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FM50,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 13:25:51 +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/t12737571874l1rdlkbk1x1dcz.htm/, Retrieved Thu, 13 May 2010 15:26:30 +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/t12737571874l1rdlkbk1x1dcz.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:
FM50,steven,coomans,thesis,ETS,per2maand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
1201.42 1157.2125 1722.05 1918.2475 1930.4575 1264.1775 1456.3725 2168.985 1983.765 1672.695 1938.575 1307.6425 1523.3425 1928.39 2208.435 2290.175 2578.245 1152.84 1398.7575 1393.9175 1972.2525 2410.4775 2363.27 1341.6075 1437.0425
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Serverwessa.org @ wessa.org


Demand Forecast
PointForecast95% LB80% LB80% UB95% UB
261681.979872481491247.641279271901397.981131435841965.978613527142116.31846569108
272129.165822785241469.950341438931698.127988001332560.203657569162788.38130413156
282266.190396916121464.157166891951741.769055675272790.611738156983068.22362694030
292390.248192070801449.607350050611775.196204781833005.300179359783330.889034091
301318.44583127858751.816261231706947.946669913711688.944992643441885.07540132544
311493.76446504982801.5199547705721041.130109068931946.398821030712186.00897532907
321917.62131038867968.3642687065621296.935492961562538.307127815772866.87835207077
332127.127762920401010.535611810441397.027398517232857.228127323573243.71991403036
342231.48850891680996.5114148158721423.980390991783038.996626841813466.46560301772
352228.53512834891934.3482643616541382.311822853153074.758433844673522.72199233616
361344.63743152739528.442557552127810.95628902521878.318574029572160.83230550265
371447.39925728933532.122686958468848.9320758175882045.866438761072362.67582762019
381681.98178959975577.043696578176959.5016041926862404.461975006812786.91988262132
392129.16824960541679.6865906415961181.403135590223076.933363620593578.64990856922
402266.19297991670670.8325125037921223.042840042693309.343119790713861.55344732961
412390.25091647228653.4573384803581254.622636330343525.879196614224127.0444944642
421318.44733404125331.29153062565672.9808482881561963.913819794352305.60313745685
431493.76616764043343.028420108815741.3391978017532246.19313747912644.50391517204
441917.62349609070399.713518594266925.1155095356172910.131482645783435.53347358713
452127.13018741758399.14574118992997.2618889348133256.998485900363855.11463364525
462231.49105236421373.1454919453021016.384202920453446.597901807984089.83661278312
472228.53766843007327.891288549986985.7717998247823471.303537035354129.18404831015
481344.63896414321171.260302652134577.4078963930542111.870031893372518.01762563429
491447.40090703293156.164210993368603.1066126729932291.695201392882738.6376030725


Actuals and Interpolation
TimeActualForecast
11201.421201.52699174648
21157.21251157.41646441400
31722.051721.96970488708
41918.24751918.17344273383
51930.45751930.48017222676
61264.17751263.99969486656
71456.37251456.29994042014
82168.9852168.79723376114
91983.7651983.89302511237
101672.6951672.92990971789
111938.5751938.52541901855
121307.64251307.50607718124
131523.34251523.21360851071
141928.391928.25609237535
152208.4352208.49199362838
162290.1752290.220544191
172578.2452578.19488211031
181152.841153.01584440369
191398.75751398.7598374143
201393.91751394.14187074320
211972.25251972.09241634781
222410.47752410.25436087483
232363.272363.22252889700
241341.60751341.65173666235
251437.04251437.06301486057


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/13/t12737571874l1rdlkbk1x1dcz/1r5w11273757139.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t12737571874l1rdlkbk1x1dcz/1r5w11273757139.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/13/t12737571874l1rdlkbk1x1dcz/2keem1273757139.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/13/t12737571874l1rdlkbk1x1dcz/2keem1273757139.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):
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
 




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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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