Decompositie Wisselkoers

*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 15:07:47 +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/t1305558258f3j9k4im3uvrtmp.htm/, Retrieved Mon, 16 May 2011 17:04:24 +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 'George Udny Yule' @ 216.218.223.82 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 1.2638 NA NA 0.00271255787037034 NA 2 1.264 NA NA -0.0183436921296296 NA 3 1.2261 NA NA 0.00271811342592593 NA 4 1.1989 NA NA 0.00947297453703705 NA 5 1.2 NA NA 0.00214866898148145 NA 6 1.2146 NA NA -0.00999577546296295 NA 7 1.2266 1.25766880787037 1.24589583333333 0.0117729745370371 -0.0310688078703703 8 1.2191 1.25235769675926 1.24947083333333 0.00288686342592598 -0.033257696759259 9 1.2224 1.25386394675926 1.254875 -0.00101105324074086 -0.0314639467592592 10 1.2507 1.25077158564815 1.2627 -0.0119284143518519 -7.15856481483534e-05 11 1.2997 1.26531811342593 1.26957916666667 -0.00426105324074068 0.0343818865740741 12 1.3406 1.28634866898148 1.27252083333333 0.0138278356481481 0.0542513310185186 13 1.3123 1.2743333912037 1.27162083333333 0.00271255787037034 0.0379666087962964 14 1.3013 1.25277297453704 1.27111666666667 -0.0183436921296296 0.0485270254629631 15 1.3185 1.27430978009259 1.27159166666667 0.00271811342592593 0.0441902199074076 16 1.2943 1.27908547453704 1.2696125 0.00947297453703705 0.0152145254629632 17 1.2697 1.26470700231481 1.26255833333333 0.00214866898148145 0.00499299768518546 18 1.2155 1.24109172453704 1.2510875 -0.00999577546296295 -0.0255917245370367 19 1.2041 1.25226880787037 1.24049583333333 0.0117729745370371 -0.0481688078703704 20 1.2295 1.23475769675926 1.23187083333333 0.00288686342592598 -0.00525769675925924 21 1.2234 1.22156811342593 1.22257916666667 -0.00101105324074086 0.00183188657407407 22 1.2022 1.20303825231481 1.21496666666667 -0.0119284143518519 -0.000838252314814492 23 1.1789 1.20820561342593 1.21246666666667 -0.00426105324074068 -0.0293056134259255 24 1.1861 1.22869450231481 1.21486666666667 0.0138278356481481 -0.0425945023148144 25 1.2126 1.22235422453704 1.21964166666667 0.00271255787037034 -0.009754224537037 26 1.194 1.20611047453704 1.22445416666667 -0.0183436921296296 -0.0121104745370371 27 1.2028 1.23135144675926 1.22863333333333 0.00271811342592593 -0.0285514467592589 28 1.2273 1.24261880787037 1.23314583333333 0.00947297453703705 -0.0153188078703701 29 1.2767 1.24235283564815 1.24020416666667 0.00214866898148145 0.0343471643518518 30 1.2661 1.24038755787037 1.25038333333333 -0.00999577546296295 0.0257124421296298 31 1.2681 1.27136880787037 1.25959583333333 0.0117729745370371 -0.00326880787037021 32 1.281 1.27084519675926 1.26795833333333 0.00288686342592598 0.0101548032407408 33 1.2722 1.27677228009259 1.27778333333333 -0.00101105324074086 -0.00457228009259225 34 1.2617 1.27609658564815 1.288025 -0.0119284143518519 -0.0143965856481481 35 1.2888 1.29205978009259 1.29632083333333 -0.00426105324074068 -0.00325978009259242 36 1.3205 1.31644450231481 1.30261666666667 0.0138278356481481 0.00405549768518543 37 1.2993 1.31285005787037 1.3101375 0.00271255787037034 -0.0135500578703704 38 1.308 1.29954797453704 1.31789166666667 -0.0183436921296296 0.0084520254629632 39 1.3246 1.32895978009259 1.32624166666667 0.00271811342592593 -0.00435978009259252 40 1.3513 1.34739797453704 1.337925 0.00947297453703705 0.00390202546296337 41 1.3518 1.35428616898148 1.3521375 0.00214866898148145 -0.00248616898148124 42 1.3421 1.35526255787037 1.36525833333333 -0.00999577546296295 -0.01316255787037 43 1.3726 1.3899021412037 1.37812916666667 0.0117729745370371 -0.0173021412037035 44 1.3626 1.39524103009259 1.39235416666667 0.00288686342592598 -0.0326410300925926 45 1.391 1.40781394675926 1.408825 -0.00101105324074086 -0.0168139467592592 