Decompositie Wisselkoers (multiplicatief)

*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 May 2011 13:00:11 +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/19/t1305809803qwpw3kd7cfi59hf.htm/, Retrieved Thu, 19 May 2011 14:56:47 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

1,2638 1,2640 1,2261 1,1989 1,2000 1,2146 1,2266 1,2191 1,2224 1,2507 1,2997 1,3406 1,3123 1,3013 1,3185 1,2943 1,2697 1,2155 1,2041 1,2295 1,2234 1,2022 1,1789 1,1861 1,2126 1,1940 1,2028 1,2273 1,2767 1,2661 1,2681 1,2810 1,2722 1,2617 1,2888 1,3205 1,2993 1,3080 1,3246 1,3513 1,3518 1,3421 1,3726 1,3626 1,3910 1,4233 1,4683 1,4559 1,4728 1,4759 1,5520 1,5754 1,5554 1,5562 1,5759 1,4955 1,4342 1,3266 1,2744 1,3511 1,3244 1,2797 1,3050 1,3199 1,3646 1,4014 1,4092 1,4266 1,4575 1,4821 1,4908 1,4579 1,4266 1,3680 1,3570 1,3417 1,2563 1,2223 1,2811 1,2903 1,3103 1,3901 1,3654 1,3221

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 4 seconds R Server 'Gwilym Jenkins' @ www.wessa.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 1.2638 NA NA 1.0026960805569 NA 2 1.264 NA NA 0.986993103631587 NA 3 1.2261 NA NA 1.00195032564548 NA 4 1.1989 NA NA 1.00677192096988 NA 5 1.2 NA NA 1.00176129462746 NA 6 1.2146 NA NA 0.991872921896452 NA 7 1.2266 1.25536667811305 1.24589583333333 1.0076016345238 0.977085039284067 8 1.2191 1.25202319124331 1.24947083333333 1.00204275109261 0.973704008461205 9 1.2224 1.25393523101074 1.254875 0.99925110549715 0.974850988926026 10 1.2507 1.25174806822198 1.2627 0.99132657655974 0.9991627163256 11 1.2997 1.26564605578312 1.26957916666667 0.99690203574002 1.02690637248959 12 1.3406 1.28630255114551 1.27252083333333 1.01083024925892 1.04221203542366 13 1.3123 1.27504922553783 1.27162083333333 1.0026960805569 1.02921516574896 14 1.3013 1.25458338391117 1.27111666666667 0.986993103631587 1.03723675659022 15 1.3185 1.27407168450475 1.27159166666667 1.00195032564548 1.03487112698256 16 1.2943 1.27821021551237 1.2696125 1.00677192096988 1.01258774518648 17 1.2697 1.26478207054268 1.26255833333333 1.00176129462746 1.00388836114289 18 1.2155 1.24091981417313 1.2510875 0.991872921896452 0.9795153450829 19 1.2041 1.24992562928662 1.24049583333333 1.0076016345238 0.963337315266686 20 1.2295 1.23438723882408 1.23187083333333 1.00204275109261 0.99604075717055 21 1.2234 1.22166358384945 1.22257916666667 0.99925110549715 1.00142135377816 22 1.2022 1.20442874630086 1.21496666666667 0.99132657655974 0.99814954076137 23 1.1789 1.20871048826692 1.21246666666667 0.99690203574002 0.97533694912364 24 1.1861 1.22802397548302 1.21486666666667 1.01083024925892 0.965860621355919 25 1.2126 1.22292991885055 1.21964166666667 1.0026960805569 0.991553139152681 26 1.194 1.20852781821296 1.22445416666667 0.986993103631587 0.987978912860737 27 1.2028 1.23102956843223 1.22863333333333 1.00195032564548 0.977068326256225 28 1.2273 1.24149659946101 1.23314583333333 1.00677192096988 0.988564930852674 29 1.2767 1.24238853160237 1.24020416666667 1.00176129462746 1.02761734153597 30 1.2661 1.24022137032396 1.25038333333333 0.991872921896452 1.02086613752614 31 1.2681 1.26917082050603 1.25959583333333 1.0076016345238 0.999156283386974 32 1.281 1.27054845660413 1.26795833333333 1.00204275109261 1.00822600928091 33 1.2722 1.27682640841917 1.27778333333333 0.99925110549715 0.99637663476518 34 1.2617 1.27685341377336 1.288025 0.99132657655974 0.988132221279358 35 1.2888 1.2923048777222 1.29632083333333 0.99690203574002 0.997287886331918 36 1.3205 1.3167243298555 1.30261666666667 1.01083024925892 1.00286747199767 37 1.2993 1.31366973624062 1.3101375 1.0026960805569 0.989061378332624 38 1.308 1.30074998633354 1.31789166666667 0.986993103631587 1.00557371804162 39 1.3246 1.32882826980127 1.32624166666667 1.00195032564548 0.996818046471944 40 1.3513 1.34698532236363 1.337925 1.00677192096988 1.0032032105805 41 1.3518 1.35451901251433 1.3521375 1.00176129462746 