Home » date » 2010 » May » 13 »

B521,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 12:07:59 +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/t1273752512ppxnc3e54l2tk1r.htm/, Retrieved Thu, 13 May 2010 14:08:35 +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/t1273752512ppxnc3e54l2tk1r.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,Arima

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	2 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	210.102388435796	67.9893263254586	117.179651758785	303.025125112807	352.215450546133
52	210.102388435796	56.79208678965	109.858170359085	310.346606512507	363.412690081942
53	210.102388435796	46.358763340686	103.036186840346	317.168590031246	373.846013530906
54	210.102388435796	36.5515286633834	96.6235807651842	323.581196106408	383.653248208209
55	210.102388435796	27.2696039134372	90.5544563892318	329.65032048236	392.935172958155
56	210.102388435796	18.436655283897	84.7789017189154	335.425875152677	401.768121587695
57	210.102388435796	9.99321964758025	79.2580359052325	340.946740966360	410.211557224012
58	210.102388435796	1.89190454613620	73.9608706853001	346.243906186292	418.312872325456
59	210.102388435796	-5.90578680306385	68.8622343659465	351.342542505646	426.110563674656
60	210.102388435796	-13.4316312088157	63.9413492087125	356.26342766288	433.636408080408
61	210.102388435796	-20.7122213854485	59.180827172313	361.023949699279	440.916998257041
62	210.102388435796	-27.7700779729689	54.565942801747	365.638834069845	447.974854844561
63	210.102388435796	-34.6244724598660	50.0840951479981	370.120681723594	454.829249331458
64	210.102388435796	-41.2920478835075	45.7244019138596	374.480374957733	461.4968247551
65	210.102388435796	-47.7872949188431	41.4773881562641	378.727388715328	467.992071790435
66	210.102388435796	-54.1229225191004	37.3347439373588	382.870032934233	474.327699390692
67	210.102388435796	-60.3101503224269	33.2891331300792	386.915643741513	480.514927194019
68	210.102388435796	-66.3589421081393	29.3340407693121	390.87073610228	486.563718979731
69	210.102388435796	-72.2781942085826	25.4636498560033	394.741127015589	492.482971080175
70	210.102388435796	-78.0758890644944	21.6727409527015	398.532035918891	498.280665936087
71	210.102388435796	-83.7592214956308	17.9566096196340	402.248167251958	503.963998367223
72	210.102388435796	-89.334703388089	14.3109979633452	405.893778908247	509.539480259681
73	210.102388435796	-94.8082511430835	10.7320374570160	409.472739414576	515.013028014676
74	210.102388435796	-100.185259234647	7.21620084366808	412.988576027924	520.390036106239

Actuals and Interpolation
Time	Actual	Forecast
1	387	386.613000262070
2	295.5	374.123316633082
3	343.35	336.048165995784
4	264.025	338.086548077006
5	322.5	307.542001780858
6	392.5	313.784027977660
7	315.75	345.706762345858
8	274.4	333.558139957923
9	361.875	309.612466337202
10	411.276	330.768423422594
11	518.775	363.353572983656
12	392.55	426.254986865821
13	467	412.614239712998
14	382.852	434.624717051376
15	449.25	413.671773960281
16	564.252	428.070647900106
17	417	483.184602478810
18	450.8	456.399032969728
19	538.675	454.133048676047
20	394	488.348019556919
21	532	450.164435084369
22	461.4	483.284104365518
23	523	474.427389078871
24	405.9	494.085209133688
25	386.25	458.395775524229
26	384.5	429.19766065875
27	382	411.108071619878
28	381.75	399.327744740213
29	151.5	392.213856140806
30	287.775	294.794557918947
31	247.6	291.953672823028
32	290.35	274.003299091931
33	266.55	280.618971975236
34	318.025	274.925118706862
35	213.3	292.368070501736
36	148.75	260.368433823036
37	273	215.195340483111
38	282.25	238.589462676036
39	191.25	256.259317558014
40	142.25	229.94939840372
41	259.25	194.456577242768
42	272.75	220.679121618065
43	173.75	241.752725402618
44	204.75	214.231344240327
45	185.525	210.394149668727
46	267.175	200.329356777518
47	190.25	227.382455241916
48	127.25	212.35458028317
49	183.5	177.911907893752
50	254.125	180.173464499248

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273752512ppxnc3e54l2tk1r/1zv4p1273752476.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752512ppxnc3e54l2tk1r/1zv4p1273752476.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752512ppxnc3e54l2tk1r/2smms1273752476.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752512ppxnc3e54l2tk1r/2smms1273752476.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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