Classical Decomposition aantal stuks product Y

*Unverified author*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Thu, 19 Aug 2010 15:00:39 +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/Aug/19/t1282230022mziwvhqhi3374tu.htm/, Retrieved Thu, 19 Aug 2010 17:00:28 +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/Aug/19/t1282230022mziwvhqhi3374tu.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:

Sebastien Delforge

Dataseries X:

» Textbox « » Textfile « » CSV «

152 151 150 148 146 145 146 148 149 149 150 152 154 164 167 162 164 172 169 174 181 181 172 181 183 200 199 190 197 194 190 195 204 197 185 193 192 211 210 197 191 182 170 166 175 163 153 171 165 184 179 163 163 148 132 127 130 118 113 137 133 155 151 132 134 118 102 98 91 77 74 102 98 113 114 96 102 90 72 67 62 48 43 75

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 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 152 NA NA 0.992338069629766 NA 2 151 NA NA 1.11800768650341 NA 3 150 NA NA 1.12300300022508 NA 4 148 NA NA 1.03591090228458 NA 5 146 NA NA 1.06679100693656 NA 6 145 NA NA 1.01386187855496 NA 7 146 140.489950804234 148.916666666667 0.943413211891888 1.03922023720717 8 148 141.196427263606 149.541666666667 0.944194554005724 1.04818516210536 9 149 145.50526211366 150.791666666667 0.96494232957387 1.02401794846162 10 149 139.276136444749 152.083333333333 0.915788294431227 1.06981715463588 11 150 135.394074634131 153.416666666667 0.882525201308842 1.10787713868083 12 152 155.171139315239 155.291666666667 0.999223864654075 0.979563601007036 13 154 156.169203707985 157.375 0.992338069629766 0.986109913757128 14 164 178.229058690086 159.416666666667 1.11800768650341 0.92016420445317 15 167 181.739318869759 161.833333333333 1.12300300022508 0.918898568777392 16 162 170.407343425814 164.5 1.03591090228458 0.950663256308116 17 164 177.887400406672 166.75 1.06679100693656 0.921931511872545 18 172 171.215924740969 168.875 1.01386187855496 1.00457945287634 19 169 161.598821420315 171.291666666667 0.943413211891888 1.04579970642506 20 174 164.289852396996 174 0.944194554005724 1.05910375754395 21 181 170.633968612979 176.833333333333 0.96494232957387 1.06075010428042 22 181 164.231367468 179.333333333333 0.915788294431227 1.10210371374559 23 172 160.509270988046 181.875 0.882525201308842 1.07158919195895 24 181 184.023728407125 184.166666666667 0.999223864654075 0.983568812384695 25 183 184.533533531569 185.958333333333 0.992338069629766 0.991689675571588 26 200 209.859359487411 187.708333333333 1.11800768650341 0.953019205283514 27 199 212.855860334329 189.541666666667 1.12300300022508 0.93490496191852 28 190 198.031634153403 191.166666666667 1.03591090228458 0.959442670926093 29 197 205.223919959422 192.375 1.06679100693656 0.959927088611075 30 194 196.097785010505 193.416666666667 1.01386187855496 0.98930235234226 31 190 183.297325293828 194.291666666667 0.943413211891888 1.03656722592884 32 195 184.235962350367 195.125 0.944194554005724 1.05842527980049 33 204 189.168902526877 196.041666666667 0.96494232957387 1.07840135072420 34 197 180.219504774945 196.791666666667 0.915788294431227 1.09311142679040 35 185 173.710377124290 196.833333333333 0.882525201308842 1.06499106767601 36 193 195.931146127587 196.083333333333 0.999223864654075 0.985039917412223 37 192 193.257839060397 194.75 0.992338069629766 0.993491394364583 38 211 215.449397919928 192.708333333333 1.11800768650341 0.979348292625157 39 210 213.698112584498 190.291666666667 1.12300300022508 0.982694687661146 40 197 194.405945995407 187.666666666667 1.03591090228458 1.01334349107128 41 191 197.267437032686 184.916666666667 1.06679100693656 0.96822872985546 42 182 185.198769816040 182.666666666667 1.01386187855496 