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B521,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 13:09:03 +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/t1273756198nfiyj1wtxxz0bbe.htm/, Retrieved Thu, 13 May 2010 15:10:00 +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/t1273756198nfiyj1wtxxz0bbe.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:
B521,steven,coomans,thesis,Arima,per2maand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
341,25 303,6875 357,5 295,075 386,5755 455,6625 424,926 506,751 433,9 466,3375 496,7 464,45 385,375 381,875 219,6375 268,975 292,2875 181,025 277,625 166,75 266 189,25 226,35 158,75 218,8125
 
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
26209.414075178548102.401522366656139.442327906270279.385822450827316.426627990440
27290.524811316582169.796143181674211.584578294538369.465044338626411.253479451489
28264.943295219106119.941526035548170.131734512018359.754855926194409.945064402663
29255.24870824175494.8189236900868150.349310511024360.148105972483415.678492793421
30306.684590709726129.918675339492191.103571178487422.265610240964483.450506079959
31261.80265570783771.1025898407337137.110584889609386.494726526064452.50272157494
32313.500955096749109.367423282953180.025213022075446.976697171423517.634486910545
33267.16835806483950.6358163769914125.585339203126408.751376926552483.700899752687
34303.01586417717574.664820543475153.705142260422452.326586093929531.366907810876
35285.68745384860846.1421256492712129.057182683295442.31772501392525.232782047944
36317.28081679139167.0223041022669153.645570808606480.916062774175567.539329480515
37289.20735439180228.6846148276172118.860691196968459.554017586636549.730093955987
38293.59916887812332.1153698135266122.624102652902464.574235103343555.082967942718
39255.684010977318-9.8652376565522482.0506901610731429.317331793563521.233259611189
40267.641952204615-0.18767582963880692.5175714435707442.76633296566535.47158023887
41272.1739392226141.3251985593154895.0754668217094449.272411623519543.022679885913
42248.129717785499-25.333399202320269.3217965895961426.937638981402521.592834773319
43269.110198390853-7.1153218839306288.4960387716936449.724358010012545.335718665636
44244.943370603749-33.934786488924462.5947449976619427.291996209835523.821527696422
45266.601945560824-14.942703172252282.509794786303450.694096335345548.146594293901
46249.844720054549-34.322980455566664.037447911531435.651992197568534.012420564665
47257.945034697678-28.830438325851170.4326317596503445.457437635707544.720507721208
48243.176447821398-46.179169982110853.9769788922616432.375916750533532.532065624906
49256.299626781121-35.615285273468865.4267252889207447.172528273321548.21453883571


Actuals and Interpolation
TimeActualForecast
1341.25340.90875028286
2303.6875332.86089603337
3357.5325.788548906474
4295.075327.541311770725
5386.5755332.035063343418
6455.6625355.979142780996
7424.926422.853279739822
8506.751447.538297256219
9433.9463.452634199947
10466.3375469.038817658043
11496.7454.838864911353
12464.45483.073307566238
13385.375464.126997390433
14381.875429.475306145968
15219.6375366.780195970248
16268.975314.305881579131
17292.2875216.573154866353
18181.025228.421842054758
19277.625233.11741103145
20166.75200.091184304044
21266235.499484918374
22189.25219.691637200487
23226.35204.478173107192
24158.75216.921637949675
25218.8125235.210210182817


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/13/t1273756198nfiyj1wtxxz0bbe/1z3tj1273756140.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273756198nfiyj1wtxxz0bbe/1z3tj1273756140.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/13/t1273756198nfiyj1wtxxz0bbe/2auam1273756140.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273756198nfiyj1wtxxz0bbe/2auam1273756140.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 <- 'ARIMA'
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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