Home » date » 2010 » Jun » 03 »

B11A,steven,coomans,thesis,permaand,ets,aangepastebroncode

*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, 03 Jun 2010 11:31:54 +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/Jun/03/t1275564813djtde90uk1paxov.htm/, Retrieved Thu, 03 Jun 2010 13:33:36 +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/Jun/03/t1275564813djtde90uk1paxov.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:

B11A,steven,coomans,thesis,permaand,ets,aangepastebroncode

Dataseries X:

» Textbox « » Textfile « » CSV «

62 30 31 50 33 12 20 30 21.5 23 13.5 0.5 12 10 70.5 30 20.5 12 20 45 11.505 0 10 5.5 27.5 0.5 7 0 2.5 0 0 6.025 1 0 0 0 0 2 0 6 20 0 0 0 7 35 0 0 0 1

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	4 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	3.88029461125816	-21.4416733718269	-12.676850299243	20.4374395217593	29.2022625943432
52	3.82687967221445	-21.4951216171418	-12.7302870160869	20.3840463605158	29.1488809615707
53	3.77793517618638	-21.5441213579153	-12.7792676347124	20.3351379870851	29.0999917102880
54	3.73308697951463	-21.589049749093	-12.8241682677531	20.2903422267823	29.0552237081222
55	3.69199225165074	-21.630251602923	-12.8653330415212	20.2493175448227	29.0142361062245
56	3.65433685447688	-21.6680422204792	-12.9030768545507	20.2117505635045	28.9767159294330
57	3.61983294095742	-21.7027099662161	-12.9376878921992	20.177353774114	28.9423758481309
58	3.58821675476605	-21.7345186102682	-12.9694299198217	20.1458634293537	28.9109521198003
59	3.55924661406814	-21.7637094606230	-12.9985443748073	20.1170376029435	28.8822026887593
60	3.53270106404587	-21.7905033050139	-13.0252522757907	20.0906544038825	28.8559054331056
61	3.50837718404359	-21.8151021803545	-13.0497559655799	20.0665103336670	28.8318565484416
62	3.4860890363929	-21.8376909857416	-13.0722407027576	20.0444187755434	28.8098690585274
63	3.46566624505975	-21.8584389534558	-13.0928761154979	20.0242086056174	28.7897714435754
64	3.44695269324854	-21.8775009909679	-13.1118175298612	20.0057229163583	28.771406377465
65	3.42980533000722	-21.8950189056890	-13.1292071836919	19.9888178437063	28.7546295657034
66	3.41409307671101	-21.9111225230707	-13.1451753362089	19.9733614896309	28.7393086764927
67	3.39969582506537	-21.9259307076462	-13.1598412824549	19.9592329325857	28.7253223577769
68	3.38650351896898	-21.9395522956923	-13.1733142809309	19.9463213188688	28.7125593336302
69	3.374415313218	-21.9520869473793	-13.1856944019889	19.9345250284249	28.7009175738153
70	3.36333880262082	-21.9636259255408	-13.1970733038727	19.9237509091143	28.6903035307824
71	3.35318931563021	-21.9742528075334	-13.2075349426768	19.9139135739372	28.6806314387938
72	3.34388926709336	-21.9840441360668	-13.2171562219372	19.9049347561239	28.6718226702536
73	3.33536756517211	-21.9930700143426	-13.2260075870571	19.8967427174013	28.6638051446868
74	3.32755906789954	-22.0013946503594	-13.2341535693146	19.8892717051136	28.6565127861585

Actuals and Interpolation
Time	Actual	Forecast
1	62	49.1485946234278
2	30	45.3349835319040
3	31	41.8176511556289
4	50	38.5909798541646
5	33	35.6520298034287
6	12	32.9504078576450
7	20	30.4539152863161
8	30	28.1658962839276
9	21.5	26.0766501187039
10	23	24.156446356987
11	13.5	22.3980495637221
12	0.5	20.7779239031914
13	12	19.2763140211818
14	10	17.9029571235767
15	70.5	16.6398256486149
16	30	15.5438391587265
17	20.5	14.5275375931142
18	12	13.5952912424967
19	20	12.7363232603913
20	45	11.9577990661807
21	11.505	11.2759453783387
22	0	10.6345631046696
23	10	10.0354091488450
24	5.5	9.491781865029
25	27.5	8.98941596678817
26	0.5	8.55085675842196
27	7	8.1309880805476
28	0	7.74915685785539
29	2.5	7.39159969411268
30	0	7.06270326550288
31	0	6.75629871592574
32	6.025	6.47193662930408
33	1	6.21434038969776
34	0	5.97297356764721
35	0	5.74809890698749
36	0	5.53896241148096
37	0	5.34435520194198
38	2	5.16316210918967
39	0	4.99648594330416
40	6	4.84004626539840
41	20	4.70048199890951
42	0	4.58831288025937
43	0	4.4728445018471
44	0	4.36461125530118
45	7	4.26306411947283
46	35	4.17515697240404
47	0	4.12606441076908
48	0	4.06097419798148
49	0	3.99910621856815
50	1	3.94022356081926

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/03/t1275564813djtde90uk1paxov/1aqay1275564709.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jun/03/t1275564813djtde90uk1paxov/1aqay1275564709.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jun/03/t1275564813djtde90uk1paxov/2lz901275564709.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jun/03/t1275564813djtde90uk1paxov/2lz901275564709.ps (open in new window)

Parameters (Session):

par1 = Input box ; par2 = ETS ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B11AEM ; par9 = 3 ; par10 = 0.1 ;

Parameters (R input):

par1 = Input box ; par2 = ETS ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B11AEM ; 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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