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B28A,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 13:54:23 +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/t1273758905flplqmbcnmtiegd.htm/, Retrieved Thu, 13 May 2010 15:55:08 +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/t1273758905flplqmbcnmtiegd.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,per3maand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
275,0916667 401,3423333 260,6333333 286,1 368,0833333 385,1916667 181,5333333 145,1 203,1833333 227,5833333 239,0833333 109,175 231,0833333 216,9166667 229,8583333 272,7916667 155,0833333
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Serverwessa.org @ wessa.org


Demand Forecast
PointForecast95% LB80% LB80% UB95% UB
18207.87404692967251.5980751842378105.690681405043310.057412454302364.150018675107
19207.87404692967242.808927662676899.9437667398216315.804327119523372.939166196668
20207.87404692967234.464681979923894.487757750692321.260336108653381.283411879421
21207.87404692967226.503922306230989.2824967850729326.465597074272389.244171553114
22207.87404692967218.878183589835584.2962942489462331.451799610399396.869910269509
23207.87404692967211.548422995306879.5036214044042336.244472454941404.199670864038
24207.8740469296724.4826388838418274.8835535045478340.864540354797411.265454975503
25207.874046929672-2.3457882917290470.4186849630406345.329408896304418.093882151074
26207.874046929672-8.95928393567466.0943525961839349.653741263161424.707377795019
27207.874046929672-15.376949994884261.8980663122533353.850027547091431.125043854229
28207.874046929672-21.615216177463057.8190833025508357.929010556794437.363310036808
29207.874046929672-27.688335752927453.8480838550775361.900010004267443.436429612272
30207.874046929672-33.608768998814049.9769206322754365.771173227069449.356862858159
31207.874046929672-39.387483928645346.1984220355783369.549671823767455.13557778799
32207.874046929672-45.034195118795542.5062360449192373.241857814426460.78228897814
33207.874046929672-50.557555529651538.8947047936137376.853389065731466.305649388996
34207.874046929672-55.965312157143235.3587627932955380.389331066049471.713406016488
35207.874046929672-61.264433516712831.8938535766291383.854240282716477.012527376058
36207.874046929672-66.461214950088328.4958608409113387.252233018433482.209308809433
37207.874046929672-71.561366295431325.1610511236437390.587042735701487.309460154776
38207.874046929672-76.570085402024921.8860257338611393.862068125484492.31817926137
39207.874046929672-81.492120186629718.6676801756617397.080413683683497.240214045975
40207.874046929672-86.331821341555915.5031696842512400.244924175094502.079915200901
41207.874046929672-91.093187360098812.3898797853930403.358214073952506.841281219444


Actuals and Interpolation
TimeActualForecast
1275.0916667274.816575230748
2401.3423333295.968930016752
3260.6333333338.328662343278
4286.1308.265916154109
5368.0833333301.071991695963
6385.1916667324.590521416548
7181.5333333345.060642650557
8145.1289.120656303052
9203.1833333240.050659948121
10227.5833333227.504420390666
11239.0833333227.532064816018
12109.175231.457107359672
13231.0833333189.871114035390
14216.9166667203.886458556338
15229.8583333208.317704231102
16272.7916667215.643057115841
17155.0833333235.07741198107


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/13/t1273758905flplqmbcnmtiegd/1zcd51273758860.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273758905flplqmbcnmtiegd/1zcd51273758860.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/13/t1273758905flplqmbcnmtiegd/2zcd51273758860.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273758905flplqmbcnmtiegd/2zcd51273758860.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')
-SERVER-wessa.org
 




Copyright

Creative Commons License

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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