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gaelle.wauters-exponental smoothing- werkloosheid

R Software Module: rwasp_exponentialsmoothing.wasp (opens new window with default values)
Title produced by software: Exponential Smoothing
Date of computation: Mon, 26 May 2008 03:28:26 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/26/t1211794162x2y1fc7y1o0pbep.htm/, Retrieved Mon, 26 May 2008 11:29:26 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
9.8 9.7 9.5 9.3 9.1 9 9.5 10 10.2 10.1 10 9.9 10 9.9 9.7 9.5 9.2 9 9.3 9.8 9.8 9.6 9.4 9.3 9.2 9.2 9 8.8 8.7 8.7 9.1 9.7 9.8 9.6 9.4 9.4 9.5 9.4 9.3 9.2 9 8.9 9.2 9.8 9.9 9.6 9.2 9.1 9.1 9.1 8.9 8.7 8.5 8.4 8.4 8.7 8.5 8.1 7.8 7.7 7.4 7.2 7 6.6 6.4 6.4 6.8 7.3 7 7 6.7 6.7 6.3 6.2 6 6.3 6.2 6.1 6.2 6.6 6.6 7.8 7.4 7.4 7.5 7.4 7.4 7 6.9 6.9 7.6 7.7 7.6 8.2 8 8.1 8.3 8.2 8.1 7.7 7.6 7.7 8.2 8.4 8.4 8.6 8.4 8.5 8.7 8.7 8.6 7.4 7.3 7.4 9 9.2 9.2 8.5 8.3 8.3 8.6 8.6 8.5 8.1 8.1 8 8.6 8.7 8.7 8.6 8.4 8.4 8.7 8.7 8.5 8.3 8.3 8.3 8.1 8.2 8.1 8.1 7.9 7.7 8.1 8 7.7 7.8 7.6 7.4 7.3 7.4 7.1 7.3 7.1 7.1
 
Text written by user:
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Estimated Parameters of Exponential Smoothing
ParameterValue
alpha1
beta0
gamma0


Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
39.59.7-0.199999999999999
49.39.5-0.199999999999999
59.19.3-0.200000000000001
699.1-0.0999999999999996
79.590.5
8109.50.5
910.2100.199999999999999
1010.110.2-0.0999999999999996
111010.1-0.0999999999999996
129.910-0.0999999999999996
13109.90.0999999999999996
149.910-0.0999999999999996
159.79.9-0.200000000000001
169.59.7-0.199999999999999
179.29.5-0.300000000000001
1899.2-0.199999999999999
199.390.300000000000001
209.89.30.5
219.89.80
229.69.8-0.200000000000001
239.49.6-0.199999999999999
249.39.4-0.0999999999999996
259.29.3-0.100000000000001
269.29.20
2799.2-0.199999999999999
288.89-0.199999999999999
298.78.8-0.100000000000001
308.78.70
319.18.70.4
329.79.10.6
339.89.70.100000000000001
349.69.8-0.200000000000001
359.49.6-0.199999999999999
369.49.40
379.59.40.0999999999999996
389.49.5-0.0999999999999996
399.39.4-0.0999999999999996
409.29.3-0.100000000000001
4199.2-0.199999999999999
428.99-0.0999999999999996
439.28.90.299999999999999
449.89.20.600000000000001
459.99.80.0999999999999996
469.69.9-0.300000000000001
479.29.6-0.4
489.19.2-0.0999999999999996
499.19.10
509.19.10
518.99.1-0.199999999999999
528.78.9-0.200000000000001
538.58.7-0.199999999999999
548.48.5-0.0999999999999996
558.48.40
568.78.40.299999999999999
578.58.7-0.199999999999999
588.18.5-0.4
597.88.1-0.3
607.77.8-0.0999999999999996
617.47.7-0.3
627.27.4-0.2
6377.2-0.2
646.67-0.4
656.46.6-0.199999999999999
666.46.40
676.86.40.399999999999999
687.36.80.5
6977.3-0.3
70770
716.77-0.3
726.76.70
736.36.7-0.4
746.26.3-0.0999999999999996
7566.2-0.2
766.360.3
776.26.3-0.0999999999999996
786.16.2-0.100000000000001
796.26.10.100000000000001
806.66.20.399999999999999
816.66.60
827.86.61.2
837.47.8-0.399999999999999
847.47.40
857.57.40.0999999999999996
867.47.5-0.0999999999999996
877.47.40
8877.4-0.4
896.97-0.0999999999999996
906.96.90
917.66.90.700
927.77.60.100000000000001
937.67.7-0.100000000000001
948.27.60.6
9588.2-0.199999999999999
968.180.0999999999999996
978.38.10.200000000000001
988.28.3-0.100000000000001
998.18.2-0.0999999999999996
1007.78.1-0.399999999999999
1017.67.7-0.100000000000001
1027.77.60.100000000000001
1038.27.70.499999999999999
1048.48.20.200000000000001
1058.48.40
1068.68.40.199999999999999
1078.48.6-0.199999999999999
1088.58.40.0999999999999996
1098.78.50.199999999999999
1108.78.70
1118.68.7-0.0999999999999996
1127.48.6-1.2
1137.37.4-0.100000000000001
1147.47.30.100000000000001
11597.41.6
1169.290.199999999999999
1179.29.20
1188.59.2-0.700
1198.38.5-0.199999999999999
1208.38.30
1218.68.30.299999999999999
1228.68.60
1238.58.6-0.0999999999999996
1248.18.5-0.4
1258.18.10
12688.1-0.0999999999999996
1278.680.6
1288.78.60.0999999999999996
1298.78.70
1308.68.7-0.0999999999999996
1318.48.6-0.199999999999999
1328.48.40
1338.78.40.299999999999999
1348.78.70
1358.58.7-0.199999999999999
1368.38.5-0.199999999999999
1378.38.30
1388.38.30
1398.18.3-0.200000000000001
1408.28.10.0999999999999996
1418.18.2-0.0999999999999996
1428.18.10
1437.98.1-0.199999999999999
1447.77.9-0.2
1458.17.70.399999999999999
14688.1-0.0999999999999996
1477.78-0.3
1487.87.70.0999999999999996
1497.67.8-0.2
1507.47.6-0.199999999999999
1517.37.4-0.100000000000001
1527.47.30.100000000000001
1537.17.4-0.300000000000001
1547.37.10.2
1557.17.3-0.2
1567.17.10


Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
1577.16.500464648380997.699535351619
1587.16.252128974618287.94787102538172
1597.16.061574310062218.1384256899378
1607.15.900929296761998.29907070323801
1617.15.759398198865668.44060180113434
1627.15.631444305773348.56855569422666
1637.15.513778557424448.68622144257556
1647.15.404257949236558.79574205076344
1657.15.301393945142988.89860605485702
1667.15.204102751094028.99589724890598
1677.15.111566190125969.08843380987404
1687.15.023148620124419.17685137987558
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/26/t1211794162x2y1fc7y1o0pbep/161ri1211794099.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/26/t1211794162x2y1fc7y1o0pbep/161ri1211794099.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/26/t1211794162x2y1fc7y1o0pbep/2ejnh1211794099.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/26/t1211794162x2y1fc7y1o0pbep/2ejnh1211794099.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/26/t1211794162x2y1fc7y1o0pbep/3k3nx1211794099.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/26/t1211794162x2y1fc7y1o0pbep/3k3nx1211794099.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = Double ; par3 = additive ;
 
Parameters (R input):
par1 = 12 ; par2 = Double ; par3 = additive ;
 
R code (references can be found in the software module):
par1 <- as.numeric(par1)
if (par2 == 'Single') K <- 1
if (par2 == 'Double') K <- 2
if (par2 == 'Triple') K <- par1
nx <- length(x)
nxmK <- nx - K
x <- ts(x, frequency = par1)
if (par2 == 'Single') fit <- HoltWinters(x, gamma=0, beta=0)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=0)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par1, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$alpha)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,fit$beta)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'gamma',header=TRUE)
a<-table.element(a,fit$gamma)
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,'Interpolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nxmK) {
a<-table.row.start(a)
a<-table.element(a,i+K,header=TRUE)
a<-table.element(a,x[i+K])
a<-table.element(a,fit$fitted[i,'xhat'])
a<-table.element(a,myresid[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,'Extrapolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Forecast',header=TRUE)
a<-table.element(a,'95% Lower Bound',header=TRUE)
a<-table.element(a,'95% Upper Bound',header=TRUE)
a<-table.row.end(a)
for (i in 1:np) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,p[i,'fit'])
a<-table.element(a,p[i,'lwr'])
a<-table.element(a,p[i,'upr'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
 





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