Home » date » 2010 » May » 13 »

B611,steven,coomans,thesis,Arima,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 13:23:18 +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/t1273757036mvau1jn8z7bl48v.htm/, Retrieved Thu, 13 May 2010 15:23:59 +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/t1273757036mvau1jn8z7bl48v.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,Arima,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	2 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
26	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
27	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
28	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
29	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
30	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
31	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
32	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
33	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
34	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
35	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
36	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
37	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
38	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
39	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
40	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
41	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
42	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
43	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
44	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
45	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
46	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
47	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
48	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753
49	34.004660000026	-3.51342843642327	9.47290056402003	58.536419436032	71.5227484364753

Actuals and Interpolation
Time	Actual	Forecast
1	22.325	34.004660000026
2	94.125	34.004660000026
3	12.275	34.004660000026
4	7.125	34.004660000026
5	18.925	34.004660000026
6	38.025	34.004660000026
7	28.138	34.004660000026
8	2.386	34.004660000026
9	13.225	34.004660000026
10	26.25	34.004660000026
11	31.975	34.004660000026
12	31.275	34.004660000026
13	34.4875	34.004660000026
14	52.1375	34.004660000026
15	15.675	34.004660000026
16	48.9	34.004660000026
17	16.5	34.004660000026
18	37	34.004660000026
19	54.125	34.004660000026
20	34.4875	34.004660000026
21	44.4875	34.004660000026
22	40.2	34.004660000026
23	52.13	34.004660000026
24	49.575	34.004660000026
25	44.3625	34.004660000026

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273757036mvau1jn8z7bl48v/1y53c1273756995.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273757036mvau1jn8z7bl48v/1y53c1273756995.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273757036mvau1jn8z7bl48v/29w2f1273756995.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273757036mvau1jn8z7bl48v/29w2f1273756995.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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