Home » date » 2010 » May » 13 »

B511,steven,coomans,thesis,Arima

*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 12:05:04 +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/t1273752356ayd1ok9t1ctck3h.htm/, Retrieved Thu, 13 May 2010 14:05:59 +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/t1273752356ayd1ok9t1ctck3h.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,Arima

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	39 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	88.6291346731998	56.3125085787798	67.4984286545673	109.759840691832	120.945760767620
52	76.8246000121716	44.1766032603693	55.4772223668697	98.1719776574735	109.472596763974
53	78.2693837561892	45.2933460669089	56.7075116710385	99.83125584134	111.245421445470
54	74.2272753516727	40.9264280431735	52.4530216721165	96.0015290312289	107.528122660172
55	73.8578060427836	40.2352867849539	51.8732223808869	95.8423897046802	107.480325300613
56	87.8073839587263	53.8662412109892	65.6144635949662	110.000304322486	121.748526706463
57	78.82218638408	44.5653835443313	56.4228669801904	101.221505787969	113.078989223829
58	74.8181445394849	40.2485638278165	52.2143106778869	97.421978401083	109.387725251153
59	83.7914264307104	48.9118725293242	60.9849119945237	106.597940866897	118.670980332097
60	71.718511305097	36.5317147742029	48.7111017116759	94.725920898518	106.905307835991
61	80.7467876827847	45.2558998038963	57.5405434443576	103.953031921212	116.237675561673
62	88.2136969018796	52.4208202481556	64.8099928171389	111.617400986620	124.006573555604
63	80.3185846141482	41.1159570911982	54.6853642734753	105.951804954821	119.521212137098
64	80.3185846141482	40.6178036222229	54.3596392279732	106.277530000323	120.019365606074
65	80.3185846141482	40.125823847631	54.0379509442507	106.599218284046	120.511345380665
66	80.3185846141482	39.6397937680556	53.7201529569944	106.917016271302	120.997375460241
67	80.3185846141482	39.1595026107092	53.4061074492864	107.23106177901	121.477666617587
68	80.3185846141482	38.6847517611490	53.0956845541226	107.541484674174	121.952417467147
69	80.3185846141482	38.2153538034712	52.7887617268307	107.848407501466	122.421815424825
70	80.3185846141482	37.7511316557810	52.4852231797831	108.151946048513	122.886037572515
71	80.3185846141482	37.2919177896256	52.1849593720125	108.452209856284	123.345251438671
72	80.3185846141482	36.8375535236354	51.8878665473483	108.749302680948	123.799615704661
73	80.3185846141482	36.3878883829274	51.5938463155528	109.043322912744	124.249280845369
74	80.3185846141482	35.9427795169394	51.3028052716641	109.334363956632	124.694389711357

Actuals and Interpolation
Time	Actual	Forecast
1	66	65.9340000642625
2	66	65.9999544970003
3	66	65.9999741429027
4	76	67.2912652912436
5	34	65.097618300786
6	66	61.2705546832229
7	66	62.1803867658173
8	66	62.85982112799
9	66	63.3873521146392
10	66	63.8082659858302
11	44	62.7780896791892
12	44	59.9095531527368
13	66	59.6936234581407
14	87.5	60.8356206794414
15	66	64.5842558764913
16	66	67.8930159267455
17	66	54.5864341559968
18	65.5	66.1702603554484
19	65.5	66.0732597482336
20	88	66.1506824734975
21	42	68.9786501509172
22	88	65.4118136535975
23	88	61.8306169774286
24	64	65.4303451710531
25	88	71.7459994615169
26	88	81.541820717718
27	88	75.0341722880627
28	63	75.7932175260843
29	110	78.5471215921968
30	85	79.4125986158928
31	88	80.2193112847716
32	108	89.1470965719674
33	88.023	75.9111663510669
34	88	93.5794162521968
35	66	95.1408210293909
36	44.5	82.6263841746852
37	88.5	83.2819192115887
38	88	81.4190987011273
39	108	84.9800826115048
40	66	79.8950729834965
41	85	92.7859883215547
42	66	84.1762183576399
43	66	82.6180545745621
44	110	84.5153822287424
45	83	86.7568550833349
46	66	80.6175653648853
47	83	69.9703995656468
48	44	67.2147242043591
49	83	77.1762590877615
50	105	78.7550058539266

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273752356ayd1ok9t1ctck3h/1m0bd1273752263.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752356ayd1ok9t1ctck3h/1m0bd1273752263.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752356ayd1ok9t1ctck3h/2m0bd1273752263.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752356ayd1ok9t1ctck3h/2m0bd1273752263.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = Input box ; par2 = ARIMA ; 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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