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B611,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 13:22:33 +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/t1273756979jheviml8ejowe31.htm/, Retrieved Thu, 13 May 2010 15:23:01 +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/t1273756979jheviml8ejowe31.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:

B611,steven,coomans,thesis,ETS,per2maand

Dataseries X:

» Textbox « » Textfile « » CSV «

22.325 94.125 12.275 7.125 18.925 38.025 28.138 2.386 13.225 26.25 31.975 31.275 34.4875 52.1375 15.675 48.9 16.5 37 54.125 34.4875 44.4875 40.2 52.13 49.575 44.3625

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
26	102.011248412841	29.5171815603186	54.6099259393838	149.412570886298	174.505315265363
27	19.4230859509892	5.13146858011101	10.078299638443	28.7678722635353	33.7147033218673
28	59.8306597236831	14.3395059505467	30.085593081515	89.5757263658511	105.321813496819
29	25.3887348786001	5.4766800375612	12.3689420374925	38.4085277197077	45.300789719639
30	46.8430758445211	9.00681436938111	22.1032742408710	71.5828774481712	84.679337319661
31	56.5496766473684	9.57457975999483	25.8343117567476	87.2650415379892	103.524773534742
32	32.7813065968831	4.8117210302479	14.4929776003854	51.0696355933807	60.7508921635182
33	34.4151967390246	4.28987435443961	14.717307201753	54.1130862762963	64.5405191236097
34	44.6335453452273	4.5921717910817	18.4518984529179	70.8151922375367	84.6749188993728
35	50.3480577497758	4.10142651943852	20.1090109499894	80.5871045495623	96.5946889801132
36	45.4326292426166	2.74194854328336	17.5186935193364	73.3465649658967	88.1233099419498
37	43.9880509063975	1.73908594479038	16.3629375796888	71.6131642331062	86.2370158680045
38	102.011587581907	1.93574648494275	36.5755123057051	167.447662858108	202.087428678870
39	19.4231505292589	-0.0255024544729672	6.70635987614681	32.1399411823709	38.8718035129907
40	59.8308586498727	-1.27820142839897	19.8737919741571	99.7879253255883	120.939918728144
41	25.3888192915802	-1.04579411132008	8.1041546455596	42.6734839376009	51.8234326944805
42	46.8432315893287	-2.84851252309757	14.3515465562152	79.3349166224422	96.534975701755
43	56.5498646648791	-4.53707252036131	16.6072633713962	96.492465958362	117.636801850119
44	32.7814155888354	-3.26071807640265	9.214731104532	56.3481000731388	68.8235492540735
45	34.4153111633680	-4.07930606945003	9.24503386802521	59.5855884587109	72.9099283961861
46	44.6336937437432	-6.13405646953262	11.4384461380741	77.8289413494122	95.401443957019
47	50.3482251480183	-7.86317793930613	12.2858345390322	88.4106157570045	108.559628235343
48	45.4327802979428	-7.94050435523845	10.5338652948726	80.331695301013	98.806064951124
49	43.9881971587604	-8.5000986753717	9.66794524107751	78.3084490764432	96.4764929928924

Actuals and Interpolation
Time	Actual	Forecast
1	22.325	22.6302772146684
2	94.125	93.756538256208
3	12.275	12.4174361918487
4	7.125	7.957394580737
5	18.925	18.5967972646909
6	38.025	37.6891879612587
7	28.138	28.3831854617570
8	2.386	3.268311761931
9	13.225	13.4256098558079
10	26.25	25.9613378599121
11	31.975	31.677913672259
12	31.275	30.9670977340054
13	34.4875	34.1052947012212
14	52.1375	52.3157623512095
15	15.675	15.3162678731622
16	48.9	48.638206172574
17	16.5	16.5588725982834
18	37	36.8388933781173
19	54.125	53.7729755226329
20	34.4875	34.1226326563131
21	44.4875	43.9518330273531
22	40.2	40.2574947399717
23	52.13	52.0305427112002
24	49.575	49.4446735915632
25	44.3625	44.3510798393309

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273756979jheviml8ejowe31/1vw7t1273756940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273756979jheviml8ejowe31/1vw7t1273756940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273756979jheviml8ejowe31/25n6e1273756940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273756979jheviml8ejowe31/25n6e1273756940.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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