Home » date » 2010 » Apr » 26 »

B611,steven,coomans,thesis,forecast,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 13:56:50 +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/t12722904886xrg6j7d4xpzmnr.htm/, Retrieved Mon, 26 Apr 2010 16:01:30 +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/Apr/26/t12722904886xrg6j7d4xpzmnr.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,forecast,croston,permaand

Dataseries X:

» Textbox « » Textfile « » CSV «

0 0.5 0 0.5 0 0 0 4.5 4.5 10 3.75 58.45 7.45 22.275 22.21 5.8 36.43 31.65 50 43 0 68.5 33.5 23 0.5 69.25 32 39,213 46.426 46.855 153.135 64 31 2.25 2.25 2.3 22.6 1.5 10.65 34 81.75 106.5 0.525 24.025 5.25 9 12.8 25.05 0.3 75.75 54.75 1.526 102 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	5 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
89	42.4449816896765	-17.486183220823	3.25809926643332	81.6318641129196	102.376146600176
90	42.4449816896765	-17.7850936282640	3.06265225636827	81.8273111229847	102.675057007617
91	42.4449816896765	-18.0825279078617	2.86817043442063	82.0217929449323	102.972491287215
92	42.4449816896765	-18.3785077149554	2.67463964092516	82.2153237384278	103.268471094308
93	42.4449816896765	-18.6730541805203	2.48204605907988	82.4079173202731	103.563017559873
94	42.4449816896765	-18.9661879287729	2.29037620343445	82.5995871759185	103.856151308126
95	42.4449816896765	-19.2579290940236	2.09961690887087	82.790346470482	104.147892473377
96	42.4449816896765	-19.548297336816	1.90975532005076	82.9802080593022	104.438260716169
97	42.4449816896765	-19.8373118593880	1.72077888130551	83.1691844980475	104.727275238741
98	42.4449816896765	-20.1249914204915	1.53267532694667	83.3572880524063	105.014954799844

Actuals and Interpolation
Time	Actual	Forecast
1	0	NA
2	0.5	NA
3	0	0.25
4	0.5	0.25
5	0	0.25
6	0	0.25
7	0	0.25
8	4.5	0.25
9	4.5	0.409090909090909
10	10	0.60576923076923
11	3.75	1.08215010141988
12	58.45	1.22445060806486
13	7.45	4.42620235883893
14	22.275	4.60317876515579
15	22.21	5.68222292183592
16	5.8	6.73230145884357
17	36.43	6.67082673591613
18	31.65	8.70230192769576
19	50	10.3201438271359
20	43	13.2026545533481
21	0	15.4281382898409
22	68.5	15.4281382898409
23	33.5	18.1073612864325
24	23	19.2354225596963
25	0.5	19.5188849719695
26	69.25	18.0505427550403
27	32	22.0955766284452
28	39.213	22.8948602472724
29	46.426	24.2376448062988
30	46.855	26.0963992922845
31	153.135	27.8640673133504
32	64	38.6920867129805
33	31	40.9096998383156
34	2.25	40.0304794602628
35	2.25	36.6402430694141
36	2.3	33.5222239475904
37	22.6	30.6647637477155
38	1.5	29.9203635247716
39	10.65	27.2767360320706
40	34	25.7192643044733
41	81.75	26.4998825937528
42	106.5	31.7382869668445
43	0.525	38.8635976359932
44	24.025	35.1924339553087
45	5.25	34.118524073019
46	9	31.3317099801819
47	12.8	29.16842879075
48	25.05	27.5778349475401
49	0.3	27.3314978305258
50	75.75	24.6905477905730
51	54.75	29.6905096700156
52	1.526	32.1495472812321
53	102	29.1388839334106
54	3.752	36.3141202793457
55	17.25	33.102572024096
56	9.2	31.5369114376114
57	50.25	29.3281022302393
58	2.25	31.3992934872701
59	3.95	28.510721034102
60	60	26.0746541221805
61	55.8	29.4422988599490
62	6.75	32.0606522375897
63	61.95	29.5446493440406
64	7.025	32.767818138103
65	85.75	30.2059591749421
66	18.525	35.7362278415582
67	6	34.021838860801
68	25.35	31.2295258955398
69	46.775	30.6434379625934
70	51.025	32.251988279695
71	30	34.1244640074976
72	3	33.7129719948635
73	30	30.6480724731703
74	44	30.5833867484609
75	80.75	31.9227834274344
76	27.5	36.7980862576207
77	39.725	35.8695493045712
78	29.25	36.2546197401134
79	32.725	35.5549339492932
80	56.25	35.2722228125884
81	28.65	37.3681172482578
82	51.75	36.4970099899227
83	32.26	38.0211996367501
84	72	37.4454568127827
85	65.4	40.8988751893646
86	33.75	43.3476883555938
87	77.85	42.3883776203165
88	10.875	45.9330164519578

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Apr/26/t12722904886xrg6j7d4xpzmnr/1u2ly1272290205.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Apr/26/t12722904886xrg6j7d4xpzmnr/1u2ly1272290205.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Apr/26/t12722904886xrg6j7d4xpzmnr/24bkj1272290205.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Apr/26/t12722904886xrg6j7d4xpzmnr/24bkj1272290205.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B611crostonm ; par9 = 3 ; par10 = 0.1 ;

Parameters (R input):

par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B611crostonm ; par9 = 3 ; par10 = 0.1 ;

R code (references can be found in the software module):

par10 <- '0.1'

par9 <- '3'

par8 <- 'B611crostonm'

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')

-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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