Home » date » 2010 » May » 13 »

B611,steven,coomans,thesis,Arima

*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 12:16:42 +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/t12737530409zgjr2ujd70qusu.htm/, Retrieved Thu, 13 May 2010 14:17:23 +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/t12737530409zgjr2ujd70qusu.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:

B611,steven,coomans,thesis,Arima

Dataseries X:

» Textbox « » Textfile « » CSV «

10.65 34 81.75 106.5 0.525 24.025 5.25 9 12.8 25.05 0.3 75.75 54.75 1.526 1.02 3.752 17.25 9.2 50.25 2.25 3.95 60 55.8 6.75 61.95 7.025 85.75 18.525 6 25.35 46.775 51.025 30 3 30 44 80.75 27.5 39.725 29.25 32.725 56.25 28.65 51.75 32.26 72 65.4 33.75 77.85 10.875

Output produced by software:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
52	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
53	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
54	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
55	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
56	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
57	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
58	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
59	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
60	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
61	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
62	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
63	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
64	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
65	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
66	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
67	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
68	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
69	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
70	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
71	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
72	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
73	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494
74	34.00466	-19.6419368235494	-1.07296422557655	69.0822842255766	87.6512568235494

Actuals and Interpolation
Time	Actual	Forecast
1	10.65	34.00466
2	34	34.00466
3	81.75	34.00466
4	106.5	34.00466
5	0.525	34.00466
6	24.025	34.00466
7	5.25	34.00466
8	9	34.00466
9	12.8	34.00466
10	25.05	34.00466
11	0.3	34.00466
12	75.75	34.00466
13	54.75	34.00466
14	1.526	34.00466
15	1.02	34.00466
16	3.752	34.00466
17	17.25	34.00466
18	9.2	34.00466
19	50.25	34.00466
20	2.25	34.00466
21	3.95	34.00466
22	60	34.00466
23	55.8	34.00466
24	6.75	34.00466
25	61.95	34.00466
26	7.025	34.00466
27	85.75	34.00466
28	18.525	34.00466
29	6	34.00466
30	25.35	34.00466
31	46.775	34.00466
32	51.025	34.00466
33	30	34.00466
34	3	34.00466
35	30	34.00466
36	44	34.00466
37	80.75	34.00466
38	27.5	34.00466
39	39.725	34.00466
40	29.25	34.00466
41	32.725	34.00466
42	56.25	34.00466
43	28.65	34.00466
44	51.75	34.00466
45	32.26	34.00466
46	72	34.00466
47	65.4	34.00466
48	33.75	34.00466
49	77.85	34.00466
50	10.875	34.00466

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737530409zgjr2ujd70qusu/12ebd1273752999.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737530409zgjr2ujd70qusu/12ebd1273752999.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737530409zgjr2ujd70qusu/22ebd1273752999.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737530409zgjr2ujd70qusu/22ebd1273752999.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):

par10 <- '0.1'

par9 <- '3'

par8 <- 'dumresult'

par7 <- 'dum'

par6 <- '12'

par5 <- 'ZZZ'

par4 <- 'NA'

par3 <- 'NA'

par2 <- 'ETS'

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')

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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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