Home » date » 2010 » May » 13 »

FM50,steven,coomans,thesis,croston

*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 11:43:34 +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/t12737510559kys59h9duq01i4.htm/, Retrieved Thu, 13 May 2010 13:44:17 +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/t12737510559kys59h9duq01i4.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:

FM50,steven,coomans,thesis,croston

Dataseries X:

» Textbox « » Textfile « » CSV «

1216,67 1186,17 1217,475 1096,95 1685,6 1758,5 1786,6 2049,895 1845,895 2015,02 1609,63 918,725 1240,96 1671,785 2451,83 1886,14 2110,66 1856,87 1775,765 1569,625 1835,69 2041,46 1667,035 948,25 1365,66 1681,025 1661,9 2194,88 2051,025 2365,845 2398,5 2181,85 2626,77 2529,72 1700,3 605,38 1200,495 1597,02 1174,955 1612,88 1683,55 2260,955 2455,335 2365,62 2417,755 2308,785 1629,94 1053,275 1330,235 1543,85

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	3 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	1768.27401478368	803.140886756873	1137.20738246902	2399.34064709834	2733.40714281049
52	1768.27401478368	798.327225334404	1134.05989844349	2402.48813112388	2738.22080423296
53	1768.27401478368	793.537335515322	1130.92795783394	2405.62007173343	2743.01069405204
54	1768.27401478368	788.770868561446	1127.81133261274	2408.73669695463	2747.77716100592
55	1768.27401478368	784.027484178973	1124.70980027373	2411.83822929363	2752.52054538839
56	1768.27401478368	779.306850234952	1121.62314364686	2414.92488592051	2757.24117933241
57	1768.27401478368	774.608642485894	1118.55115072068	2417.99687884668	2761.93938708147
58	1768.27401478368	769.932544317864	1115.49361447249	2421.05441509487	2766.6154852495
59	1768.27401478368	765.278246497495	1112.45033270545	2424.09769686191	2771.26978306987
60	1768.27401478368	760.645446933332	1109.42110789258	2427.12692167479	2775.90258263403

Actuals and Interpolation
Time	Actual	Forecast
1	1216.67	NA
2	1186.17	1216.67
3	1217.475	1213.62
4	1096.95	1214.0055
5	1685.6	1202.29995
6	1758.5	1250.629955
7	1786.6	1301.4169595
8	2049.895	1349.93526355
9	1845.895	1419.931237195
10	2015.02	1462.5276134755
11	1609.63	1517.77685212795
12	918.725	1526.96216691516
13	1240.96	1466.13845022364
14	1671.785	1443.62060520128
15	2451.83	1466.43704468115
16	1886.14	1564.97634021303
17	2110.66	1597.09270619173
18	1856.87	1648.44943557256
19	1775.765	1669.29149201530
20	1569.625	1679.93884281377
21	1835.69	1668.90745853239
22	2041.46	1685.58571267915
23	1667.035	1721.17314141124
24	948.25	1715.75932727012
25	1365.66	1639.00839454310
26	1681.025	1611.67355508879
27	1661.9	1618.60869957991
28	2194.88	1622.93782962192
29	2051.025	1680.13204665973
30	2365.845	1717.22134199376
31	2398.5	1782.08370779438
32	2181.85	1843.72533701494
33	2626.77	1877.53780331345
34	2529.72	1952.46102298210
35	1700.3	2010.18692068389
36	605.38	1979.19822861550
37	1200.495	1841.81640575395
38	1597.02	1777.68426517856
39	1174.955	1759.61783866070
40	1612.88	1701.15155479463
41	1683.55	1692.32439931517
42	2260.955	1691.44695938365
43	2455.335	1748.39776344529
44	2365.62	1819.09148710076
45	2417.755	1873.74433839068
46	2308.785	1928.14540455161
47	1629.94	1966.20936409645
48	1053.275	1932.58242768681
49	1330.235	1844.65168491813
50	1543.85	1793.21001642631

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t12737510559kys59h9duq01i4/1xiq21273751010.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737510559kys59h9duq01i4/1xiq21273751010.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737510559kys59h9duq01i4/2xiq21273751010.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t12737510559kys59h9duq01i4/2xiq21273751010.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = Input box ; par2 = Croston ; 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')

-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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