810119189	-53.6687112529619	16.7616640813354	282.853956157043	353.28433149134
65	149.807810119189	-58.2194187604565	13.7861163791495	285.829503859229	357.835038998835
66	149.807810119189	-62.6726858350926	10.8742815514155	288.741338686963	362.288306073471
67	149.807810119189	-67.0345159304329	8.0222341508431	291.593386087535	366.650136168811
68	149.807810119189	-71.310320237061	5.22643599008885	294.389184248289	370.925940475439
69	149.807810119189	-75.5049963918406	2.48368467653765	297.131935561840	375.120616630219
70	149.807810119189	-79.6229942282432	-0.208929379526012	299.824549617904	379.238614466621
71	149.807810119189	-83.6683710838253	-2.85405912853298	302.469679366911	383.283991322203
72	149.807810119189	-87.6448386231989	-5.45413149752468	305.069751735903	387.260458861577
73	149.807810119189	-91.5558027150758	-8.01137346435084	307.626993702729	391.171422953454
74	149.807810119189	-95.4043975827155	-10.5278343874468	310.143454625825	395.020017821094

Actuals and Interpolation
Time	Actual	Forecast
1	283.25	282.966750204949
2	286.75	283.840363485213
3	230.25	281.694091305162
4	200.5	260.877303843805
5	297.95	240.477467725152
6	329.5	260.301637689324
7	289.75	283.473863134466
8	223.775	285.501477042126
9	281.78	265.016411510642
10	265.8	270.574646930639
11	256.75	268.990967395394
12	89.275	264.927551074807
13	225.5	206.724868986268
14	124.25	212.945092462218
15	230	183.557737684414
16	286.525	198.945546067943
17	227	227.963047580939
18	218.3	227.643960820815
19	334.525	224.548070833112
20	128.95	260.986363812137
21	195.5	217.239187431134
22	106.056	210.036412890030
23	173.525	175.584926903992
24	114.75	174.902418000307
25	131.05	154.972316858305
26	141.25	147.046214989471
27	160.25	145.125774315763
28	145.5	150.13683381711
29	297.5	148.600527064262
30	179.25	197.934895247504
31	137	191.744090918481
32	158.6	173.605912966515
33	55.6	168.634053626825
34	15.25	131.182855791850
35	67.75	92.7712074566496
36	93	84.481013833961
37	126.75	87.3035812463404
38	160	100.373232251632
39	150.525	120.129171311937
40	239.25	130.200120349172
41	165.05	166.331255043692
42	215.81	165.906741062477
43	166	182.441022220241
44	79.05	176.993672894802
45	204.25	144.542320823904
46	102	164.325067999016
47	87.025	143.675110072167
48	72.175	124.905417120366
49	176.75	107.434423025053
50	188.975	130.400523089806

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737520872zuu9p6ny05wbew/1crts1273752036.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737520872zuu9p6ny05wbew/1crts1273752036.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737520872zuu9p6ny05wbew/2m0bd1273752036.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737520872zuu9p6ny05wbew/2m0bd1273752036.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

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