Classical decomposition - Joachim Van Der Aa - Eigen tijdreeks

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 24 May 2008 08:16:33 -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/24/t1211639449tyojciufja8ayvg.htm/, Retrieved Sat, 24 May 2008 16:30:54 +0200

User-defined keywords:

Dataseries X:

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66.2 66.2 66.08 66.31 66.39 66.37 66.23 66.27 66.27 66.27 66.28 66.28 66.28 66.26 66.13 65.86 65.9 65.94 65.94 65.91 65.95 65.91 66.08 66.47 66.47 66.56 66.78 67.08 67.28 67.27 67.27 67.26 67.37 67.5 67.63 67.64 67.64 67.71 67.87 67.93 68.33 68.39 68.39 68.58 68.44 68.49 68.52 68.54 68.54 68.54 68.62 68.75 68.71 68.72 68.72 68.72 68.92 68.9 69.12 69.09 69.09 69.1 69.16 68.83 68.52 68.53 68.53 68.51 68.38 68.44 68.41 68.42 68.42 68.45 68.63 68.84 68.72 68.37 68.37 68.47 68.69 68.46 68.17 68.17

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 13 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 66.2 NA NA 0.0286319444444478 NA 2 66.2 NA NA 0.0207986111111130 NA 3 66.08 NA NA 0.0625486111111092 NA 4 66.31 NA NA 0.00488194444444663 NA 5 66.39 NA NA 0.0270486111111102 NA 6 66.37 NA NA 0.0134652777777778 NA 7 66.23 66.2436319444444 66.2658333333333 -0.0222013888888905 -0.0136319444444410 8 66.27 66.2393819444444 66.2716666666667 -0.0322847222222232 0.03061805555555 9 66.27 66.2208819444444 66.27625 -0.0553680555555554 0.049118055555553 10 66.27 66.1945486111111 66.2595833333333 -0.0650347222222247 0.0754513888888795 11 66.28 66.2110486111111 66.2204166666667 -0.00936805555555746 0.0689513888888911 12 66.28 66.2089652777778 66.1820833333333 0.0268819444444465 0.0710347222222225 13 66.28 66.1807152777778 66.1520833333333 0.0286319444444478 0.0992847222222224 14 66.26 66.1457986111111 66.125 0.0207986111111130 0.114201388888887 15 66.13 66.1592152777778 66.0966666666667 0.0625486111111092 -0.0292152777777801 16 65.86 66.0732152777778 66.0683333333333 0.00488194444444663 -0.213215277777778 17 65.9 66.0720486111111 66.045 0.0270486111111102 -0.172048611111109 18 65.94 66.0580486111111 66.0445833333333 0.0134652777777778 -0.118048611111107 19 65.94 66.0382152777778 66.0604166666667 -0.0222013888888905 -0.0982152777777827 20 65.91 66.0485486111111 66.0808333333333 -0.0322847222222232 -0.138548611111105 21 65.95 66.0650486111111 66.1204166666667 -0.0553680555555554 -0.115048611111092 22 65.91 66.1332986111111 66.1983333333333 -0.0650347222222247 -0.223298611111119 23 66.08 66.2972986111111 66.3066666666667 -0.00936805555555746 -0.217298611111104 24 66.47 66.4464652777778 66.4195833333333 0.0268819444444465 0.0235347222222231 25 66.47 66.5590486111111 66.5304166666667 0.0286319444444478 -0.0890486111111102 26 66.56 66.6628819444445 66.6420833333333 0.0207986111111130 -0.102881944444448 27 66.78 66.8200486111111 66.7575 0.0625486111111092 -0.0400486111111036 28 67.08 66.8877986111111 66.8829166666667 0.00488194444444663 0.192201388888890 29 67.28 67.0407986111111 67.01375 0.0270486111111102 0.239201388888901 30 67.27 67.1405486111111 67.1270833333333 0.0134652777777778 0.129451388888896 31 67.27 67.2023819444444 67.2245833333333 -0.0222013888888905 0.0676180555555561 32 67.26 67.2889652777778 67.32125 -0.0322847222222232 -0.0289652777777576 33 67.37 67.3592152777778 67.4145833333333 -0.0553680555555554 0.0107847222222262 34 67.5 67.4303819444445 67.4954166666667 -0.0650347222222247 0.0696180555555372 35 67.63 67.5652152777778 67.5745833333333 -0.00936805555555746 0.064784722222214 36 67.64 67.6918819444444 67.665 0.0268819444444465 -0.0518819444444318 37 67.64 67.7869652777778 67.7583333333333 0.0286319444444478 -0.146965277777767 