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FM50,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 12:18:19 +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/t1273753140bhn7ary7lzfd4l0.htm/, Retrieved Thu, 13 May 2010 14:19:02 +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/t1273753140bhn7ary7lzfd4l0.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,ETS

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	11 seconds
R Server	wessa.org @ wessa.org

Demand Forecast
Point	Forecast	95% LB	80% LB	80% UB	95% UB
51	1749.22155831441	1251.45783486464	1423.75135371366	2074.69176291516	2246.98528176417
52	1801.44383704242	1219.64604556019	1421.02670877594	2181.86096530890	2383.24162852466
53	2009.91850569201	1291.47041185983	1540.15054736433	2479.68646401969	2728.36659952419
54	2186.63301768539	1335.87231036930	1630.35049159570	2742.91554377508	3037.39372500148
55	2213.61701065110	1287.26358920356	1607.90706553581	2819.32695576638	3139.97043209863
56	2172.71086764939	1203.44465201129	1538.94175460520	2806.47998069358	3141.97708328750
57	2183.43720884076	1152.25431692174	1509.18295727004	2857.69146041148	3214.62010075977
58	2259.21870339407	1135.91444104145	1524.72952534994	2993.7078814382	3382.52296574669
59	1688.49893365773	808.640335404342	1113.19031934453	2263.80754797092	2568.35753191111
60	935.568157727725	426.567815813190	602.750723229617	1268.38559222583	1444.56849964226
61	1323.77986581384	574.231971210276	833.676840780306	1813.88289084737	2073.3277604174
62	1593.38738693285	657.001167384723	981.117347747462	2205.65742611825	2529.77360648099
63	1749.22467663797	684.822181357903	1053.24929394957	2445.20005932637	2813.62717191803
64	1801.44704846223	668.795725314928	1060.84615587173	2542.04794105272	2934.09837160953
65	2009.92208875797	706.53995796828	1157.68632147250	2862.15785604345	3313.30421954766
66	2186.63691577893	726.549210210879	1231.93688064422	3141.33695091364	3646.72462134698
67	2213.62095684879	693.837325322864	1219.88785377436	3207.35405992322	3733.40458837472
68	2172.71474092403	640.966619255074	1171.15847824326	3174.27100360480	3704.46286259298
69	2183.44110123715	604.679659275013	1151.14448072408	3215.73772175022	3762.20254319929
70	2259.22273088554	585.60743075826	1164.90450620610	3353.54095556499	3932.83803101283
71	1688.50194373154	408.261046800651	851.397415955726	2525.60647150735	2968.74284066243
72	935.569825557746	210.186488806952	461.267155628278	1409.87249548721	1660.95316230854
73	1323.78222570583	275.089464321736	638.078885885295	2009.48556552636	2372.47498708992
74	1593.39022745206	304.662570097208	750.736505306593	2436.04394959753	2882.11788480691

Actuals and Interpolation
Time	Actual	Forecast
1	1216.67	1216.57896906222
2	1186.17	1186.32706637457
3	1217.475	1217.61518741362
4	1096.95	1097.13722496335
5	1685.6	1685.35698835458
6	1758.5	1758.44429928912
7	1786.6	1786.57095582242
8	2049.895	2049.71036034501
9	1845.895	1845.92826649821
10	2015.02	2014.98176910371
11	1609.63	1609.54241047328
12	918.725	918.654492873966
13	1240.96	1240.97493327842
14	1671.785	1671.67364264948
15	2451.83	2451.42825634976
16	1886.14	1886.27704742758
17	2110.66	2110.72659138529
18	1856.87	1857.08748894488
19	1775.765	1775.92924079464
20	1569.625	1569.80044248696
21	1835.69	1835.63172097274
22	2041.46	2041.35676313207
23	1667.035	1666.89228111939
24	948.25	948.159567965853
25	1365.66	1365.60094114980
26	1681.025	1680.97488911353
27	1661.9	1661.97891553469
28	2194.88	2194.64696222739
29	2051.025	2051.10610598749
30	2365.845	2365.8268033643
31	2398.5	2398.49010356152
32	2181.85	2181.91895239553
33	2626.77	2626.61215634528
34	2529.72	2529.72574330652
35	1700.3	1700.40313375847
36	605.38	605.770298247161
37	1200.495	1200.41697572286
38	1597.02	1596.87595983126
39	1174.955	1175.24282736234
40	1612.88	1612.75933399255
41	1683.55	1683.56473508225
42	2260.955	2260.72911247337
43	2455.335	2455.16084014401
44	2365.62	2365.56505712385
45	2417.755	2417.71252994142
46	2308.785	2308.84414672757
47	1629.94	1630.02245147924
48	1053.275	1053.15585122134
49	1330.235	1330.29555655607
50	1543.85	1543.91407603040

What is next?

Simulate Time Series

Generate Forecasts

Forecast Analysis

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/13/t1273753140bhn7ary7lzfd4l0/1qzs01273753087.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273753140bhn7ary7lzfd4l0/1qzs01273753087.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273753140bhn7ary7lzfd4l0/2qzs01273753087.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/13/t1273753140bhn7ary7lzfd4l0/2qzs01273753087.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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