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FM22,steven,coomans,thesis,ETS,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 13:30:24 +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/t1273757456jx2b2fx3v3ls3pn.htm/, Retrieved Thu, 13 May 2010 15:30:58 +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/t1273757456jx2b2fx3v3ls3pn.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,ETS,per2maand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
724 762.275 721.125 653.275 663.7125 735.5125 628.1375 792.55 636.5 800.825 728.05 618.2625 450.625 767.525 675.65 583.25 690.7875 208.0625 142.5 205.925 462.5625 251.4375 195.725 191.625 137.25
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Serverwessa.org @ wessa.org


Demand Forecast
PointForecast95% LB80% LB80% UB95% UB
26172.4810696524-117.467047794635-17.1058139699319362.067953274732462.429187099435
27172.4810696524-157.930523819749-43.5634588329983388.525598137798502.892663124549
28172.4810696524-193.952732541843-67.1171150972524412.079254402053538.914871846643
29172.4810696524-226.737719585099-88.5540658795414433.516205184342571.699858889899
30172.4810696524-257.027430810606-108.359443502361453.321582807161601.989570115406
31172.4810696524-285.317425604744-126.857276831775471.819416136575630.279564909544
32172.4810696524-311.958165494207-144.276720342938489.238859647738656.920304799008
33172.4810696524-337.208328745443-160.786914662897505.749053967697682.170468050243
34172.4810696524-361.265305631667-176.516926353926521.479065658726706.227444936467
35172.4810696524-384.283785038164-191.567901041492536.530040346292729.245924342964
36172.4810696524-406.387666511793-206.020852284771550.982991589571751.349805816593
37172.4810696524-427.678012590013-219.941861325114564.904000629914772.640151894813
38172.4810696524-448.238540387254-233.385667841366578.347807146166793.200679692054
39172.4810696524-468.139523661229-246.398221373886591.360360678686813.101662966029
40172.4810696524-487.440633936163-259.018539035180603.98067833998832.402773240963
41172.4810696524-506.193053293919-271.280086989757616.242226294557851.15519259872
42172.4810696524-524.441074813308-283.211826925147628.173966229947869.403214118108
43172.4810696524-542.223334794925-294.839021760419639.80116106522887.185474099725
44172.4810696524-559.573775305361-306.183865020003651.146004324803904.53591461016
45172.4810696524-576.522405850789-317.265978865288662.228118170088921.484545155589
46172.4810696524-593.095913155718-328.10281280751673.06495211231938.058052460518
47172.4810696524-609.318154506403-338.709966287676683.672105592476954.280293811203
48172.4810696524-625.210560731818-349.101452171652694.063591476452970.172700036618
49172.4810696524-640.792468263815-359.28991387264704.25205317744985.754607568615


Actuals and Interpolation
TimeActualForecast
1724726.178301126702
2762.275724.988015993228
3721.125745.362675029711
4653.275732.118526227605
5663.7125689.036201204222
6735.5125675.198617089505
7628.1375708.15582272578
8792.55664.431555782846
9636.5734.439084864176
10800.825680.922408372177
11728.05746.440564025738
12618.2625736.39144148354
13450.625671.84245826112
14767.525550.963003806743
15675.65669.298582567855
16583.25672.769176074555
17690.7875623.853375069715
18208.0625660.4280675445
19142.5413.24277101072
20205.925265.301289743849
21462.5625232.856411440937
22251.4375358.374292751612
23195.725299.941015059498
24191.625242.994446986505
25137.25214.924733247989


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/13/t1273757456jx2b2fx3v3ls3pn/18i3r1273757416.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273757456jx2b2fx3v3ls3pn/18i3r1273757416.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/13/t1273757456jx2b2fx3v3ls3pn/28i3r1273757416.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t1273757456jx2b2fx3v3ls3pn/28i3r1273757416.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
 




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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