0.997992636139315 42 1.3421 1.35416277222681 1.36525833333333 0.991872921896452 0.991092081045045 43 1.3726 1.38860520091825 1.37812916666667 1.0076016345238 0.988473901071618 44 1.3626 1.39519839966192 1.39235416666667 1.00204275109261 0.976635294543182 45 1.391 1.40776993870202 1.408825 0.99925110549715 0.988087585733303 46 1.4233 1.4152549954433 1.4276375 0.99132657655974 1.00568449119247 47 1.4683 1.44098035507738 1.44545833333333 0.99690203574002 1.01895906826652 48 1.4559 1.47870566550653 1.4628625 1.01083024925892 0.9845772786035 49 1.4728 1.48424505114469 1.48025416666667 1.0026960805569 0.992288974697364 50 1.4759 1.47482678251529 1.4942625 0.986993103631587 1.00072769053114 51 1.552 1.50452860898926 1.5016 1.00195032564548 1.03155233521457 52 1.5754 1.50952445412121 1.49937083333333 1.00677192096988 1.04363993289339 53 1.5554 1.48988200745087 1.4872625 1.00176129462746 1.04397528946687 54 1.5562 1.46283071642825 1.47481666666667 0.991872921896452 1.06382781173732 55 1.5759 1.47539748671204 1.46426666666667 1.0076016345238 1.06811894028092 56 1.4955 1.45287013516543 1.44990833333333 1.00204275109261 1.02934182746465 57 1.4342 1.43036966787135 1.43144166666667 0.99925110549715 1.00267786168477 58 1.3266 1.39827026676491 1.41050416666667 0.99132657655974 0.948743623841238 59 1.2744 1.3875962510635 1.39190833333333 0.99690203574002 0.918422775373786 60 1.3511 1.39242709193958 1.37750833333333 1.01083024925892 0.97032010352369 61 1.3244 1.36779025718868 1.3641125 1.0026960805569 0.968277111961699 62 1.2797 1.33668064777699 1.35429583333333 0.986993103631587 0.95737153233141 63 1.305 1.35503344560993 1.35239583333333 1.00195032564548 0.963075859291867 64 1.3199 1.36905460184789 1.35984583333333 1.00677192096988 0.964095952212904 65 1.3646 1.37776404855508 1.37534166666667 1.00176129462746 0.99044535341963 66 1.4014 1.37752137953748 1.38880833333333 0.991872921896452 1.01733448265648 67 1.4092 1.40814007760758 1.39751666666667 1.0076016345238 1.00075271090517 68 1.4266 1.40832515970124 1.40545416666667 1.00204275109261 1.01297629327494 69 1.4575 1.41024308518813 1.4113 0.99925110549715 1.03350976530799 70 1.4821 1.40210752672168 1.414375 0.99132657655974 1.0570515967954 71 1.4908 1.40640031571264 1.41077083333333 0.99690203574002 1.06001113860998 72 1.4579 1.41394514087068 1.39879583333333 1.01083024925892 1.03108667929101 73 1.4266 1.38973258975153 1.38599583333333 1.0026960805569 1.02652842030211 74 1.368 1.35709495513711 1.37497916666667 0.986993103631587 1.00803557984032 75 1.357 1.36582528557573 1.36316666666667 1.00195032564548 0.993538495978266 76 1.3417 1.36236376345644 1.3532 1.00677192096988 0.984832418469485 77 1.2563 1.34650909616271 1.34414166666667 1.00176129462746 0.933005208490767 78 1.2223 1.32242283872613 1.33325833333333 0.991872921896452 0.92428833214755 79 1.2811 NA NA 1.0076016345238 NA 80 1.2903 NA NA 1.00204275109261 NA 81 1.3103 NA NA 0.99925110549715 NA 82 1.3901 NA NA 0.99132657655974 NA 83 1.3654 NA NA 0.99690203574002 NA 84 1.3221 NA NA 1.01083024925892 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/19/t1305809803qwpw3kd7cfi59hf/1ahus1305810007.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305809803qwpw3kd7cfi59hf/1ahus1305810007.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305809803qwpw3kd7cfi59hf/2drlp1305810007.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305809803qwpw3kd7cfi59hf/2drlp1305810007.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305809803qwpw3kd7cfi59hf/3fkt01305810007.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305809803qwpw3kd7cfi59hf/3fkt01305810007.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305809803qwpw3kd7cfi59hf/4uuaj1305810007.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305809803qwpw3kd7cfi59hf/4uuaj1305810007.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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