Olieprijs classical decomposition

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 30 Nov 2010 19:52:26 +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/Nov/30/t1291146757cgup1vr7caasjj9.htm/, Retrieved Tue, 30 Nov 2010 20:52:37 +0100

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/Nov/30/t1291146757cgup1vr7caasjj9.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

46.85 48.05 54.63 53.22 49.87 56.42 59.03 64.99 65.55 62.27 58.34 59.45 65.54 61.93 62.97 70.16 70.96 70.97 74.46 73.08 63.90 59.14 59.40 62.09 54.35 59.39 60.74 64.04 63.53 67.53 74.15 72.36 79.63 85.66 94.63 91.74 92.93 95.35 105.42 112.46 125.46 134.02 133.48 116.69 103.76 76.72 57.44 42.04 41.92 39.26 48.06 49.95 59.21 69.70 64.29 71.14 69.47 75.82 78.15 74.60

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 46.85 NA NA 0.866661904355989 NA 2 48.05 NA NA 0.8666950993582 NA 3 54.63 NA NA 0.937255593993909 NA 4 53.22 NA NA 1.00053748045992 NA 5 49.87 NA NA 1.06599106865283 NA 6 56.42 NA NA 1.13717455313811 NA 7 59.03 58.4817382182484 57.3345833333333 1.14715488491501 0.897499186693677 8 64.99 59.8104921447999 58.6916666666667 1.11882547813325 0.989709518191973 9 65.55 60.6923630049933 59.6175 1.07486300499335 1.02292977536694 10 62.27 61.6521774692816 60.6708333333333 0.981344135948257 1.04586968241315 11 58.34 63.1829414051674 62.2554166666667 0.92752473850071 1.01033123322177 12 59.45 64.6163887242171 63.7404166666667 0.875972057550466 1.06474769343733 13 65.54 65.8562452376893 64.9895833333333 0.866661904355989 1.16362482406321 14 61.93 66.8362784326915 65.9695833333333 0.8666950993582 1.08315597075219 15 62.97 67.1751722606606 66.2379166666666 0.937255593993909 1.01430598405513 16 70.16 67.0392874804599 66.03875 1.00053748045992 1.06183582560592 17 70.96 67.0184910686528 65.9525 1.06599106865283 1.00931976577367 18 70.97 67.2438412198048 66.1066666666667 1.13717455313811 0.944066122472968 19 74.46 66.8975715515817 65.7504166666667 1.14715488491501 0.98719390136559 20 73.08 66.2971588114666 65.1783333333333 1.11882547813325 1.00215048280260 21 63.9 66.0544463383267 64.9795833333333 1.07486300499335 0.914894086513394 22 59.14 65.6130108026149 64.6316666666667 0.981344135948257 0.932426554428352 23 59.4 64.994608071834 64.0670833333333 0.92752473850071 0.999599407756416 24 62.09 64.4901387242171 63.6141666666667 0.875972057550466 1.11423697113663 25 54.35 64.3245785710227 63.4579166666667 0.866661904355989 0.988243648008665 26 59.39 64.2816950993582 63.415 0.8666950993582 1.08057517923544 27 60.74 64.9776722606606 64.0404166666667 0.937255593993909 1.01195825501121 28 64.04 66.8013708137933 65.8008333333333 1.00053748045992 0.97271713744689 29 63.53 69.4397410686528 68.37375 1.06599106865283 0.871637466371409 30 67.53 72.2142578864714 71.0770833333333 1.13717455313811 0.835487619561568 31 74.15 75.067154884915 73.92 1.14715488491501 0.87443420679483 32 72.36 78.1446588114666 77.0258333333333 1.11882547813325 0.839652924444632 33 79.63 81.4606963383267 80.3858333333333 1.07486300499335 0.92160342903004 34 85.66 85.2463441359482 84.265 0.981344135948257 1.03588015539157 35 94.63 89.7904414051674 88.8629166666667 0.92752473850071 1.14810808366757 36 91.74 95.0897220575505 94.21375 0.875972057550466 1.11161447273794 37 92.93 100.322911904356 99.45625 0.866661904355989 1.07813749552492 38 95.35 104.642111766025 103.775416666667 0.8666950993582 1.06013182144496 39 105.42 107.565172260661 106.627916666667 0.937255593993909 1.05485811150576 40 112.46 108.261370813793 107.260833333333 1.00053748045992 1.04790895171173 41 125.46 106.404741068653 105.33875 1.06599106865283 1.11728394129220 42 134.02 102.855507886471 101.718333333333 1.13717455313811 1.15862593383311 43 133.48 98.6692382182484 97.5220833333333 1.14715488491501 1.19313934916943 44 116.69 94.1784088114666 93.0595833333333 1.11882547813325 1.12075371858432 45 103.76 89.4073630049933 88.3325 1.07486300499335 1.09283935311315 46 76.72 84.319260802615 83.3379166666667 0.981344135948257 0.93809025179045 47 57.44 78.9004414051674 77.9729166666667 0.92752473850071 0.794227919377763 48 42.04 73.4084720575505 72.5325 0.875972057550466 0.661667506711621 49 41.92 67.8362452376893 66.9695833333333 0.866661904355989 0.722260676426735 50 39.26 63.0554450993582 62.18875 0.8666950993582 0.728403672590934 51 48.06 59.7993389273272 58.8620833333333 0.937255593993909 0.87114429345143 52 49.95 58.3963708137933 57.3958333333333 1.00053748045992 0.86980472925898 53 59.21 59.2872410686528 58.22125 1.06599106865283 0.954025470586245 54 69.7 61.5780078864715 60.4408333333333 1.13717455313811 1.01408696815588 55 64.29 NA NA 1.14715488491501 NA 56 71.14 NA NA 1.11882547813325 NA 57 69.47 NA NA 1.07486300499335 NA 58 75.82 NA NA 0.981344135948257 NA 59 78.15 NA NA 0.92752473850071 NA 60 74.6 NA NA 0.875972057550466 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291146757cgup1vr7caasjj9/1j0mj1291146742.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291146757cgup1vr7caasjj9/1j0mj1291146742.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291146757cgup1vr7caasjj9/2j0mj1291146742.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291146757cgup1vr7caasjj9/2j0mj1291146742.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291146757cgup1vr7caasjj9/3cr341291146742.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291146757cgup1vr7caasjj9/3cr341291146742.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291146757cgup1vr7caasjj9/451l71291146742.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291146757cgup1vr7caasjj9/451l71291146742.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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,m$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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