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B580,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 12:09:33 +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/t1273752610keg0cdfdtwij4k0.htm/, Retrieved Thu, 13 May 2010 14:10:13 +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/t1273752610keg0cdfdtwij4k0.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:

B580,steven,coomans,thesis,ETS

Dataseries X:

» Textbox « » Textfile « » CSV «

209 175 247,5 177 188,775 194,825 182,275 145,25 286,3 257,75 335 234,15 276,275 327,052 375,325 199,75 215,875 225 228,1 128,5 242,5 327,275 346,8 221,175 245,275 230,725 335,3 97,25 254,5 71,25 273,575 98,325 184,55 203,025 121,655 135 98,75 69,1 256,525 97,775 202,7 81,9 165,25 75,825 300 238,5 194,5 140,75 211,75 274,8

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	308.895645629986	213.713534478742	246.659408372625	371.131882887346	404.077756781229
52	155.285248212916	53.9640470318534	89.0348757465456	221.535620679287	256.606449393979
53	230.489560744847	123.380563296737	160.454751714542	300.524369775151	337.598558192956
54	163.552512894028	50.952828791874	89.9275368336609	237.177488954394	276.152196996181
55	221.055110594817	103.220309345482	144.007075422803	298.103145766831	338.889911844153
56	114.679998919142	-8.16702754481517	34.3546458365297	195.005352001754	237.527025383099
57	245.138712841876	117.476097184034	161.664615231751	328.612810452001	372.801328499717
58	265.437541147905	133.134498997819	178.929231744686	351.945850551124	397.740583297991
59	255.408202255407	118.622068134535	165.968556573073	344.847847937740	392.194336376279
60	199.833165879081	58.706279662107	107.55525495908	292.111076799083	340.960052096056
61	216.449430570969	71.1086939351686	121.416230960046	311.482630181893	361.79016720677
62	223.883534276246	74.4503343125067	126.17441672692	321.592651825572	373.316734239985
63	308.895645629986	155.479112045862	208.581966222607	409.209325037364	462.312179214109
64	155.285248212916	-2.01378008202968	52.432941966412	258.137554459420	312.584276507862
65	230.489560744847	69.4015853149597	125.159795131308	335.819326358386	391.577536174734
66	163.552512894028	-1.23731492815799	55.8022361046781	271.302789683377	328.342340716213
67	221.055110594817	52.6447816977118	110.937515396957	331.172705792677	389.465439491923
68	114.679998919142	-57.2746183634335	2.24491811186807	227.115079726415	286.634616201717
69	245.138712841876	69.711400457622	130.432958757277	359.844466926475	420.56602522613
70	265.437541147905	86.6049561530887	148.505198983323	382.369883312487	444.270126142722
71	255.408202255407	73.2339862381726	136.290885008613	374.5255195022	437.582418272641
72	199.833165879081	14.3775202193721	78.5702371166125	321.09609464155	385.288811538791
73	216.449430570969	27.7673481688557	93.0768482174273	339.822012924511	405.131512973083
74	223.883534276246	32.0313001345408	98.4381011168046	349.328967435687	415.735768417951

Actuals and Interpolation
Time	Actual	Forecast
1	209	207.578503057629
2	175	215.531763346576
3	247.5	285.751097248607
4	177	118.186987745813
5	188.775	214.853226812988
6	194.825	138.401727102421
7	182.275	216.486643563448
8	145.25	97.6257034058337
9	286.3	245.459409265308
10	257.75	280.672235749570
11	335	262.276026647169
12	234.15	233.243533537997
13	276.275	250.184476503336
14	327.052	267.134675437714
15	375.325	374.008804029831
16	199.75	220.89450878188
17	215.875	288.373712865628
18	225	194.992809298708
19	228.1	263.427632470548
20	128.5	144.169115657069
21	242.5	268.90584331271
22	327.275	279.572485861439
23	346.8	286.95899170416
24	221.175	253.216947852348
25	245.275	258.137771298881
26	230.725	260.877650429826
27	335.3	334.877745498272
28	97.25	181.434595292499
29	254.5	225.904002981947
30	71.25	169.425310278149
31	273.575	191.077796826959
32	98.325	114.816648313125
33	184.55	239.251961739029
34	203.025	239.601909862737
35	121.655	216.235737726035
36	135	126.133353910805
37	98.75	145.984252152785
38	69.1	136.179714477329
39	256.525	196.708490866528
40	97.775	64.9287086805014
41	202.7	152.116753990838
42	81.9	103.646395070923
43	165.25	153.209605600343
44	75.825	51.2266305836655
45	300	190.651450747171
46	238.5	250.866635894662
47	194.5	236.328106963891
48	140.75	165.487705941164
49	211.75	173.069538773582
50	274.8	194.613497545848

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273752610keg0cdfdtwij4k0/1u8la1273752561.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752610keg0cdfdtwij4k0/1u8la1273752561.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752610keg0cdfdtwij4k0/2u8la1273752561.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273752610keg0cdfdtwij4k0/2u8la1273752561.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 = 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 <- 'ARIMA'

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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