Home » date » 2010 » Apr » 26 »

b511,steven,coomans,forecast,thesis,croston,permaand

*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: Mon, 26 Apr 2010 13:27:22 +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/Apr/26/t1272288748z2761o9zm7metw1.htm/, Retrieved Mon, 26 Apr 2010 15:32:31 +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/Apr/26/t1272288748z2761o9zm7metw1.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:
b511,steven,coomans,forecast,thesis,croston,permaand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
22.6 44.6 45.2 69 66 47 67.8 22.6 22.6 44.5 44.6 47 45.2 40.5 66 24.4 2.3 0 0 48 0 0 0 0 8 6 0 0 0 0.02 2 0 22 46.5 66 44 66 44 66 66 66 76 34 66 66 66 66 66 44 44 66 87.5 66.000 66 66 65.5 65.5 88 42 88 88 64 88 88 88 63 110 85 88 108 88.023 88 66 44.5 88.5 88 108 66 85 66 66 110 83 66 83 44 83 105
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Serverwessa.org @ wessa.org


Demand Forecast
PointForecast95% LB80% LB80% UB95% UB
8979.907364245657240.697949492318554.2697059735738105.545022517741119.116778998996
9079.907364245657240.502390100857654.1418365605333105.672891930781119.312338390457
9179.907364245657240.307796452494154.0145986129627105.800129878352119.506932038820
9279.907364245657240.114154379421153.8879828670308105.926745624283119.700574111893
9379.907364245657239.921450056891853.7619802832219106.052748208092119.893278434422
9479.907364245657239.729669991702053.6365820388037106.178146452511120.085058499612
9579.907364245657239.538801011163853.5117795206186106.302948970696120.275927480151
9679.907364245657239.348830252546853.3875643181794106.427164173135120.465898238767
9779.907364245657239.159745152963253.263928217055106.550800274259120.654983338351
9879.907364245657238.971533439671753.1408631925316106.673865298783120.843195051643


Actuals and Interpolation
TimeActualForecast
122.6NA
244.622.6
345.224.8
46926.84
56631.056
64734.5504
767.835.79536
822.638.995824
922.637.3562416
1044.535.88061744
1144.636.742555696
124737.5283001264
1345.238.47547011376
1440.539.147923102384
156639.2831307921456
1624.441.9548177129311
172.340.1993359416379
18036.4094023474741
19036.4094023474741
204836.4094023474741
21031.3070517606056
22031.3070517606056
23031.3070517606056
24031.3070517606056
25831.3070517606056
26621.9060860135785
27020.8610080889019
28020.8610080889019
29020.8610080889019
300.0220.8610080889019
31216.147253294258
32015.311532231519
332215.311532231519
3446.514.8112204041594
356616.7303751293463
364419.8367800144359
376621.4187024614594
384424.4417509474760
396625.8121036758916
406628.7147675156586
416631.4847084376460
427634.1165315789855
433437.3878764640517
446637.1173402597233
456639.4711618767186
466641.6739161525754
476643.7286338941152
486645.6394824777996
494447.4115493285947
504447.1107266150881
516646.8331481646340
5287.548.5620734890819
536652.1091750738346
546653.3859459919329
556654.5548135544771
5665.555.6232071101794
5765.556.5513665723761
588857.3974017937913
594260.3065526188112
608858.5576554701051
618861.383016951335
626463.9476041115951
638863.9526710222928
648866.2858418066584
658868.3989270643913
666370.3115179364795
6711069.5963568630292
688573.5570182299898
698874.6809603746687
7010875.9915023030596
7188.02379.1460740701206
728880.0221990759717
736680.8106106224677
7444.579.3452212119394
7588.575.8939181499635
768877.1437000124855
7710878.2209315770863
786681.1780889878318
798579.6698049229845
806680.199812128388
816678.7870573832083
8211077.5142113756624
838380.7493860030258
846680.9736112773917
858379.4812585703795
864479.8320730951588
878376.2585799913025
8810576.9310766849343


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Apr/26/t1272288748z2761o9zm7metw1/11sdc1272288435.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Apr/26/t1272288748z2761o9zm7metw1/11sdc1272288435.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Apr/26/t1272288748z2761o9zm7metw1/2c1cf1272288435.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Apr/26/t1272288748z2761o9zm7metw1/2c1cf1272288435.ps (open in new window)


 
Parameters (Session):
par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B511crostonm ; par9 = 3 ; par10 = 0.1 ;
 
Parameters (R input):
par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B511crostonm ; 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#output',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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