0.982727910022205 43 170 170.404011397972 180.625 0.943413211891888 0.997629096905303 44 166 168.420703570771 178.375 0.944194554005724 0.985627042759895 45 175 169.789644074602 175.958333333333 0.96494232957387 1.03068712437555 46 163 158.66032201021 173.25 0.915788294431227 1.02735200543404 47 153 150.617634356709 170.666666666667 0.882525201308842 1.01581730886603 48 171 167.952877917272 168.083333333333 0.999223864654075 1.01814272027079 49 165 163.818476328047 165.083333333333 0.992338069629766 1.00721239568598 50 184 180.97749425274 161.875 1.11800768650341 1.01670100340233 51 179 177.855600160648 158.375 1.12300300022508 1.00643443241775 52 163 160.177723265754 154.625 1.03591090228458 1.01761965819406 53 163 161.174341297999 151.083333333333 1.06679100693656 1.01132722918113 54 148 150.051558026134 148 1.01386187855496 0.986327645956352 55 132 137.030769027297 145.25 0.943413211891888 0.96328730355228 56 127 134.744431144567 142.708333333333 0.944194554005724 0.942525037370503 57 130 135.413573583533 140.333333333333 0.96494232957387 0.960021928080987 58 118 126.264311094705 137.875 0.915788294431227 0.934547529519195 59 113 119.471849127184 135.375 0.882525201308842 0.9458295056579 60 137 132.813505343604 132.916666666667 0.999223864654075 1.03152160351137 61 133 129.417423247549 130.416666666667 0.992338069629766 1.02768233721976 62 155 143.058400218832 127.958333333333 1.11800768650341 1.08347360073160 63 151 140.515750403164 125.125 1.12300300022508 1.07461262930850 64 132 126.16531530741 121.791666666667 1.03591090228458 1.04624634495125 65 134 126.370284696694 118.458333333333 1.06679100693656 1.06037586543085 66 118 116.974314238279 115.375 1.01386187855496 1.00876846997053 67 102 106.094677454009 112.458333333333 0.943413211891888 0.96140543944079 68 98 103.153255025125 109.25 0.944194554005724 0.950042729879245 69 91 102.243681004431 105.958333333333 0.96494232957387 0.890030553536663 70 77 94.2498786352137 102.916666666667 0.915788294431227 0.816977179334332 71 74 88.3260638976599 100.083333333333 0.882525201308842 0.837804796619727 72 102 97.5075954591602 97.5833333333333 0.999223864654075 1.04607235487333 73 98 94.4375062930994 95.1666666666667 0.992338069629766 1.0377232928604 74 113 103.555461962379 92.625 1.11800768650341 1.09120270296368 75 114 101.210645395286 90.125 1.12300300022508 1.12636372937614 76 96 90.8580187212104 87.7083333333333 1.03591090228458 1.05659358800864 77 102 90.8994837160531 85.2083333333333 1.06679100693656 1.12211858450838 78 90 83.9393146953629 82.7916666666667 1.01386187855496 1.07220317829176 79 72 NA NA 0.943413211891888 NA 80 67 NA NA 0.944194554005724 NA 81 62 NA NA 0.96494232957387 NA 82 48 NA NA 0.915788294431227 NA 83 43 NA NA 0.882525201308842 NA 84 75 NA NA 0.999223864654075 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/19/t1282230022mziwvhqhi3374tu/1rqnh1282230037.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t1282230022mziwvhqhi3374tu/1rqnh1282230037.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t1282230022mziwvhqhi3374tu/2j0421282230037.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t1282230022mziwvhqhi3374tu/2j0421282230037.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t1282230022mziwvhqhi3374tu/3j0421282230037.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t1282230022mziwvhqhi3374tu/3j0421282230037.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t1282230022mziwvhqhi3374tu/4u9ln1282230037.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t1282230022mziwvhqhi3374tu/4u9ln1282230037.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')

Copyright

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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