Home » date » 2010 » May » 13 »

B521,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 12:06:44 +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/t12737524367eedlxz4oedr3mu.htm/, Retrieved Thu, 13 May 2010 14:07:19 +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/t12737524367eedlxz4oedr3mu.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:

B521,steven,coomans,thesis,ETS

Dataseries X:

» Textbox « » Textfile « » CSV «

387 295,5 343,35 264,025 322,5 392,5 315,75 274,4 361,875 411,276 518,775 392,55 467 382,852 449,25 564,252 417 450,8 538,675 394 532 461,4 523 405,9 386,25 384,5 382 381,75 151,5 287,775 247,6 290,35 266,55 318,025 213,3 148,75 273 282,25 191,25 142,25 259,25 272,75 173,75 204,75 185,525 267,175 190,25 127,25 183,5 254,125

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	209.738598032764	69.0649056994597	117.757014665561	301.720181399967	350.412290366068
52	209.738598032764	58.4192609921121	110.796201738106	308.680994327422	361.057935073416
53	209.738598032764	48.4748519205034	104.293902147572	315.183293917956	371.002344145025
54	209.738598032764	39.1090289132913	98.1699196498413	321.307276415687	380.368167152237
55	209.738598032764	30.2312056901806	92.36502305993	327.112173005598	389.245990375348
56	209.738598032764	21.7722235043089	86.8339919038846	332.643204161643	397.704972561219
57	209.738598032764	13.6778625421801	81.5413737512436	337.935822314284	405.799333523348
58	209.738598032764	5.90468022504007	76.4587630265715	343.018433038957	413.572515840488
59	209.738598032764	-1.58276883907700	71.5629832338084	347.91421283172	421.059964904605
60	209.738598032764	-8.81385366778426	66.8348310045263	352.642365061002	428.291049733312
61	209.738598032764	-15.8132323567447	62.2581832765927	357.219012788935	435.290428422273
62	209.738598032764	-22.6018466594914	57.8193469733813	361.657849092147	442.079042725019
63	209.738598032764	-29.1976618516082	53.5065752325864	365.970620832942	448.674857917136
64	209.738598032764	-35.6162273431994	49.3097008414329	370.167495224095	455.093423408728
65	209.738598032764	-41.8711084234988	45.21985393405	374.257342131478	461.348304489027
66	209.738598032764	-47.9742235938365	41.2292414212997	378.247954644228	467.451419659365
67	209.738598032764	-53.9361115593417	37.3309724142865	382.146223651242	473.41330762487
68	209.738598032764	-59.766145016813	33.5189184359819	385.958277629546	479.243341082341
69	209.738598032764	-65.4727036500626	29.7876003056494	389.689595759879	484.949899715591
70	209.738598032764	-71.063315460882	26.132095727493	393.345100338035	490.54051152641
71	209.738598032764	-76.544773243722	22.5479631319577	396.92923293357	496.02196930925
72	209.738598032764	-81.9232313470292	19.031178406892	400.446017658636	501.400427412557
73	209.738598032764	-87.2042866519928	15.5780819483915	403.899114117137	506.681482717521
74	209.738598032764	-92.3930468054456	12.1853340457032	407.291862019825	511.870242870974

Actuals and Interpolation
Time	Actual	Forecast
1	387	342.645625588810
2	295.5	360.224691610665
3	343.35	334.57221331123
4	264.025	338.051132668702
5	322.5	308.712193932653
6	392.5	314.176745205712
7	315.75	345.218771733043
8	274.4	333.539349111146
9	361.875	310.100521795569
10	411.276	330.620413749521
11	518.775	362.586818293621
12	392.55	424.489221258347
13	467	411.830679983883
14	382.852	433.696057002919
15	449.25	413.544920914575
16	564.252	427.695992841313
17	417	481.817533216771
18	450.8	456.128258800945
19	538.675	454.016498360475
20	394	487.569387097804
21	532	450.484827677171
22	461.4	482.791913845528
23	523	474.313609939397
24	405.9	493.609593178994
25	386.25	458.847458672266
26	384.5	430.074749419565
27	382	412.012009506951
28	381.75	400.117284272407
29	151.5	392.837738406794
30	287.775	297.187825780639
31	247.6	293.457219993799
32	290.35	275.282527901956
33	266.55	281.254252006065
34	318.025	275.426483713804
35	213.3	292.309646652501
36	148.75	260.99558094183
37	273	216.509044850814
38	282.25	238.898228406847
39	191.25	256.079930665467
40	142.25	230.385742741739
41	259.25	195.45471144866
42	272.75	220.738837405002
43	173.75	241.352535000074
44	204.75	214.559474748700
45	185.525	210.671664210590
46	267.175	200.705231896851
47	190.25	227.049340171498
48	127.25	212.464577360093
49	183.5	178.691297918695
50	254.125	180.597141310443

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737524367eedlxz4oedr3mu/1p1lt1273752392.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737524367eedlxz4oedr3mu/1p1lt1273752392.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737524367eedlxz4oedr3mu/2ib2e1273752392.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737524367eedlxz4oedr3mu/2ib2e1273752392.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

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