| Wisselkoers Deomposition | | *Unverified author* | | R Software Module: /rwasp_decompose.wasp (opens new window with default values) | | Title produced by software: Classical Decomposition | | Date of computation: Mon, 16 May 2011 11:35:05 +0000 | | | | Cite this page as follows: | | Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/16/t13055455525cxc1omlfxf2sce.htm/, Retrieved Mon, 16 May 2011 13:32:38 +0200 | | | | Original text written by user: | | | | | IsPrivate? | | No (this computation is public) | | | | User-defined keywords: | | KDGP2W92 | | | | Dataseries X: | | » Textbox « » Textfile « » CSV « | | 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
1,4748
1,5527
1,575
1,5557
1,5553
1,577
1,4975
1,4369
1,3322
1,2732
1,3449
1,3239
1,2785
1,305
1,319
1,365
1,4016
1,4088
1,4268
1,4562
1,4816
1,4914
1,4614
1,4272 | | | | Output produced by software: |
| Classical Decomposition by Moving Averages | | t | Observations | Fit | Trend | Seasonal | Random | | 1 | 0.8833 | NA | NA | 1.0062489895994 | NA | | 2 | 0.87 | NA | NA | 0.997616024114758 | NA | | 3 | 0.8758 | NA | NA | 1.00409575856586 | NA | | 4 | 0.8858 | NA | NA | 1.00310790960093 | NA | | 5 | 0.917 | NA | NA | 1.01393716552327 | NA | | 6 | 0.9554 | NA | NA | 1.00720455910134 | NA | | 7 | 0.9922 | 0.957947877980286 | 0.9523625 | 1.00586476050904 | 1.03575572617994 | | 8 | 0.9778 | 0.96852504488531 | 0.968454166666667 | 1.00007318696236 | 1.00957637096084 | | 9 | 0.9808 | 0.97612571662463 | 0.985629166666667 | 0.990357986184423 | 1.00478860795875 | | 10 | 0.9811 | 0.985976550207264 | 1.00245833333333 | 0.983558635228992 | 0.995054091087421 | | 11 | 1.0014 | 1.0040764347946 | 1.0208 | 0.983617197095027 | 0.997334431222708 | | 12 | 1.0183 | 1.04412647540272 | 1.0396375 | 1.00431782751461 | 0.975264993263623 | | 13 | 1.0622 | 1.06105601789958 | 1.05446666666667 | 1.0062489895994 | 1.00107815429263 | | 14 | 1.0773 | 1.06363742124399 | 1.06617916666667 | 0.997616024114758 | 1.01284514674186 | | 15 | 1.0807 | 1.0821558363297 | 1.07774166666667 | 1.00409575856586 | 0.99865468883425 | | 16 | 1.0848 | 1.09486302601538 | 1.09147083333333 | 1.00310790960093 | 0.990808872181932 | | 17 | 1.1582 | 1.12176093360029 | 1.10634166666667 | 1.01393716552327 | 1.03248380765299 | | 18 | 1.1663 | 1.13022200593858 | 1.1221375 | 1.00720455910134 | 1.03192115696903 | | 19 | 1.1372 | 1.14587694406872 | 1.13919583333333 | 1.00586476050904 | 0.992427682471807 | | 20 | 1.1139 | 1.155380385926 | 1.15529583333333 | 1.00007318696236 | 0.96409806983805 | | 21 | 1.1222 | 1.15788941902234 | 1.1691625 | 0.990357986184423 | 0.96917717837643 | | 22 | 1.1692 | 1.16056230612139 | 1.1799625 | 0.983558635228992 | 1.00744268001214 | | 23 | 1.1702 | 1.16703311551833 | 1.18647083333333 | 0.983617197095027 | 1.00271362006746 | | 24 | 1.2286 | 1.19536000159596 | 1.19022083333333 | 1.00431782751461 | 1.02780752104777 | | 25 | 1.2613 | 1.20339832288667 | 1.195925 | 1.0062489895994 | 1.04811513861382 | | 26 | 1.2646 | 1.20110059590013 | 1.20397083333333 | 0.997616024114758 | 1.05286768178837 | | 27 | 1.2262 | 1.21740753500852 | 1.21244166666667 | 1.00409575856586 | 1.00722228566741 | | 28 | 1.1985 | 1.22370805738733 | 1.21991666666667 | 1.00310790960093 | 0.979400268523888 | | 29 | 1.2007 | 1.24573587577646 | 1.2286125 | 1.01393716552327 | 0.96384797399498 | | 30 | 1.2138 | 1.2475823205022 | 1.23865833333333 | 1.00720455910134 | 0.972921770413832 | | 31 | 