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B28A,steven,coomans,thesis,Arima,per2maand

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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 13:00:05 +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/t12737556442cinqj60jkebzsu.htm/, Retrieved Thu, 13 May 2010 15:00:48 +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/t12737556442cinqj60jkebzsu.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:

B28A,steven,coomans,thesis,Arima,per2maand

Dataseries X:

» Textbox « » Textfile « » CSV «

250.75 314.5125 449.3885 305.7 162.375 352.025 379.125 327.125 423.6625 152.25 183.8125 153.8875 245.625 108.9 291.625 284.875 192.25 45.2625 205.375 301.25 165.375 281.6375 140.5875 331.75 232.625

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	3 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
26	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
27	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
28	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
29	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
30	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
31	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
32	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
33	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
34	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
35	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
36	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
37	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
38	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
39	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
40	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
41	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
42	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
43	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
44	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
45	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
46	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
47	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
48	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658
49	251.27004	58.8788940713424	125.472231363403	377.067848636597	443.661185928658

Actuals and Interpolation
Time	Actual	Forecast
1	250.75	251.27004
2	314.5125	251.27004
3	449.3885	251.27004
4	305.7	251.27004
5	162.375	251.27004
6	352.025	251.27004
7	379.125	251.27004
8	327.125	251.27004
9	423.6625	251.27004
10	152.25	251.27004
11	183.8125	251.27004
12	153.8875	251.27004
13	245.625	251.27004
14	108.9	251.27004
15	291.625	251.27004
16	284.875	251.27004
17	192.25	251.27004
18	45.2625	251.27004
19	205.375	251.27004
20	301.25	251.27004
21	165.375	251.27004
22	281.6375	251.27004
23	140.5875	251.27004
24	331.75	251.27004
25	232.625	251.27004

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737556442cinqj60jkebzsu/1wsoh1273755602.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737556442cinqj60jkebzsu/1wsoh1273755602.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737556442cinqj60jkebzsu/2p2n21273755602.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737556442cinqj60jkebzsu/2p2n21273755602.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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