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B611,steven,coomans,thesis,Arima,per3maand

*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 14:18:52 +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/t1273760364tn66o8hmrzs7q8p.htm/, Retrieved Thu, 13 May 2010 16:19:27 +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/t1273760364tn66o8hmrzs7q8p.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,per3maand

Dataseries X:

» Textbox « » Textfile « » CSV «

42.13333333 43.68333333 9.016666667 33.7 19.09866667 10.06733333 18.81666667 40.85 51.575 16.625 42.6 25.66666667 49.325 39.40833333 37.55333333 57.05 29.575

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
18	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
19	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
20	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
21	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
22	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
23	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
24	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
25	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
26	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
27	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
28	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
29	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
30	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
31	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
32	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
33	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
34	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
35	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
36	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
37	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
38	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
39	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
40	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743
41	33.3379019604118	5.49459555024925	15.1321424661854	51.5436614546381	61.1812083705743

Actuals and Interpolation
Time	Actual	Forecast
1	42.13333333	33.3379019604118
2	43.68333333	33.3379019604118
3	9.016666667	33.3379019604118
4	33.7	33.3379019604118
5	19.09866667	33.3379019604118
6	10.06733333	33.3379019604118
7	18.81666667	33.3379019604118
8	40.85	33.3379019604118
9	51.575	33.3379019604118
10	16.625	33.3379019604118
11	42.6	33.3379019604118
12	25.66666667	33.3379019604118
13	49.325	33.3379019604118
14	39.40833333	33.3379019604118
15	37.55333333	33.3379019604118
16	57.05	33.3379019604118
17	29.575	33.3379019604118

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273760364tn66o8hmrzs7q8p/1nfd81273760330.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273760364tn66o8hmrzs7q8p/1nfd81273760330.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273760364tn66o8hmrzs7q8p/2nfd81273760330.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273760364tn66o8hmrzs7q8p/2nfd81273760330.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):

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')

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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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