1.2266 | 1.25274588376964 | 1.24544166666667 | 1.00586476050904 | 0.979129140148547 | | 32 | 1.2176 | 1.24917474994823 | 1.24908333333333 | 1.00007318696236 | 0.97472351250332 | | 33 | 1.2218 | 1.24243297910962 | 1.25452916666667 | 0.990357986184423 | 0.983393084812987 | | 34 | 1.249 | 1.24165671559602 | 1.2624125 | 0.983558635228992 | 1.00591410195084 | | 35 | 1.2991 | 1.24845202900787 | 1.26924583333333 | 0.983617197095027 | 1.04056861602634 | | 36 | 1.3408 | 1.27771406345215 | 1.27222083333333 | 1.00431782751461 | 1.0493740644737 | | 37 | 1.3119 | 1.27932400185606 | 1.27137916666667 | 1.0062489895994 | 1.02546344639565 | | 38 | 1.3014 | 1.26787851851431 | 1.27090833333333 | 0.997616024114758 | 1.02643903260146 | | 39 | 1.3201 | 1.27675796180441 | 1.27155 | 1.00409575856586 | 1.0339469496116 | | 40 | 1.2938 | 1.27367537013433 | 1.26972916666667 | 1.00310790960093 | 1.01580043890112 | | 41 | 1.2694 | 1.28032803207356 | 1.26272916666667 | 1.01393716552327 | 0.991464662336682 | | 42 | 1.2165 | 1.26025631120423 | 1.25124166666667 | 1.00720455910134 | 0.965279831717393 | | 43 | 1.2037 | 1.24781714644315 | 1.24054166666667 | 1.00586476050904 | 0.96464454221606 | | 44 | 1.2292 | 1.23191515352991 | 1.231825 | 1.00007318696236 | 0.997795989827604 | | 45 | 1.2256 | 1.21063423476988 | 1.22242083333333 | 0.990357986184423 | 1.01236192137996 | | 46 | 1.2015 | 1.19474916501756 | 1.21472083333333 | 0.983558635228992 | 1.00565042033935 | | 47 | 1.1786 | 1.19239814398842 | 1.21225833333333 | 0.983617197095027 | 0.988428240971368 | | 48 | 1.1856 | 1.21984024864162 | 1.21459583333333 | 1.00431782751461 | 0.971930546905831 | | 49 | 1.2103 | 1.22693197113092 | 1.2193125 | 1.0062489895994 | 0.98644425972893 | | 50 | 1.1938 | 1.22125243958725 | 1.22417083333333 | 0.997616024114758 | 0.97752107697199 | | 51 | 1.202 | 1.23332663651411 | 1.22829583333333 | 1.00409575856586 | 0.974599886529124 | | 52 | 1.2271 | 1.23657291632797 | 1.23274166666667 | 1.00310790960093 | 0.992339379099378 | | 53 | 1.277 | 1.25706662360118 | 1.2397875 | 1.01393716552327 | 1.01585705643963 | | 54 | 1.265 | 1.25900989556234 | 1.25000416666667 | 1.00720455910134 | 1.00475778979877 | | 55 | 1.2684 | 1.26677769717874 | 1.25939166666667 | 1.00586476050904 | 1.0012806531287 | | 56 | 1.2811 | 1.26795112403345 | 1.26785833333333 | 1.00007318696236 | 1.01037017572469 | | 57 | 1.2727 | 1.2653638929814 | 1.27768333333333 | 0.990357986184423 | 1.00579762632654 | | 58 | 1.2611 | 1.26678663872612 | 1.2879625 | 0.983558635228992 | 0.995510973551284 | | 59 | 1.2881 | 1.27500149651946 | 1.2962375 | 0.983617197095027 | 1.01027332400495 | | 60 | 1.3213 | 1.30815326294108 | 1.30252916666667 | 1.00431782751461 | 1.01004984464081 | | 61 | 1.2999 | 1.3182197180082 | 1.31003333333333 | 1.0062489895994 | 0.986102682460337 | | 62 | 1.3074 | 1.31457110517632 | 1.3177125 | 0.997616024114758 | 0.994544908869455 | | 63 | 1.3242 | 1.33139332226738 | 1.3259625 | 1.00409575856586 | 0.994597147103662 | | 64 | 1.3516 | 1.34172370295188 | 1.33756666666667 | 1.00310790960093 | 1.00736090226802 | | 65 | 1.3511 | 1.37065293456892 | 1.3518125 | 1.01393716552327 | 0.985734583806167 | | 66 | 1.3419 | 1.37481323974502 | 1.36497916666667 | 1.00720455910134 | 0.976059846680614 | | 67 | 1.3716 | 1.38587627592618 | 1.37779583333333 | 1.00586476050904 | 0.98969873705599 | | 68 | 1.3622 | 1.39203520470581 | 1.39193333333333 | 1.00007318696236 | 0.97856720533723 | | 