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FM22,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 14:25:25 +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/t1273760757f8ua8kjqnkpd174.htm/, Retrieved Thu, 13 May 2010 16:26:00 +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/t1273760757f8ua8kjqnkpd174.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:
FM22,steven,coomans,thesis,Arima,per3maand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
738.1666667 733.4333333 671.625 696.7083333 678.8 692.6583333 733.8833333 697.5416667 546.4166667 716.1166667 600.2583333 387.8083333 137.25 403.4083333 241.9583333 183.9 91.5
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Serverwessa.org @ wessa.org


Demand Forecast
PointForecast95% LB80% LB80% UB95% UB
1886.644662096658-141.51367918119-62.5400585222275235.829382715543314.803003374506
1946.9840343740384-209.596010556908-120.784639368160214.752708116237303.564079304985
207.32340665141896-274.82976135999-177.166633492480191.813446795318289.476574662828
21-32.3372210712006-337.930907165959-232.154192160692167.479750018290273.256465023558
22-71.9978487938201-399.35788261452-286.047069671775142.051372084135255.362185026880
23-111.658476516440-459.425183527328-339.050903790395115.733950757515236.108230494449
24-151.319104239059-518.359665032148-391.31402711165388.6758186335344215.72145655403
25-190.979731961679-576.331342869707-442.94760695312160.9881430297640194.37187894635
26-230.640359684298-633.471533740528-494.03750669007832.756787321482172.190814371932
27-270.300987406918-689.884168139449-544.6516828204084.0497080065727149.282193325614
28-309.961615129537-745.65317611705-594.845014254419-25.0782160046551125.729945857976
29-349.622242852157-800.847493783267-644.662575891604-54.5819098127092101.603008078954
30-389.282870574776-855.524565235336-694.141928408243-84.42381274130976.9588240857843
31-428.943498297396-909.732858206763-743.314763151852-114.5722334429451.8458616119713
32-468.604126020015-963.513713080285-792.208111145773-145.00014089425726.3054610402544
33-508.264753742635-1016.90272924808-840.84524958139-175.6842579038790.373221762807077
34-547.925381465254-1069.93082290086-889.246393469253-206.604369461255-25.9199400296482
35-587.586009187874-1122.62504668933-937.429231586872-237.742786788875-52.5469716864128
36-627.246636910493-1175.00923367354-985.409347534584-269.083926286402-79.4840401474453
37-666.907264633113-1227.10450951629-1033.20055464083-300.613974625395-106.710019749933
38-706.567892355732-1278.92970444387-1080.81516532877-332.32061938269-134.206080267598
39-746.228520078352-1330.50168795355-1128.26420996981-364.192830186896-161.955352203153
40-785.889147800971-1381.83564326934-1175.55761634047-396.220679261469-189.942652332604
41-825.549775523591-1432.94529429463-1222.70435801887-428.395193028308-218.154256752555


Actuals and Interpolation
TimeActualForecast
1738.1666667737.38883988618
2733.4333333702.013587224558
3671.625679.86573961518
4696.7083333636.184921863945
5678.8627.868765768785
6692.6583333614.460675199214
7733.8833333615.047131681786
8697.5416667636.526410902895
9546.4166667628.255186240842
10716.1166667546.492806998329
11600.2583333594.095092729053
12387.8083333557.605131041833
13137.25430.592686869799
14403.4083333240.022177721986
15241.9583333284.415410317549
16183.9222.912777660310
1791.5163.182061709202


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/13/t1273760757f8ua8kjqnkpd174/11pji1273760722.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273760757f8ua8kjqnkpd174/11pji1273760722.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/13/t1273760757f8ua8kjqnkpd174/2bgi31273760722.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273760757f8ua8kjqnkpd174/2bgi31273760722.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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