US DOllar per euro- DEcompositie- Raf Deceulaer

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 19 May 2008 13:11:55 -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/19/t1211224349nae8nzxg7rvrivp.htm/, Retrieved Mon, 19 May 2008 21:12:33 +0200

User-defined keywords:

Dataseries X:

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1.0137 0.9834 0.9643 0.947 0.906 0.9492 0.9397 0.9041 0.8721 0.8552 0.8564 0.8973 0.9383 0.9217 0.9095 0.892 0.8742 0.8532 0.8607 0.9005 0.9111 0.9059 0.8883 0.8924 0.8833 0.87 0.8758 0.8858 0.917 0.9554 0.9922 0.9778 0.9808 0.9811 1.0014 1.0183 1.0622 1.0773 1.0807 1.0848 1.1582 1.1663 1.1372 1.1139 1.1222 1.1692 1.1702 1.2286 1.2613 1.2646 1.2262 1.1985 1.2007 1.2138 1.2266 1.2176 1.2218 1.249 1.2991 1.3408 1.3119 1.3014 1.3201 1.2938 1.2694 1.2165 1.2037 1.2292 1.2256 1.2015 1.1786 1.1856 1.2103 1.1938 1.202 1.2271 1.277 1.265 1.2684 1.2811 1.2727 1.2611 1.2881 1.3213 1.2999 1.3074 1.3242 1.3516 1.3511 1.3419 1.3716 1.3622 1.3896 1.4227 1.4684 1.457 1.4718

Text written by user:

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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 1.0137 NA NA 1.02027342292487 NA 2 0.9834 NA NA 1.00941665581264 NA 3 0.9643 NA NA 1.00197629714242 NA 4 0.947 NA NA 0.99205975811345 NA 5 0.906 NA NA 1.00380066383502 NA 6 0.9492 NA NA 0.995435588726848 NA 7 0.9397 0.915643866963747 0.920891666666667 0.994301392994558 1.02627236844388 8 0.9041 0.91059152863511 0.915179166666666 0.994987169508823 0.992871086067712 9 0.8721 0.903738000647339 0.910325 0.992764123414537 0.964992065593483 10 0.8552 0.898549294164093 0.90575 0.992050007357541 0.951756353885493 11 0.8564 0.896149758076072 0.902133333333333 0.993367304991212 0.955643844438 12 0.8973 0.90538865035516 0.896808333333333 1.00956761517808 0.991066101444957 13 0.9383 0.907550214248719 0.889516666666667 1.02027342292487 1.03388218664764 14 0.9217 0.894418863299188 0.886075 1.00941665581264 1.03050152207231 15 0.9095 0.889304062528756 0.88755 1.00197629714242 1.02270982257049 16 0.892 0.884210461659542 0.8912875 0.99205975811345 1.00880959757685 17 0.8742 0.898129731452553 0.894729166666667 1.00380066383502 0.973356041321724 18 0.8532 0.891765119809233 0.895854166666667 0.995435588726848 0.956754173321465 19 0.8607 0.88826743527663 0.893358333333333 0.994301392994558 0.968964937605705 20 0.9005 0.884456532316012 0.8889125 0.994987169508823 1.01813935122620 21 0.9111 0.878947853182242 0.885354166666667 0.992764123414537 1.03658026662373 22 0.9059 0.876666324418465 0.883691666666667 0.992050007357541 1.03334641102010 23 0.8883 0.87934529449997 0.885216666666667 0.993367304991212 1.01018337796999 24 0.8924 0.899785550090922 0.891258333333333 1.00956761517808 0.99179187741993 25 0.8833 0.919262102916044 0.900995833333333 1.02027342292487 0.960879380535795 26 0.87 0.91826212589003 0.909695833333333 1.00941665581264 0.947441885569166 27 0.8758 0.91763076742922 0.915820833333333 1.00197629714242 0.95441438003827 28 0.8858 0.914538555181535 0.921858333333333 0.99205975811345 0.9685758954407 29 0.917 0.933237659670182 0.929704166666667 1.00380066383502 0.982600723940008 30 0.9554 0.935373493892042 0.9396625 0.995435588726848 1.02141017063102 31 0.9922 0.94693536038578 0.9523625 0.994301392994558 1.04780119267674 32 0.9778 0.963599470090693 0.968454166666667 0.994987169508823 1.01473696317825 33 0.9808 0.978497275657634 0.985629166666667 0.992764123414537 1.00235332729038 34 0.9811 0.994488796958962 1.00245833333333 0.992050007357541 0.986537005746165 35 1.0014 1.01402934493503 1.0208 0.993367304991212 0.987545385152697 36 1.0183 1.0495843515247 1.0396375 1.00956761517808 0.97019358045949 37 1.0622 1.07584431536018 1.05446666666667 1.02027342292487 0.98731757451764 38 1.0773 1.07621900891378 1.06617916666667 1.00941665581264 1.00100443411357 39 1.0807 1.07987160444277 1.07774166666667 1.00197629714242 1.00076712412274 40 1.0848 1.08280429090455 1.09147083333333 0.99205975811345 1.00184309307990 41 1.1582 1.11054649942834 1.10634166666667 1.00380066383502 1.04290995523032 42 1.1663 1.11701560294497 1.1221375 0.995435588726848 1.04412149384941 43 1.1372 1.13270400397693 1.13919583333333 0.994301392994558 1.00396925940695 44 1.1139 1.14950453115367 1.15529583333333 0.994987169508823 0.9690261932957 45 1.1222 1.16070258444165 1.1691625 0.992764123414537 0.966828208226855 46 1.1692 1.17058180680662 1.1799625 0.992050007357541 0.998819555541879 47 1.1702 1.17860133415901 1.18647083333333 0.993367304991212 0.992871776133696 48 1.2286 1.2016084082436 1.19022083333333 1.00956761517808 1.02246288522220 49 1.2613 1.22017049331142 1.195925 1.02027342292487 1.03370799975416 50 1.2646 1.21530821227929 1.20397083333333 1.00941665581264 1.04055908387903 51 1.2262 1.21483781166785 1.21244166666667 1.00197629714242 1.00935284383069 52 1.1985 1.2102302332519 1.21991666666667 0.99205975811345 0.99030743661032 53 1.2007 1.233282043096 1.2286125 1.00380066383502 0.973581028542175 54 1.2138 1.23300458727308 1.23865833333333 0.995435588726848 0.98442456137527 55 1.2266 1.23834438406013 1.24544166666667 0.994301392994558 0.990516059820431 56 1.2176 1.24282189031398 1.24908333333333 0.994987169508823 0.97970594941194 57 1.2218 1.24545154844380 1.25452916666667 0.992764123414537 0.981009659931487 58 1.249 1.25237632991325 1.2624125 0.992050007357541 0.99730406122137 59 1.2991 1.26082731282966 1.26924583333333 0.993367304991212 1.03035521738853 60 1.3408 1.2843929526882 1.27222083333333 1.00956761517808 1.04391728185190 61 1.3119 1.29715437421037 1.27137916666667 1.02027342292487 1.01136767225459 62 1.3014 1.28287603967775 1.27090833333333 1.00941665581264 1.01443940002722 63 1.3201 1.27406296063145 1.27155 1.00197629714242 1.03613403794875 64 1.2938 1.25964720995293 1.26972916666667 0.99205975811345 1.02711298034658 65 1.2694 1.26752837574384 1.26272916666667 1.00380066383502 1.00147659357532 66 1.2165 1.24553048509790 1.25124166666667 0.995435588726848 0.976692272533487 67 1.2037 1.23347230723446 1.24054166666667 0.994301392994558 0.975863011224622 68 1.2292 1.22565007008021 1.231825 0.994987169508823 1.00289636496293 69 1.2256 1.21357554704784 1.22242083333333 0.992764123414537 1.00990828546391 70 1.2015 1.20506381164569 1.21472083333333 0.992050007357541 0.997042636571399 71 1.1786 1.20421779353647 1.21225833333333 0.993367304991212 0.978726611021717 72 1.1856 1.22621661886356 1.21459583333333 1.00956761517808 0.966876473341874 73 1.2103 1.24403213799008 1.2193125 1.02027342292487 0.97288483395246 74 1.1938 1.23569842872671 1.22417083333333 1.00941665581264 0.966093322000997 75 1.202 1.23072331087880 1.22829583333333 1.00197629714242 0.976661439151349 76 1.2271 1.22295339964970 1.23274166666667 0.99205975811345 1.00339064460795 77 1.277 1.24449951551436 1.2397875 1.00380066383502 1.02611530505274 78 1.265 1.24429863355685 1.25000416666667 0.995435588726848 1.01663697595165 79 1.2684 1.25221488849241 1.25939166666667 0.994301392994558 1.01292518692784 80 1.2811 1.26150277442151 1.26785833333333 0.994987169508823 1.01553482558727 81 1.2727 1.26843817441803 1.27768333333333 0.992764123414537 1.00335990012594 82 1.2611 1.27772320760124 1.2879625 0.992050007357541 0.98698997756138 83 1.2881 1.28763995200355 1.2962375 0.993367304991212 1.00035727999565 84 1.3213 1.31499126449156 1.30252916666667 1.00956761517808 1.00479754936690 85 1.2999 1.33659219314567 1.31003333333333 1.02027342292487 0.972547951922928 86 1.3074 1.33012094507252 1.3177125 1.00941665581264 0.982918136011098 87 1.3242 1.32858299589971 1.3259625 1.00197629714242 0.996700999551225 88 1.3516 1.32694606379395 1.33756666666667 0.99205975811345 1.01857945615028 89 1.3511 1.35695028488048 1.3518125 1.00380066383502 0.995688652012044 90 1.3419 1.35874884037072 1.36497916666667 0.995435588726848 0.987599738914133 91 1.3716 1.36994431634543 1.37779583333333 0.994301392994558 1.00120857733764 92 1.3622 NA NA 0.994987169508823 NA 93 1.3896 NA NA 0.992764123414537 NA 94 1.4227 NA NA 0.992050007357541 NA 95 1.4684 NA NA 0.993367304991212 NA 96 1.457 NA NA 1.00956761517808 NA 97 1.4718 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224349nae8nzxg7rvrivp/1jeea1211224313.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224349nae8nzxg7rvrivp/1jeea1211224313.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224349nae8nzxg7rvrivp/2b95r1211224313.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224349nae8nzxg7rvrivp/2b95r1211224313.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224349nae8nzxg7rvrivp/30ogy1211224313.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224349nae8nzxg7rvrivp/30ogy1211224313.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224349nae8nzxg7rvrivp/48ri21211224313.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211224349nae8nzxg7rvrivp/48ri21211224313.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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