38 67.71 67.8807986111111 67.86 0.0207986111111130 -0.170798611111124 39 67.87 68.0221319444445 67.9595833333333 0.0625486111111092 -0.152131944444449 40 67.93 68.0502986111111 68.0454166666667 0.00488194444444663 -0.120298611111110 41 68.33 68.1507986111111 68.12375 0.0270486111111102 0.179201388888885 42 68.39 68.2117986111111 68.1983333333333 0.0134652777777778 0.178201388888880 43 68.39 68.2511319444444 68.2733333333333 -0.0222013888888905 0.138868055555562 44 68.58 68.3131319444445 68.3454166666667 -0.0322847222222232 0.266868055555548 45 68.44 68.3558819444444 68.41125 -0.0553680555555554 0.0841180555555496 46 68.49 68.4116319444444 68.4766666666667 -0.0650347222222247 0.0783680555555577 47 68.52 68.5172986111111 68.5266666666667 -0.00936805555555746 0.00270138888888027 48 68.54 68.5831319444444 68.55625 0.0268819444444465 -0.0431319444444398 49 68.54 68.6123819444444 68.58375 0.0286319444444478 -0.0723819444444302 50 68.54 68.6241319444444 68.6033333333333 0.0207986111111130 -0.0841319444444366 51 68.62 68.6917152777778 68.6291666666667 0.0625486111111092 -0.0717152777777699 52 68.75 68.6711319444444 68.66625 0.00488194444444663 0.0788680555555601 53 68.71 68.7353819444444 68.7083333333333 0.0270486111111102 -0.0253819444444474 54 68.72 68.7697152777778 68.75625 0.0134652777777778 -0.0497152777777927 55 68.72 68.7798819444445 68.8020833333333 -0.0222013888888905 -0.0598819444444558 56 68.72 68.8160486111111 68.8483333333333 -0.0322847222222232 -0.0960486111111152 57 68.92 68.8387986111111 68.8941666666667 -0.0553680555555554 0.0812013888888998 58 68.9 68.8549652777778 68.92 -0.0650347222222247 0.0450347222222405 59 69.12 68.9060486111111 68.9154166666667 -0.00936805555555746 0.213951388888901 60 69.09 68.9264652777778 68.8995833333333 0.0268819444444465 0.163534722222238 61 69.09 68.9123819444444 68.88375 0.0286319444444478 0.177618055555570 62 69.1 68.8878819444444 68.8670833333333 0.0207986111111130 0.212118055555550 63 69.16 68.8983819444445 68.8358333333333 0.0625486111111092 0.261618055555545 64 68.83 68.7990486111111 68.7941666666667 0.00488194444444663 0.0309513888888802 65 68.52 68.7724652777778 68.7454166666667 0.0270486111111102 -0.252465277777787 66 68.53 68.7013819444444 68.6879166666667 0.0134652777777778 -0.171381944444448 67 68.53 68.6098819444444 68.6320833333333 -0.0222013888888905 -0.0798819444444376 68 68.51 68.5447986111111 68.5770833333333 -0.0322847222222232 -0.0347986111110856 69 68.38 68.4725486111111 68.5279166666667 -0.0553680555555554 -0.0925486111110985 70 68.44 68.4412152777778 68.50625 -0.0650347222222247 -0.00121527777777430 71 68.41 68.5056319444444 68.515 -0.00936805555555746 -0.0956319444444489 72 68.42 68.5435486111111 68.5166666666667 0.0268819444444465 -0.123548611111104 73 68.42 68.5319652777778 68.5033333333333 0.0286319444444478 -0.111965277777770 74 68.45 68.5157986111111 68.495 0.0207986111111130 -0.06579861111112 75 68.63 68.5687986111111 68.50625 0.0625486111111092 0.0612013888888896 76 68.84 68.5248819444444 68.52 0.00488194444444663 0.315118055555573 77 68.72 68.5378819444444 68.5108333333333 0.0270486111111102 0.182118055555563 78 68.37 68.5038819444444 68.4904166666666 0.0134652777777778 -0.133881944444425 79 68.37 NA NA -0.0222013888888905 NA 80 68.47 NA NA -0.0322847222222232 NA 81 68.69 NA NA -0.0553680555555554 NA 82 68.46 NA NA -0.0650347222222247 NA 83 68.17 NA NA -0.00936805555555746 NA 84 68.17 NA NA 0.0268819444444465 NA

Charts produced by software:

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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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