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workshop Forecast 1

*The author of this computation has been verified*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Fri, 03 Dec 2010 09:12:58 +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/Dec/03/t12913674750gvy2a0bt17qtok.htm/, Retrieved Fri, 03 Dec 2010 10:11:15 +0100
 
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/Dec/03/t12913674750gvy2a0bt17qtok.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
58608 46865 51378 46235 47206 45382 41227 33795 31295 42625 33625 21538 56421 53152 53536 52408 41454 38271 35306 26414 31917 38030 27534 18387 50556 43901 48572 43899 37532 40357 35489 29027 34485 42598 30306 26451 47460 50104 61465 53726 39477 43895 31481 29896 33842 39120 33702 25094 51442 45594 52518 48564 41745 49585 32747 33379 35645 37034 35681 20972
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
158608NANA1.28959004955486NA
246865NANA1.20769812338532NA
351378NANA1.35205990824657NA
446235NANA1.24280523930081NA
547206NANA1.00455825076067NA
645382NANA1.07775085933368NA
74122737171.852096957541557.1250.8944760278040761.10909189815093
83379531004.782809353941727.95833333330.7430218023532311.08999312163555
93129534634.938472688742079.83333333330.8230768928747830.903567362323383
104262542995.482342215142426.95833333331.013400065223040.991383226282558
113362533161.534736384242444.50.781291680580151.01397598957045
122153823899.230180070941908.54166666670.5702711005828180.901200575822737
135642153344.590927780341365.54166666671.289590049554861.05767049702161
145315249287.720358764140811.29166666671.207698123385321.07840248266927
155353654798.537394597440529.66666666671.352059908246570.976960381524309
165240850164.746029792940364.1251.242805239300811.04471773800818
174145440100.835242333839918.8751.004558250760671.03374405419460
183827142607.577941468539533.79166666671.077750859333680.898220500883063
193530635026.004106255539158.1250.8944760278040761.00799394338261
202641428627.360715757738528.29166666670.7430218023532310.92268373121315
213191731224.2450080978379360.8230768928747831.02218644491556
223803037875.447412686637374.6251.013400065223041.0040805481617
232753428795.807040582436856.66666666670.781291680580150.95618087595864
241838720974.666124619536780.16666666670.5702711005828180.876628971863245
255055647553.256946904336874.70833333331.289590049554861.06314484529311
264390144674.212885921936991.20833333331.207698123385320.982692187819932
274857250306.205677789237207.08333333331.352059908246570.965527002992498
284389946610.68553025437504.41666666671.242805239300810.941822663635918
293753237982.598600823637810.251.004558250760670.9881367095085
304035741236.633942110338261.751.077750859333680.978668628886025
313548934409.374694588138468.750.8944760278040761.03137590598476
322902728679.310323438938598.20833333330.7430218023532311.01212336254394
333448532424.188233297639393.8750.8230768928747831.06355785230071
344259840881.107556132740340.54166666671.013400065223041.04199720962819
353030631900.953163588140831.04166666670.781291680580150.950002962124386
362645123415.046254380241059.50.5702711005828181.12965824250942
374746052924.66816789441039.91666666671.289590049554860.896746293230251
385010449405.873491835340909.1251.207698123385321.01413043548921
396146555324.319691416840918.54166666671.352059908246571.11099423079821
405372650640.377951583640746.83333333331.242805239300811.06093205013925
413947740929.135376679840743.41666666671.004558250760670.964520741439674
424389544002.816241447640828.3751.077750859333680.997549787703224
433148136617.836007236340937.750.8944760278040760.85971765217854
442989630401.294309634240915.750.7430218023532310.9833791843042
453384233215.302306832440355.04166666670.8230768928747831.01886774015718
463912040300.049293735539767.16666666671.013400065223040.970718415624398
473370230975.545721760939646.58333333330.781291680580151.08801957204336
482509422798.393104283339978.16666666670.5702711005828181.10069160950143
495144251929.2121154751402681.289590049554860.990617764151867
504559448870.561298644840465.8751.207698123385320.932954293718423
515251855010.078434408540686.1251.352059908246570.954697784381821
524856450550.274571734340674.33333333331.242805239300810.960706947913495
534174540855.258488655140669.8751.004558250760671.02177789455406
544958543735.758559761940580.58333333331.077750859333681.13374048222453
5532747NANA0.894476027804076NA
5633379NANA0.743021802353231NA
5735645NANA0.823076892874783NA
5837034NANA1.01340006522304NA
5935681NANA0.78129168058015NA
6020972NANA0.570271100582818NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/03/t12913674750gvy2a0bt17qtok/1fwx01291367575.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t12913674750gvy2a0bt17qtok/1fwx01291367575.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t12913674750gvy2a0bt17qtok/28nfm1291367575.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t12913674750gvy2a0bt17qtok/28nfm1291367575.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t12913674750gvy2a0bt17qtok/38nfm1291367575.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t12913674750gvy2a0bt17qtok/38nfm1291367575.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t12913674750gvy2a0bt17qtok/4jweo1291367575.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t12913674750gvy2a0bt17qtok/4jweo1291367575.ps (open in new window)


 
Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
 
Parameters (R input):
par1 = multiplicative ; par2 = 12 ;
 
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
 





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Software written by Ed van Stee & Patrick Wessa


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