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B521,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 14:08:42 +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/t127375975722dk35p0s1j3106.htm/, Retrieved Thu, 13 May 2010 16:09:22 +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/t127375975722dk35p0s1j3106.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,per3maand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
341.95 326.3416667 317.3416667 440.867 433.034 477.3506667 488.225 463.4333333 384.25 273.675 268.1666667 226.6916667 248.8333333 224.75 188.0083333 194.8916667 145.875
 
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
18145.87543.727563744631479.084381456999212.665618543001248.022436255369
19145.8751.4167102860165051.4188014172001240.3311985828290.333289713984
20145.875-31.049549457201630.1902552145703261.559744785430322.799549457202
21145.875-58.419872510737212.2937629139981279.456237086002350.169872510737
22145.875-82.5336111943308-3.47336332140813295.223363321408374.283611194331
23145.875-104.334097359124-17.7279350352248309.477935035225396.084097359124
24145.875-124.381713394528-30.8363665769598322.58636657696416.131713394528
25145.875-143.041579427967-43.0373971655998334.7873971656434.791579427967
26145.875-160.567308766106-54.4968556290029346.246855629003452.317308766106
27145.875-177.143555713826-65.3354809273599357.08548092736468.893555713826
28145.875-192.909719355804-75.644421222888367.394421222888484.659719355804
29145.875-207.974098914403-85.4944895708593377.244489570859499.724098914403
30145.875-222.422819075921-94.941999876746386.691999876746514.172819075921
31145.875-236.325709404920-104.032611238620395.78261123862528.07570940492
32145.875-249.740319474826-112.803953299935404.553953299935541.490319474826
33145.875-262.714745021474-121.287474172004413.037474172004554.464745021475
34145.875-275.289669066932-129.509775053131421.259775053131567.039669066932
35145.875-287.499869141951-137.493595748400429.2435957484579.249869141951
36145.875-299.375351978915-145.258556605515437.008556605515591.125351978915
37145.875-310.942222388662-152.821726642816444.571726642816602.692222388662
38145.875-322.223358685903-160.198065186223451.948065186223613.973358685903
39145.875-333.23894483774-167.400769822447459.150769822447624.98894483774
40145.875-344.006894819073-174.441553870079466.191553870079635.756894819073
41145.875-354.543194718248-181.330870070450473.08087007045646.293194718248


Actuals and Interpolation
TimeActualForecast
1341.95341.608050170975
2326.3416667341.950000000067
3317.3416667326.3416667
4440.867317.3416667
5433.034440.867
6477.3506667433.034
7488.225477.3506667
8463.4333333488.225
9384.25463.4333333
10273.675384.25
11268.1666667273.675
12226.6916667268.1666667
13248.8333333226.6916667
14224.75248.8333333
15188.0083333224.75
16194.8916667188.0083333
17145.875194.8916667


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/13/t127375975722dk35p0s1j3106/1yfxy1273759716.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t127375975722dk35p0s1j3106/1yfxy1273759716.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/13/t127375975722dk35p0s1j3106/2yfxy1273759716.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t127375975722dk35p0s1j3106/2yfxy1273759716.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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