Home » date » 2010 » May » 13 »

B28A,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 11:56:00 +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/t1273751815rgmq4io3cp8hw5p.htm/, Retrieved Thu, 13 May 2010 13:56:58 +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/t1273751815rgmq4io3cp8hw5p.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:

B28A,steven,coomans,thesis,ETS

Dataseries X:

» Textbox « » Textfile « » CSV «

266,25 235,25 323,775 305,25 383,527 515,25 496,15 115,25 170,5 154,25 170 534,05 193,75 564,5 346 308,25 437,05 410,275 149,75 154,75 240,1 127,525 222,25 85,525 427,75 63,5 118,3 99,5 182,25 401 119,5 450,25 147,5 237 80,025 10,5 176,75 234 282,5 320 167,5 163,25 238,15 325,125 126,3 154,875 327,25 336,25 188 277,25

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	13 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	232.996205083228	8.85485470356377	86.4380536334458	379.554356533010	457.137555462892
52	232.996205083228	8.18559516261072	86.0004483410887	379.991961825367	457.806815003845
53	232.996205083228	7.51754848946103	85.5636361003614	380.428774066094	458.474861676994
54	232.996205083228	6.85070303990437	85.1276092975238	380.864800868932	459.141707126551
55	232.996205083228	6.18504732484305	84.692360420259	381.300049746196	459.807362841612
56	232.996205083228	5.52057000741422	84.2578820557907	381.734528110665	460.471840159041
57	232.996205083228	4.85725990017806	83.8241668890451	382.16824327741	461.135150266277
58	232.996205083228	4.19510596237433	83.3912077008573	382.601202465598	461.797304204081
59	232.996205083228	3.53409729724157	82.9589973662178	383.033412800238	462.458312869214
60	232.996205083228	2.87422314940008	82.5275288525618	383.464881313893	463.118187017055
61	232.996205083228	2.21547290229356	82.0967952180964	383.895614948359	463.776937264162
62	232.996205083228	1.55783607569171	81.6667896101673	384.325620556288	464.434574090764
63	232.996205083228	0.901302323248927	81.2375052636626	384.754904902793	465.091107843206
64	232.996205083228	0.245861430118822	80.8089354994534	385.183474667002	465.746548736337
65	232.996205083228	-0.408496689376506	80.3810737228694	385.611336443586	466.400906855832
66	232.996205083228	-1.06178199402524	79.9539134222089	386.038496744246	467.054192160481
67	232.996205083228	-1.71400431795345	79.5274481672826	386.464961999173	467.706414484409
68	232.996205083228	-2.36517337280225	79.1016716079895	386.890738558466	468.357583539258
69	232.996205083228	-3.01529874985684	78.6765774729256	387.31583269353	469.007708916312
70	232.996205083228	-3.66438992212855	78.2521595680215	387.740250598434	469.656800088584
71	232.996205083228	-4.31245624639089	77.8284117752119	388.163998391243	470.304866412846
72	232.996205083228	-4.95950696517065	77.4053280511334	388.587082115322	470.951917131626
73	232.996205083228	-5.6055512086964	76.9829024258504	389.009507740605	471.597961375152
74	232.996205083228	-6.25059799680329	76.5611290016097	389.431281164846	472.243008163259

Actuals and Interpolation
Time	Actual	Forecast
1	266.25	266.311321524294
2	235.25	235.417067752269
3	323.775	323.615181804147
4	305.25	305.168540483468
5	383.527	383.175856621936
6	515.25	514.478001121786
7	496.15	495.530485498141
8	115.25	115.889317709444
9	170.5	170.941626674590
10	154.25	154.728866537332
11	170	170.405904769100
12	534.05	533.12955044254
13	193.75	194.095120183447
14	564.5	563.545138664681
15	346	345.876293463356
16	308.25	308.257418801685
17	437.05	436.641950834438
18	410.275	409.989624466476
19	149.75	150.289953421438
20	154.75	155.256078086846
21	240.1	240.305758401530
22	127.525	128.097039005303
23	222.25	222.473306779830
24	85.525	86.2214108124829
25	427.75	427.154471037843
26	63.5	64.2725456009076
27	118.3	118.852239647829
28	99.5	100.108382885394
29	182.25	182.501059010644
30	401	400.322893463908
31	119.5	120.022652560074
32	450.25	449.383742888922
33	147.5	147.923303679596
34	237	237.045320771771
35	80.025	80.7016272696347
36	10.5	11.4554792842250
37	176.75	176.947324415927
38	234	233.922575170563
39	282.5	282.206216960764
40	320	319.563768371117
41	167.5	167.770320599499
42	163.25	163.525233477721
43	238.15	238.072110692052
44	325.125	324.661368009176
45	126.3	126.749158639776
46	154.875	155.17779201064
47	327.25	326.745170688580
48	336.25	335.756140224028
49	188	188.192458229121
50	277.25	277.042963328486

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273751815rgmq4io3cp8hw5p/1nq1h1273751746.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273751815rgmq4io3cp8hw5p/1nq1h1273751746.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273751815rgmq4io3cp8hw5p/2nq1h1273751746.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273751815rgmq4io3cp8hw5p/2nq1h1273751746.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 <- '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')

-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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