Home » date » 2010 » Apr » 26 »

B382,steven,coomans,thesis,croston,permaand

*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: Mon, 26 Apr 2010 10:45:09 +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/Apr/26/t1272279143ujygsjwhsmxhdt5.htm/, Retrieved Mon, 26 Apr 2010 12:52:26 +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/Apr/26/t1272279143ujygsjwhsmxhdt5.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:
B382,steven,coomans,thesis,croston,permaand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
203,5 168,85 295,75 312,2 335,25 261,5 305,75 230 230 247,25 276,25 356,3 320,5 188,5 372,75 296 329,5 376,53 281,5 390 0 203,25 337 214,775 270 280 309,25 347 214,575 213,62 231,75 224,3 278 226,525 360,302 263,25 263,75 269,775 283,25 286,75 230,25 200,5 297,95 329,5 289,75 223,775 281,78 265,8 256,75 89,275 225,5 124,25 230 286,525 227 218,3 334,525 128,95 195,5 106,056 173,525 114,75 131,05 141,25 160,25 145,5 297,5 179,25 137 158,6 55,6 15,25 67,75 93 126,75 160 150,525 239,25 165,05 215,81 166 79,05 204,25 102 87,025 72,175 176,75 188,975
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Serverwessa.org @ wessa.org


Demand Forecast
PointForecast95% LB80% LB80% UB95% UB
89146.88490073040014.464132796470860.2996145746596233.470186886139279.305668664328
90146.88490073040013.803675991469859.8677650819649233.902036378834279.966125469329
91146.88490073040013.146480761358359.4380482184607234.331753242338280.623320699441
92146.88490073040012.492499257631259.0104326976911234.759368763108281.277302203168
93146.88490073040011.841684790390758.584887990773235.184913470026281.928116670408
94146.88490073040011.193991789446658.1613843009606235.608417159838282.575809671352
95146.88490073040010.549375767079457.7398925392976236.029908921501283.220425693720
96146.8849007304009.9077932823803157.3203843013005236.449417159499283.862008178419
97146.8849007304009.2692019070882756.9028318446206236.866969616178284.500599553711
98146.8849007304008.6335601928465756.4872080676342237.282593393165285.136241267952


Actuals and Interpolation
TimeActualForecast
1203.5NA
2168.85203.5
3295.75200.035
4312.2209.6065
5335.25219.86585
6261.5231.404265
7305.75234.4138385
8230241.54745465
9230240.392709185
10247.25239.3534382665
11276.25240.14309443985
12356.3243.753784995865
13320.5255.008406496279
14188.5261.557565846651
15372.75254.251809261986
16296266.101628335787
17329.5269.091465502208
18376.53275.132318951987
19281.5285.272087056789
20390284.89487835111
210295.405390515999
22203.25295.405390515999
23337260.172592240363
24214.775267.220978273357
25270262.369361411807
26280263.080577609793
27309.25264.668346342275
28347268.877939444929
29214.575276.295923418354
30213.62270.405565089382
31231.75264.961363602901
32224.3261.764095942650
33278258.143914277958
34226.525260.069108224177
35360.302256.806833670759
36263.25266.899800625593
37263.75266.542983396723
38269.775266.269319552912
39283.25266.613509684986
40286.75268.249868857245
41230.25270.072524882384
42200.5266.14334972426
43297.95259.657863128208
44329.5263.445631437441
45289.75269.986654204779
46223.775271.945626533771
47281.78267.166683806695
48265.8268.617599332661
49256.75268.337648186674
5089.275267.185582599100
51225.5249.487148674218
52124.25247.099679095772
53230234.866568757848
54286.525234.381761520692
55227239.57824229666
56218.3238.324293165353
57334.525236.327417726316
58128.95246.122656946707
59195.5234.431729041526
60106.056230.546433790912
61173.525218.120066052183
62114.75213.667871376779
63131.05203.790683594741
64141.25196.526278923624
65160.25191.005261078259
66145.5187.933045363869
67297.5183.693851885952
68179.25195.064542400102
69137193.484329443792
70158.6187.839886922996
7155.6184.917757489737
7215.25171.993382776537
7367.75156.327118539305
7493147.473513339562
75126.75142.028435085060
76160140.501165387377
77150.525142.450389739493
78239.25143.257605109507
79165.05152.854216156414
80215.81154.073493984556
81166160.245775246224
8279.05160.821082851191
83204.25152.645443736842
84102157.805064892845
8587.025152.225370571782
8672.175145.706187540951
87176.75138.353935630926
88188.975142.193134684045


What is next?
Simulate Time Series
Generate Forecasts
Forecast Analysis
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Apr/26/t1272279143ujygsjwhsmxhdt5/1bmib1272278703.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Apr/26/t1272279143ujygsjwhsmxhdt5/1bmib1272278703.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Apr/26/t1272279143ujygsjwhsmxhdt5/2bmib1272278703.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Apr/26/t1272279143ujygsjwhsmxhdt5/2bmib1272278703.ps (open in new window)


 
Parameters (Session):
par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B382crostonm ; par9 = 3 ; par10 = 0.1 ;
 
Parameters (R input):
par1 = Input box ; par2 = Croston ; par3 = NA ; par4 = NA ; par5 = ZZZ ; par6 = 12 ; par7 = dum ; par8 = B382crostonm ; par9 = 3 ; par10 = 0.1 ;
 
R code (references can be found in the software module):
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#output',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
 




Copyright

Creative Commons License

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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