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B58A,steven,coomans,thesis,ETS,per2maand

*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 13:18:31 +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/t1273756746568zwbd4hrw17nj.htm/, Retrieved Thu, 13 May 2010 15:19:09 +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/t1273756746568zwbd4hrw17nj.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:

B58A,steven,coomans,thesis,ETS,per2maand

Dataseries X:

» Textbox « » Textfile « » CSV «

719.625 689.5125 640.5 541.3875 521.3875 613.325 683 728.6375 452.5375 380.425 377.125 316.75 387.2625 409.75 497.875 616.4 715.5125 454.925 464.25 247.675 292.5125 416.525 481.6625 219.7625 402.625

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	6 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
26	379.125191407579	136.732205394325	220.632936880150	537.617445935008	621.518177420833
27	379.125191407579	63.4494309800173	172.715906251576	585.534476563582	694.80095183514
28	379.125191407579	4.22785642823169	133.993000024976	624.257382790181	754.022526386926
29	379.125191407579	-46.8382317025686	100.602679740278	657.64770307488	805.088614517726
30	379.125191407579	-92.4059993749738	70.807517962013	687.442864853144	850.656382190131
31	379.125191407579	-133.942533102461	43.6482383805321	714.602144434625	892.192915917618
32	379.125191407579	-172.359455692417	18.528762881538	739.72161993362	930.609838507575
33	379.125191407579	-208.269175300262	-4.9513407043961	763.201723519554	966.519558115419
34	379.125191407579	-242.106623693034	-27.0764589738366	785.326841788994	1000.35700650819
35	379.125191407579	-274.193868582485	-48.0571806842603	806.307563499418	1032.44425139764
36	379.125191407579	-304.777301788197	-68.0546127869255	826.304995602083	1063.02768460335
37	379.125191407579	-334.050415475113	-87.195273008106	845.445655823263	1092.30079829027
38	379.125191407579	-362.168456417352	-105.580671757171	863.831054572328	1120.41883923251
39	379.125191407579	-389.258240453553	-123.293729453996	881.544112269154	1147.50862326871
40	379.125191407579	-415.424948238616	-140.403220032194	898.653602847352	1173.67533105377
41	379.125191407579	-440.756965042289	-156.966935513307	915.217318328464	1199.00734785745
42	379.125191407579	-465.329411586557	-173.033994697284	931.28437751244	1223.57979440171
43	379.125191407579	-489.206774243278	-188.646562956275	946.896945771433	1247.45715705844
44	379.125191407579	-512.444900410321	-203.841156940963	962.09153975612	1270.69528322548
45	379.125191407579	-535.092536859203	-218.649650452012	976.90003326717	1293.34291967436
46	379.125191407579	-557.192532834771	-233.100061104667	991.350443919824	1315.44291564993
47	379.125191407579	-578.782793100052	-247.21717349129	1005.46755630645	1337.03317591521
48	379.125191407579	-599.897041657927	-261.023038552118	1019.27342136728	1358.14742447308
49	379.125191407579	-620.565440182848	-274.537377946027	1032.78776076118	1378.81582299801

Actuals and Interpolation
Time	Actual	Forecast
1	719.625	712.710818344606
2	689.5125	718.479353436485
3	640.5	694.312167950503
4	541.3875	649.416416773587
5	521.3875	559.287370647071
6	613.325	527.667325831681
7	683	599.131936389492
8	728.6375	669.103466230299
9	452.5375	718.772997648195
10	380.425	496.65143830366
11	377.125	399.683160440186
12	316.75	380.862778420582
13	387.2625	327.373178264001
14	409.75	377.339128145203
15	497.875	404.379674802946
16	616.4	482.383275403329
17	715.5125	594.194074897074
18	454.925	695.410621898232
19	464.25	494.772308045129
20	247.675	469.307399279518
21	292.5125	284.398419958176
22	416.525	291.168036059174
23	481.6625	395.75395548464
24	219.7625	467.427868382560
25	402.625	260.799457420375

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273756746568zwbd4hrw17nj/1qy581273756704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273756746568zwbd4hrw17nj/1qy581273756704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273756746568zwbd4hrw17nj/2qy581273756704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273756746568zwbd4hrw17nj/2qy581273756704.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 <- 'Croston'

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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