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B382,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 13:04:00 +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/t1273755878x3zkimsjs9lvugi.htm/, Retrieved Thu, 13 May 2010 15:04:41 +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/t1273755878x3zkimsjs9lvugi.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:
B382,steven,coomans,thesis,ARIMA,Per2maand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
285 215.375 313.725 256.7625 273.79 173.0125 174.875 258.2625 222.65 231.7375 150.778 144.1375 136.15 152.875 238.375 147.8 35.425 80.375 143.375 194.8875 190.43 122.525 153.125 79.6 182.8625
 
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
26145.46345979031831.283713026552270.8053362876409220.121583292994259.643206554083
27145.46345979031824.606993730332866.4396641965898224.487255384045266.319925850302
28145.46345979031818.280299718007262.3028613368575228.624058243777272.646619862628
29145.46345979031812.253749095302658.3623116909041232.564607889731278.673170485332
30145.4634597903186.4882899184291654.5924805651859236.334439015449284.438629662206
31145.4634597903180.95266898772416750.9729325908535239.953986989782289.974250592911
32145.463459790318-4.3785886335530247.4870106021019243.439908978533295.305508214188
33145.463459790318-9.5265730935173844.1209244923029246.805995088332300.453492674152
34145.463459790318-14.508978807094940.8631045054157250.063815075219305.43589838773
35145.463459790318-19.340823916974437.7037308057565253.223188774879310.267743497609
36145.463459790318-24.034984880258234.6343839305758256.292535650059314.961904460893
37145.463459790318-28.60260112683831.6477801981685259.279139382466319.529520707473
38145.463459790318-33.053386419460528.7375681204696262.189351460165323.980306000096
39145.463459790318-37.395871932611625.8981694623155265.028750118320328.322791513247
40145.463459790318-41.637598506066523.1246535336036267.802266047031332.564518086701
41145.463459790318-45.785270488075620.4126365966411270.514282983994336.712190068711
42145.463459790318-49.844880150833517.7582005152412273.168719065394340.771799731469
43145.463459790318-53.821809279021615.1578263295465275.769093251089344.748728859657
44145.463459790318-57.720912850664712.6083395400571278.318580040578348.6478324313
45145.463459790318-61.546588523720110.1068646727928280.820054907842352.473508104355
46145.463459790318-65.30283476469647.6507872710319283.276132309603356.229754345331
47145.463459790318-68.99329980919145.23772188173598285.689197698899359.920219389826
48145.463459790318-72.62132316207512.86548492003911288.061434660596363.54824274271
49145.463459790318-76.18997098137810.532071532966938290.394848047668367.116890562013


Actuals and Interpolation
TimeActualForecast
1285284.715000203273
2215.375273.669961695715
3313.725251.121344746975
4256.7625274.792708736568
5273.79268.316598141974
6173.0125269.853519048767
7174.875235.891289968433
8258.2625214.685176171976
9222.65229.822804324978
10231.7375227.333358752735
11150.778228.857278044667
12144.1375201.763796135814
13136.15181.769285845826
14152.875165.941563187890
15238.375161.40829991438
16147.8188.11152745539
1735.425174.125600162880
1880.375126.004166756322
19143.375110.173380876921
20194.8875121.692499793419
21190.43147.087096517147
22122.525162.124674010416
23153.125148.385789010579
2479.6150.030031637519
25182.8625125.594727004357


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/13/t1273755878x3zkimsjs9lvugi/1tiuf1273755836.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273755878x3zkimsjs9lvugi/1tiuf1273755836.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/13/t1273755878x3zkimsjs9lvugi/2tiuf1273755836.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273755878x3zkimsjs9lvugi/2tiuf1273755836.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
 




Copyright

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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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