Home » date » 2010 » May » 13 »

B511,steven,coomans,thesis,ETS

*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 12:02:57 +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/t12737522346geryhy53724led.htm/, Retrieved Thu, 13 May 2010 14:03:56 +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/t12737522346geryhy53724led.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,thesis,ETS

Dataseries X:

» Textbox « » Textfile « » CSV «

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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	12 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	89.6262130710258	64.213703756041	73.0098663657172	106.242559776334	115.038722386011
52	75.4257142213661	49.5605171783716	58.5133709206559	92.3380575220762	101.290911264361
53	82.1335209930145	55.8234239490721	64.9302732179247	99.3367687681043	108.443618036957
54	76.2509038435137	49.5033059167763	58.7615896173587	93.7402180696687	102.998501770251
55	80.5675721061822	53.3895150888171	62.7967958093727	98.3383484029916	107.745629123547
56	100.629091956103	73.0272881812062	82.5812425473419	118.676941364863	128.230895730999
57	76.32296081571	48.3038180733522	58.0022281237199	94.6436935077002	104.342103558068
58	83.0969701222246	54.6666140146174	64.5073594630039	101.686580781445	111.527326229832
59	72.8695686256758	44.0338626902544	54.0149139602763	91.7242232910753	101.705274561097
60	57.193960996329	27.9585248753672	38.0779368113285	76.3099851813297	86.4293971172909
61	85.0135694760387	55.3833629855226	65.639418820795	104.387720131282	114.643775966555
62	93.3812957184519	63.3619365470878	73.7526918021736	113.009899634730	123.400654889816
63	89.6262130710258	59.222681804936	69.7464125239329	109.506013618119	120.029744337116
64	75.4257142213661	44.6428049722902	55.2978517446337	95.5535766980984	106.208623470442
65	82.1335209930145	50.9758527518644	61.7606167824631	102.506425203566	113.291189234165
66	76.2509038435137	44.7229308890988	55.6358703955088	96.8659372915186	107.778876797929
67	80.5675721061822	48.6735935678584	59.7132204703145	101.42192374205	112.461550644506
68	100.629091956103	68.3732606167649	79.5381374911554	121.720046421050	132.884923295440
69	76.32296081571	43.7092912310017	54.9980284985248	97.6478931328953	108.936630400418
70	83.0969701222246	50.1293461227722	61.5405994531489	104.653340791300	116.064594121677
71	72.8695686256758	39.5517502613529	51.084218169173	94.6549190821786	106.187386989999
72	57.193960996329	23.5295909747952	35.1820125909129	79.2059094017452	90.858331017863
73	85.0135694760387	51.0058024156372	62.7770858157733	107.250053136304	119.021336536440
74	93.3812957184519	59.0339376130128	70.922765408939	115.839826027965	127.728653823891

Actuals and Interpolation
Time	Actual	Forecast
1	66	66.6400081602945
2	66	74.8865290244873
3	66	69.4466779638461
4	76	54.592968576561
5	34	65.359813335731
6	66	53.5306148746235
7	66	60.2131403294856
8	66	81.3710894221322
9	66	54.1503901382023
10	66	63.170927562093
11	44	53.4785961678077
12	44	36.0086271515583
13	66	65.3413647125152
14	87.5	73.8332331092841
15	66	72.6698371634263
16	66	57.2076479997321
17	66	65.5773520419694
18	65.5	59.7783847805255
19	65.5	65.1808994846169
20	88	85.3000011788247
21	42	61.5082524102149
22	88	64.5826998014614
23	88	58.7924091111244
24	64	48.658793284746
25	88	79.3842209531546
26	88	89.3860473656974
27	88	85.3666565050138
28	63	71.669877609083
29	110	76.728036815868
30	85	77.1575352833932
31	88	82.9615773183237
32	108	103.975665764726
33	88.023	80.4326749481525
34	88	88.6492400856969
35	66	78.2967726105023
36	44.5	60.2928816562315
37	88.5	85.1148847421264
38	88	94.1239817721025
39	108	89.206744989429
40	66	78.5725460830362
41	85	82.895184727681
42	66	77.4130342232758
43	66	79.566127179505
44	110	97.0528674218656
45	83	75.2019256231291
46	66	83.457016080909
47	83	69.9167389332608
48	44	56.7242903583614
49	83	82.13007477616
50	105	90.6613214415051

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737522346geryhy53724led/11gvq1273752165.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737522346geryhy53724led/11gvq1273752165.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737522346geryhy53724led/2o45m1273752165.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737522346geryhy53724led/2o45m1273752165.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = ETS ; 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

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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