69 | 1.3896 | 1.3948490731834 | 1.40842916666667 | 0.990357986184423 | 0.99623681638084 | | 70 | 1.4227 | 1.40379225845254 | 1.42725833333333 | 0.983558635228992 | 1.01346904531893 | | 71 | 1.4684 | 1.42141701471205 | 1.44509166666667 | 0.983617197095027 | 1.03305362522164 | | 72 | 1.457 | 1.46882319205534 | 1.46250833333333 | 1.00431782751461 | 0.991950568237696 | | 73 | 1.4718 | 1.48920657756588 | 1.47995833333333 | 1.0062489895994 | 0.988311509075971 | | 74 | 1.4748 | 1.4905921391645 | 1.49415416666667 | 0.997616024114758 | 0.989405459246987 | | 75 | 1.5527 | 1.50791335662326 | 1.5017625 | 1.00409575856586 | 1.0297010721339 | | 76 | 1.575 | 1.50462424785478 | 1.4999625 | 1.00310790960093 | 1.04677297487765 | | 77 | 1.5557 | 1.50879764863328 | 1.48805833333333 | 1.01393716552327 | 1.03108591228864 | | 78 | 1.5553 | 1.48588272249992 | 1.47525416666667 | 1.00720455910134 | 1.04671787110041 | | 79 | 1.577 | 1.47300931080528 | 1.46442083333333 | 1.00586476050904 | 1.0705974418708 | | 80 | 1.4975 | 1.45018529355606 | 1.45007916666667 | 1.00007318696236 | 1.03262666271282 | | 81 | 1.4369 | 1.41777586056357 | 1.43157916666667 | 0.990357986184423 | 1.01348883132262 | | 82 | 1.3322 | 1.38739961453206 | 1.41059166666667 | 0.983558635228992 | 0.960213615490535 | | 83 | 1.2732 | 1.36917464633134 | 1.39197916666667 | 0.983617197095027 | 0.929903284005076 | | 84 | 1.3449 | 1.38357753178742 | 1.37762916666667 | 1.00431782751461 | 0.972045273286958 | | 85 | 1.3239 | 1.372741642428 | 1.36421666666667 | 1.0062489895994 | 0.964420368029623 | | 86 | 1.2785 | 1.35103397085771 | 1.3542625 | 0.997616024114758 | 0.946312252376849 | | 87 | 1.305 | 1.35765879381853 | 1.35212083333333 | 1.00409575856586 | 0.961213528717018 | | 88 | 1.319 | 1.3633741153341 | 1.35915 | 1.00310790960093 | 0.967452722744975 | | 89 | 1.365 | 1.39362283610622 | 1.37446666666667 | 1.01393716552327 | 0.97946156207788 | | 90 | 1.4016 | 1.39841539991329 | 1.3884125 | 1.00720455910134 | 1.00227729191691 | | 91 | 1.4088 | 1.40576725156525 | 1.39757083333333 | 1.00586476050904 | 1.00215736170506 | | 92 | 1.4268 | NA | NA | 1.00007318696236 | NA | | 93 | 1.4562 | NA | NA | 0.990357986184423 | NA | | 94 | 1.4816 | NA | NA | 0.983558635228992 | NA | | 95 | 1.4914 | NA | NA | 0.983617197095027 | NA | | 96 | 1.4614 | NA | NA | 1.00431782751461 | NA | | 97 | 1.4272 | NA | NA | NA | NA |
| | | | Charts produced by software: |  | | http://www.freestatistics.org/blog/date/2011/May/16/t13055455525cxc1omlfxf2sce/1g5um1305545702.png (open in new window) | | http://www.freestatistics.org/blog/date/2011/May/16/t13055455525cxc1omlfxf2sce/1g5um1305545702.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2011/May/16/t13055455525cxc1omlfxf2sce/2r35y1305545702.png (open in new window) | | http://www.freestatistics.org/blog/date/2011/May/16/t13055455525cxc1omlfxf2sce/2r35y1305545702.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2011/May/16/t13055455525cxc1omlfxf2sce/39f0i1305545702.png (open in new window) | | http://www.freestatistics.org/blog/date/2011/May/16/t13055455525cxc1omlfxf2sce/39f0i1305545702.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2011/May/16/t13055455525cxc1omlfxf2sce/4wze51305545702.png (open in new window) | | http://www.freestatistics.org/blog/date/2011/May/16/t13055455525cxc1omlfxf2sce/4wze51305545702.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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