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B58A,steven,coomans,thesis,ETS,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 14:14:39 +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/t1273760112dbxkc8o9gia6u1d.htm/, Retrieved Thu, 13 May 2010 16:15:15 +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/t1273760112dbxkc8o9gia6u1d.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:
B58A,steven,coomans,thesis,ETS,per3maand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
721.8416667 644.5833333 554.4333333 562.9666667 711.675 531.1083333 379.95 336.25 370.175 493.0833333 657.7666667 533.4583333 402.2833333 267.3416667 447.5416667 297.7583333 268.4166667
 
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
18277.42105729382275.5823349527226145.445810481080409.396304106563479.25977963492
19253.80448812841651.9657560774083121.829234966707385.779741290125455.643220179423
20230.1879189630128.349169931956898.2126546986457362.163183227374432.026667994063
21206.5713497976044.7325744978773874.5960683570775338.546631238131408.410125097331
22182.954780632198-18.884032243318650.9794746221831314.930086642214384.793593507716
23159.338211466793-42.500652310116727.3628721741458291.313550759440361.177075243702
24135.721642301387-66.11728772099773.74625969315207267.697024909622337.560572323772
25112.105073135981-89.7339404944367-19.8703641406082244.080510412571313.944086766399
2688.4885039705754-113.350612648901-43.48700064694220.464008588091290.327620590052
2764.8719348051696-136.967306202850-67.1036511456423196.847520755982266.711175813189
2841.2553656397639-160.584023174730-90.7203169565066173.231048236034243.094754454257
2917.6387964743581-184.200765582974-114.336999399315149.614592348032219.478358531690
30-5.97777269104768-207.817535445999-137.953699793841125.998154411745195.861990063903
31-29.5943418564535-231.434334782204-161.570419459843102.381735746936172.245651069297
32-53.2109110218592-255.051165609966-185.18715971706778.7653376733485148.629343566247
33-76.827480187265-278.668029947637-208.80392188524355.1489615107131125.013069573107
34-100.444049352671-302.284929813542-232.42070728408331.5326085787412101.396831108200
35-124.060618518077-325.901867225974-256.0375172332777.9162801971237177.7806301898208
36-147.677187683482-349.518844203191-279.654353052493-15.700022314471254.1644688362267
37-171.293756848888-373.135862763413-303.271216061375-39.316297636400730.5483490656374
38-194.910326014294-396.752924924817-326.888107579538-62.932544449056.9322728962295
39-218.526895179700-420.370032705532-350.505028926564-86.5487614328352-16.6837576538674
40-242.143464345105-443.987188123635-374.121981422005-110.164947268206-40.2997405665755
41-265.760033510511-467.604393197149-397.738966385373-133.781100635649-63.9156738238731


Actuals and Interpolation
TimeActualForecast
1721.8416667678.894276907849
2644.5833333655.301646093303
3554.4333333631.702030563305
4562.9666667608.087355478031
5711.675584.471710254483
6531.1083333560.887772249836
7379.95537.283559956972
8336.25513.649560056135
9370.175489.995608873635
10493.0833333466.336019331437
11657.7666667442.695253359724
12533.4583333419.096744533836
13402.2833333395.498576016643
14267.3416667371.889233239938
15447.5416667348.257168901305
16297.7583333324.657491371801
17268.4166667301.041221013742


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/13/t1273760112dbxkc8o9gia6u1d/1fg8q1273760075.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273760112dbxkc8o9gia6u1d/1fg8q1273760075.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/13/t1273760112dbxkc8o9gia6u1d/2fg8q1273760075.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273760112dbxkc8o9gia6u1d/2fg8q1273760075.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 = ETS ; 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
 




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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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