Home » date » 2010 » May » 13 »

FM22,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 12:20:51 +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/t1273753293uifznz32jyo6s74.htm/, Retrieved Thu, 13 May 2010 14:21:36 +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/May/13/t1273753293uifznz32jyo6s74.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:

FM22,steven,coomans,thesis,Arima

Dataseries X:

» Textbox « » Textfile « » CSV «

594,25 853,75 766,5 758,05 756,85 685,4 696,525 610,025 708,325 619,1 740,525 730,5 489,75 766,525 780,125 804,975 529,25 743,75 771,15 830,5 600 856,1 702,75 533,775 311,25 590 738 797,05 531,3 820 533,25 633,25 634,275 747,3 220,375 195,75 123,25 161,75 126,75 285,1 461,5 463,625 325,875 177 223 168,45 251,75 131,5 110,375 164,125

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	8 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	88.6940157741384	-187.431071878883	-91.8544744834071	269.242506031684	364.81910342716
52	149.491608238403	-150.606672129007	-46.732110184846	345.715326661651	449.589888605812
53	256.083170244488	-66.2100064247737	45.3469891805474	466.819351308428	578.376346913749
54	221.022442877749	-122.032662864246	-3.28923391813555	445.334119673633	564.077548619743
55	217.780111775789	-144.850167467048	-19.3310829927171	454.891306544295	580.410391018627
56	140.218936737977	-240.982628410732	-109.035369186947	389.473242662902	521.420501886687
57	138.143399378991	-260.765797205787	-122.68930439813	398.976103156112	537.052595963769
58	116.156523544575	-299.706988882280	-155.762011493496	388.075058582646	532.020035971429
59	238.729439584477	-193.423744849108	-43.8403394698624	521.299218638816	670.882624018061
60	176.659212499157	-271.191531221842	-116.174648874397	469.49307387271	624.509956220155
61	169.189604690315	-293.806615514896	-133.547351460675	471.926560841305	632.185824895527
62	202.7036874278	-274.974928714266	-109.633571405914	515.040946261513	680.382303569866
63	187.204247435007	-347.189636726345	-162.217131869142	536.625626739156	721.59813159636
64	187.204247435007	-373.42076233663	-179.368742999255	553.777237869269	747.829257206644
65	187.204247435007	-398.478243112422	-195.752949020446	570.161443890461	772.886737982437
66	187.204247435007	-422.506797147778	-211.464376008228	585.872870878243	796.915292017792
67	187.204247435007	-445.623640729399	-226.579667572848	600.988162442862	820.032135599414
68	187.204247435007	-467.925292572932	-241.161933910266	615.57042878028	842.333787442946
69	187.204247435007	-489.492355440613	-255.263878333883	629.672373203898	863.900850310628
70	187.204247435007	-510.392964784047	-268.930052220849	643.338547090863	884.801459654062
71	187.204247435007	-530.685332269412	-282.198518133797	656.607013003811	905.093827139427
72	187.204247435007	-550.419658631343	-295.102100568775	669.510595438789	924.828153501357
73	187.204247435007	-569.639597061974	-307.669342813905	682.07783768392	944.048091931988
74	187.204247435007	-588.383389847435	-319.925250156044	694.333745026059	962.79188471745

Actuals and Interpolation
Time	Actual	Forecast
1	594.25	593.655750472382
2	853.75	647.942799207107
3	766.5	744.673364998473
4	758.05	753.778778169047
5	756.85	755.529720168062
6	685.4	749.907388162832
7	696.525	723.33892944098
8	610.025	704.516968331494
9	708.325	672.68501267814
10	619.1	680.23484230233
11	740.525	664.582874665436
12	730.5	696.052601976007
13	489.75	655.544241875514
14	766.525	680.45740717072
15	780.125	686.76864206964
16	804.975	724.218996193073
17	529.25	753.807367605178
18	743.75	635.506534904222
19	771.15	686.076054174035
20	830.5	691.728771063574
21	600	782.989097499081
22	856.1	676.610491913217
23	702.75	794.826530239656
24	533.775	749.345042400237
25	311.25	576.525816892812
26	590	540.525418350128
27	738	581.845867357353
28	797.05	660.565830930517
29	531.3	600.288183112223
30	820	674.707766590396
31	533.25	745.636209048273
32	633.25	694.548612624309
33	634.275	554.468246486220
34	747.3	712.272349523156
35	220.375	640.90531318958
36	195.75	391.230261941304
37	123.25	245.312950038669
38	161.75	281.611155066594
39	126.75	286.879143124124
40	285.1	239.469364780464
41	461.5	194.082609246879
42	463.625	389.383530982008
43	325.875	290.573224811809
44	177	332.245829765658
45	223	316.860055686865
46	168.45	272.113450680930
47	251.75	33.9050653080551
48	131.5	146.848855028763
49	110.375	136.067930419851
50	164.125	103.076560780786

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273753293uifznz32jyo6s74/1jr0l1273753243.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273753293uifznz32jyo6s74/1jr0l1273753243.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273753293uifznz32jyo6s74/2b0ho1273753243.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273753293uifznz32jyo6s74/2b0ho1273753243.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 <- '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')

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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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