Home » date » 2010 » May » 13 »

FM22,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 11:46:41 +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/t1273751249dlvmntc7r9z36cr.htm/, Retrieved Thu, 13 May 2010 13:47:32 +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/t1273751249dlvmntc7r9z36cr.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:

FM22,steven,coomans,thesis,ETS

Dataseries X:

» Textbox « » Textfile « » CSV «

594.25 853.75 766.5 758.05 756.85 685.4 696.525 610.025 708.325 619.1 740.525 730.5 489.75 766.525 780.125 804.975 529.25 743.75 771.15 830.5 600 856.1 702.75 533.775 311.25 590 738 797.05 531.3 820 533.25 633.25 634.275 747.3 220.375 195.75 123.25 161.75 126.75 285.1 461.5 463.625 325.875 177 223 168.45 251.75 131.5 110.375 164.125

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	155.232509770838	-144.012196875557	-40.4330867955283	350.898106337205	454.477216417233
52	155.232509770838	-178.113810248867	-62.7309332615279	373.195952803204	488.578829790544
53	155.232509770838	-209.036818728657	-82.950401701364	393.415421243040	519.501838270333
54	155.232509770838	-237.532710558389	-101.582863865995	412.047883407672	547.997730100065
55	155.232509770838	-264.096588808141	-118.952050252293	429.417069793969	574.561608349817
56	155.232509770838	-289.075117363619	-135.284632191438	445.749651733114	599.540136905296
57	155.232509770838	-312.722233922016	-150.746650618853	461.21167016053	623.187253463693
58	155.232509770838	-335.230553832771	-165.464049941972	475.929069483648	645.695573374448
59	155.232509770838	-356.750294016781	-179.535051725917	490.000071267593	667.215313558457
60	155.232509770838	-377.401291249172	-193.038013026218	503.503032567894	687.866310790849
61	155.232509770838	-397.280965724391	-206.036633484141	516.501653025817	707.745985266067
62	155.232509770838	-416.469786200247	-218.5835287923	529.048548333976	726.934805741923
63	155.232509770838	-435.035132772274	-230.722756427058	541.187775968735	745.50015231395
64	155.232509770838	-453.034096618331	-242.491646303118	552.956665844794	763.499116160007
65	155.232509770838	-470.515553800268	-253.922156759549	564.387176301225	780.980573341945
66	155.232509770838	-487.521730726868	-265.041898160984	575.50691770266	797.986750268544
67	155.232509770838	-504.089405754936	-275.874918582345	586.339938124021	814.554425296612
68	155.232509770838	-520.250845247898	-286.442315864658	596.907335406335	830.715864789575
69	155.232509770838	-536.034542476093	-296.762720755987	607.227740297663	846.49956201777
70	155.232509770838	-551.465807856248	-306.852682848282	617.317702389958	861.930827397925
71	155.232509770838	-566.567245529583	-316.726982195051	627.192001736728	877.03226507126
72	155.232509770838	-581.359141937583	-326.398883387407	636.863902929083	891.82416147926
73	155.232509770838	-595.859785477445	-335.880344565518	646.345364107195	906.324805019121
74	155.232509770838	-610.085731613834	-345.182190765882	655.647210307558	920.55075115551

Actuals and Interpolation
Time	Actual	Forecast
1	594.25	699.997920798106
2	853.75	648.094696588097
3	766.5	749.03449041482
4	758.05	757.606916738666
5	756.85	757.824390989537
6	685.4	757.346140130168
7	696.525	722.033515408683
8	610.025	709.513418705798
9	708.325	660.682485072171
10	619.1	684.066397640522
11	740.525	652.179572089688
12	730.5	695.541299848975
13	489.75	712.699738883378
14	766.525	603.271485377383
15	780.125	683.399621312151
16	804.975	730.874398867526
17	529.25	767.244476743744
18	743.75	650.431960772313
19	771.15	696.234347016108
20	830.5	733.004468871136
21	600	780.857252864086
22	856.1	692.088845177539
23	702.75	772.588846288154
24	533.775	738.31052412253
25	311.25	637.920340681366
26	590	477.583912246844
27	738	532.760008036036
28	797.05	633.495958581145
29	531.3	713.771599162515
30	820	624.210837572008
31	533.25	720.30812930781
32	633.25	628.496205702196
33	634.275	630.829464355652
34	747.3	632.520603129091
35	220.375	688.856658963743
36	195.75	458.916359429194
37	123.25	329.748972922716
38	161.75	228.395088989555
39	126.75	195.684327678938
40	285.1	161.849961591333
41	461.5	222.343580086496
42	463.625	339.726401331110
43	325.875	400.538346355494
44	177	363.892061710736
45	223	272.161647449797
46	168.45	248.032113830012
47	251.75	208.971598350831
48	131.5	229.968105472534
49	110.375	181.63796226685
50	164.125	146.660655096268

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273751249dlvmntc7r9z36cr/1y8ge1273751188.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273751249dlvmntc7r9z36cr/1y8ge1273751188.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273751249dlvmntc7r9z36cr/2y8ge1273751188.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273751249dlvmntc7r9z36cr/2y8ge1273751188.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 <- '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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