46 1.4233 1.41570908564815 1.4276375 -0.0119284143518519 0.00759091435185222 47 1.4683 1.44119728009259 1.44545833333333 -0.00426105324074068 0.0271027199074076 48 1.4559 1.47669033564815 1.4628625 0.0138278356481481 -0.0207903356481483 49 1.4728 1.48296672453704 1.48025416666667 0.00271255787037034 -0.0101667245370372 50 1.4759 1.47591880787037 1.4942625 -0.0183436921296296 -1.8807870370452e-05 51 1.552 1.50431811342593 1.5016 0.00271811342592593 0.0476818865740742 52 1.5754 1.50884380787037 1.49937083333333 0.00947297453703705 0.0665561921296296 53 1.5554 1.48941116898148 1.4872625 0.00214866898148145 0.0659888310185188 54 1.5562 1.4648208912037 1.47481666666667 -0.00999577546296295 0.0913791087962963 55 1.5759 1.4760396412037 1.46426666666667 0.0117729745370371 0.0998603587962963 56 1.4955 1.45279519675926 1.44990833333333 0.00288686342592598 0.0427048032407407 57 1.4342 1.43043061342593 1.43144166666667 -0.00101105324074086 0.003769386574074 58 1.3266 1.39857575231481 1.41050416666667 -0.0119284143518519 -0.0719757523148148 59 1.2744 1.38764728009259 1.39190833333333 -0.00426105324074068 -0.113247280092593 60 1.3511 1.39133616898148 1.37750833333333 0.0138278356481481 -0.0402361689814814 61 1.3244 1.36682505787037 1.3641125 0.00271255787037034 -0.0424250578703702 62 1.2797 1.3359521412037 1.35429583333333 -0.0183436921296296 -0.0562521412037036 63 1.305 1.35511394675926 1.35239583333333 0.00271811342592593 -0.0501139467592595 64 1.3199 1.36931880787037 1.35984583333333 0.00947297453703705 -0.0494188078703703 65 1.3646 1.37749033564815 1.37534166666667 0.00214866898148145 -0.012890335648148 66 1.4014 1.37881255787037 1.38880833333333 -0.00999577546296295 0.0225874421296295 67 1.4092 1.4092896412037 1.39751666666667 0.0117729745370371 -8.96412037036942e-05 68 1.4266 1.40834103009259 1.40545416666667 0.00288686342592598 0.0182589699074076 69 1.4575 1.41028894675926 1.4113 -0.00101105324074086 0.0472110532407408 70 1.4821 1.40244658564815 1.414375 -0.0119284143518519 0.0796534143518521 71 1.4908 1.40650978009259 1.41077083333333 -0.00426105324074068 0.0842902199074078 72 1.4579 1.41262366898148 1.39879583333333 0.0138278356481481 0.0452763310185189 73 1.4266 1.3887083912037 1.38599583333333 0.00271255787037034 0.0378916087962966 74 1.368 1.35663547453704 1.37497916666667 -0.0183436921296296 0.0113645254629633 75 1.357 1.36588478009259 1.36316666666667 0.00271811342592593 -0.00888478009259264 76 1.3417 1.36267297453704 1.3532 0.00947297453703705 -0.0209729745370368 77 1.2563 1.34629033564815 1.34414166666667 0.00214866898148145 -0.0899903356481477 78 1.2223 1.32326255787037 1.33325833333333 -0.00999577546296295 -0.10096255787037 79 1.2811 NA NA 0.0117729745370371 NA 80 1.2903 NA NA 0.00288686342592598 NA 81 1.3103 NA NA -0.00101105324074086 NA 82 1.3901 NA NA -0.0119284143518519 NA 83 1.3654 NA NA -0.00426105324074068 NA 84 1.3221 NA NA 0.0138278356481481 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/16/t1305558258f3j9k4im3uvrtmp/1pspq1305558463.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305558258f3j9k4im3uvrtmp/1pspq1305558463.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305558258f3j9k4im3uvrtmp/2smto1305558463.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305558258f3j9k4im3uvrtmp/2smto1305558463.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305558258f3j9k4im3uvrtmp/3dofd1305558463.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305558258f3j9k4im3uvrtmp/3dofd1305558463.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305558258f3j9k4im3uvrtmp/42i1e1305558463.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305558258f3j9k4im3uvrtmp/42i1e1305558463.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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