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B580,steven,coomans,thesis,croston

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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 11:36:51 +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/t1273750650pqsy5o7h1mdc0b5.htm/, Retrieved Thu, 13 May 2010 13:37:33 +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/t1273750650pqsy5o7h1mdc0b5.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:

B580,steven,coomans,thesis,croston

Dataseries X:

» Textbox « » Textfile « » CSV «

209 175 247,5 177 188,775 194,825 182,275 145,25 286,3 257,75 335 234,15 276,275 327,052 375,325 199,75 215,875 225 228,1 128,5 242,5 327,275 346,8 221,175 245,275 230,725 335,3 97,25 254,5 71,25 273,575 98,325 184,55 203,025 121,655 135 98,75 69,1 256,525 97,775 202,7 81,9 165,25 75,825 300 238,5 194,5 140,75 211,75 274,8

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	3 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	189.531781251351	37.0034617837423	89.7988738380176	289.264688664684	342.060100718959
52	189.531781251351	36.2427173165450	89.3014497676743	289.762112735027	342.820845186156
53	189.531781251351	35.4857296810327	88.8064821575341	290.257080345167	343.577832821669
54	189.531781251351	34.7324437631004	88.3139349704207	290.749627532281	344.331118739601
55	189.531781251351	33.9828057831807	87.823773041765	291.239789460936	345.080756719521
56	189.531781251351	33.2367632514367	87.3359620503076	291.727600452394	345.826799251265
57	189.531781251351	32.4942649248703	86.8504684900528	292.213094012648	346.569297577831
58	189.531781251351	31.7552607662489	86.367259643413	292.696302859288	347.308301736452
59	189.531781251351	31.0197019047537	85.8863035554772	293.177258947224	348.043860597948
60	189.531781251351	30.2875405982647	85.4075690093512	293.65599349335	348.776021904437

Actuals and Interpolation
Time	Actual	Forecast
1	209	NA
2	175	209
3	247.5	205.6
4	177	209.79
5	188.775	206.511
6	194.825	204.7374
7	182.275	203.74616
8	145.25	201.599044
9	286.3	195.9641396
10	257.75	204.99772564
11	335	210.272953076
12	234.15	222.7456577684
13	276.275	223.88609199156
14	327.052	229.124982792404
15	375.325	238.917684513164
16	199.75	252.558416061847
17	215.875	247.277574455663
18	225	244.137317010096
19	228.1	242.223585309087
20	128.5	240.811226778178
21	242.5	229.58010410036
22	327.275	230.872093690324
23	346.8	240.512384321292
24	221.175	251.141145889163
25	245.275	248.144531300246
26	230.725	247.857578170222
27	335.3	246.144320353200
28	97.25	255.059888317880
29	254.5	239.278899486092
30	71.25	240.801009537482
31	273.575	223.845908583734
32	98.325	228.818817725361
33	184.55	215.769435952825
34	203.025	212.647492357542
35	121.655	211.685243121788
36	135	202.682218809609
37	98.75	195.913996928648
38	69.1	186.197597235784
39	256.525	174.487837512205
40	97.775	182.691553760985
41	202.7	174.199898384886
42	81.9	177.049908546398
43	165.25	167.534917691758
44	75.825	167.306425922582
45	300	158.158283330324
46	238.5	172.342454997291
47	194.5	178.958209497562
48	140.75	180.512388547806
49	211.75	176.536149693025
50	274.8	180.057534723723

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273750650pqsy5o7h1mdc0b5/1j7kb1273750607.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273750650pqsy5o7h1mdc0b5/1j7kb1273750607.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273750650pqsy5o7h1mdc0b5/2ugjw1273750607.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273750650pqsy5o7h1mdc0b5/2ugjw1273750607.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = dumresult ; par9 = 3 ; par10 = 0.1 ;

Parameters (R input):

par1 = Input box ; par2 = Croston ; 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 <- 'Croston'

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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