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B382,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 11:59:19 +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/t1273752012tck27ljj6cx866u.htm/, Retrieved Thu, 13 May 2010 14:00:15 +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/t1273752012tck27ljj6cx866u.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,ETS

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	11 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	147.139314398673	17.9398264690211	62.660310024181	231.618318773165	276.338802328325
52	147.139314398673	13.1211421457758	59.5095417003592	234.769087096987	281.157486651570
53	147.139314398673	8.46980330582255	56.4681947066187	237.810434090727	285.808825491524
54	147.139314398673	3.96949872702689	53.5256037080721	240.753025089274	290.309130070319
55	147.139314398673	-0.393593564859259	50.672731001539	243.605897795807	294.672222362206
56	147.139314398673	-4.63130774177631	47.9018386379056	246.376790159441	298.909936539123
57	147.139314398673	-8.75386876730767	45.2062408728463	249.0723879245	303.032497564654
58	147.139314398673	-12.7701830376736	42.5801141268926	251.698514670454	307.04881183502
59	147.139314398673	-16.6880648243003	40.0183489225955	254.260279874751	310.966693621647
60	147.139314398673	-20.5144150432659	37.5164329934678	256.762195803878	314.793043840612
61	147.139314398673	-24.2553640733082	35.0703579003083	259.208270897038	318.533992870654
62	147.139314398673	-27.916387082989	32.6765436228218	261.602085174524	322.195015880335
63	147.139314398673	-31.5023980706149	30.3317770702208	263.946851727125	325.781026867961
64	147.139314398673	-35.0178272309724	28.0331614938359	266.245467303510	329.296456028319
65	147.139314398673	-38.4666851256033	25.778074528428	268.500554268918	332.74531392295
66	147.139314398673	-41.8526163077161	23.5641331287439	270.714495668602	336.131245105062
67	147.139314398673	-45.1789444454928	21.3891640649715	272.889464732375	339.457573242839
68	147.139314398673	-48.4487105353053	19.2511789364605	275.027449860886	342.727339332651
69	147.139314398673	-51.6647054557994	17.1483528857513	277.130275911595	345.943334253146
70	147.139314398673	-54.8294978546559	15.0790063644018	279.199622432944	349.108126652002
71	147.139314398673	-57.9454581607332	13.0415894322915	281.237039365055	352.224086958079
72	147.139314398673	-61.0147793599325	11.0346681730133	283.243960624333	355.293408157279
73	147.139314398673	-64.0394950524395	9.0569128868776	285.221715910469	358.318123849786
74	147.139314398673	-67.0214952138988	7.1070877852346	287.171541012112	361.300124011245

Actuals and Interpolation
Time	Actual	Forecast
1	283.25	266.188606019126
2	286.75	270.891611334875
3	230.25	275.263005974429
4	200.5	262.855086280826
5	297.95	245.666789558327
6	329.5	260.078754776835
7	289.75	279.214851491821
8	223.775	282.118884977697
9	281.78	266.036283432995
10	265.8	270.376068493188
11	256.75	269.114666612848
12	89.275	265.706323023052
13	225.5	217.072697249434
14	124.25	219.395699090770
15	230	193.168608321036
16	286.525	203.321250657694
17	227	226.256520535747
18	218.3	226.461462047352
19	334.525	224.211739695473
20	128.95	254.619797219461
21	195.5	219.978681434033
22	106.056	213.231086891884
23	173.525	183.688072766927
24	114.75	180.886602658401
25	131.05	162.655923839499
26	141.25	153.943691533454
27	160.25	150.444651605929
28	145.5	153.147514318775
29	297.5	151.039462624184
30	179.25	191.411585007509
31	137	188.059221243893
32	158.6	173.984650948099
33	55.6	169.743843000579
34	15.25	138.279878157287
35	67.75	104.366461022770
36	93	94.2730647254302
37	126.75	93.922142041287
38	160	102.971202783778
39	150.525	118.691297915473
40	239.25	127.466317722843
41	165.05	158.279699827074
42	215.81	160.145945793739
43	166	175.489846949862
44	79.05	172.873952803060
45	204.25	147.011203881603
46	102	162.789185605352
47	87.025	146.032532346977
48	72.175	129.766995157684
49	176.75	113.891653454959
50	188.975	131.218674815109

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273752012tck27ljj6cx866u/1uqpa1273751947.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752012tck27ljj6cx866u/1uqpa1273751947.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752012tck27ljj6cx866u/2mhpd1273751947.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752012tck27ljj6cx866u/2mhpd1273751947.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = ETS ; 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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