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B511,steven,coomans,thesis,Arima,per2maans

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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 13:07:42 +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/t12737561011v8nkh5suro4a5n.htm/, Retrieved Thu, 13 May 2010 15:08:24 +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/t12737561011v8nkh5suro4a5n.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,per2maans

Dataseries X:

» Textbox « » Textfile « » CSV «

66 71 50 66 66 44 76.75 66 65.75 76.75 65 76 88 75.5 97.5 98 88.0115 55.25 88.25 87 75.5 88 74.5 63.5 94

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
26	76.7198870208831	52.1626033566038	60.6627423112239	92.7770317305423	101.277170685162
27	72.4396156218972	47.2720045133619	55.9833992467121	88.8958319970824	97.6072267304326
28	85.7787188910023	59.8951207597308	68.8543437653835	102.703094016621	111.662317022274
29	77.9136871180322	47.124556552725	57.7817567412676	98.0456174947968	108.702817683339
30	76.2803281047119	44.4387902879284	55.4602656136031	97.1003905958208	108.121865921495
31	82.1074283488786	49.126914460032	60.5426294341499	103.672227263607	115.087942237725
32	78.534546799467	43.0160710292004	55.3102638084583	101.758829790476	114.053022569734
33	77.927364011871	41.199336080352	53.9121973729546	101.942530650788	114.65539194339
34	80.4698975432896	42.492360838829	55.6377210323776	105.302074054202	118.447434247750
35	78.8497766665359	39.1275470551075	52.8768067818907	104.822746551181	118.572006277964
36	78.6322917664592	37.6890714300748	51.8609589591146	105.403624573804	119.575512102844
37	79.7403358970438	37.5676987424297	52.1651306407476	107.31554115334	121.912973051658
38	79.0069576700331	35.4328644579266	50.5153895526393	107.498525787427	122.581050882140
39	78.9333718032634	34.1829112415015	49.6726184199678	108.194125186559	123.683832365025
40	79.4156635518898	33.4963163151171	49.3906162529303	109.440710850849	125.335010788662
41	79.0842296451002	31.9433550276365	48.2604685231173	109.907990767083	126.225104262564
42	79.0616796273257	30.8047781567617	47.5081877694405	110.615171485211	127.318581097890
43	79.2713367005089	29.9123859367542	46.9972535418977	111.54541985912	128.630287464263
44	79.1217865974078	28.6522202852772	46.1215109648704	112.122062229945	129.591352909538
45	79.1162286918621	27.5910690095511	45.4257376539378	112.806719729787	130.641388374173
46	79.2072490890864	26.6422374718936	44.8368354170759	113.577662761097	131.772260706279
47	79.1398698097783	25.5420321554530	44.0941274912912	114.185612128265	132.737707464104
48	79.1393601539852	24.5412941694982	43.4396036839464	114.839116624024	133.737426138472
49	79.1788220517245	23.5959242903419	42.8351186951949	115.522525408254	134.761719813107

Actuals and Interpolation
Time	Actual	Forecast
1	66	65.9340000655022
2	71	67.4510134015007
3	50	65.1096772035754
4	66	63.3900574001068
5	66	65.7651067120978
6	44	56.7231840728576
7	76.75	61.0647320143803
8	66	64.1024412875046
9	65.75	55.3499659970663
10	76.75	72.1767781103031
11	65	68.3625842416714
12	76	67.7363072850427
13	88	74.280295689305
14	75.5	72.3141534058821
15	97.5	78.2382585173593
16	98	87.6827804286996
17	88.0115	84.8565432816706
18	55.25	95.469375671624
19	88.25	86.4534430561582
20	87	81.6480522907467
21	75.5	68.8361541965404
22	88	85.1449520700068
23	74.5	84.9718409849175
24	63.5	77.7240276566534
25	94	79.8596794457166

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737561011v8nkh5suro4a5n/15em91273756057.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737561011v8nkh5suro4a5n/15em91273756057.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737561011v8nkh5suro4a5n/2y53c1273756057.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737561011v8nkh5suro4a5n/2y53c1273756057.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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