Decompositie van tijdreeksen voor witte wijn verkoop in Australië

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 01 Jun 2010 18:47:17 +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/Jun/01/t1275418097o2gd4u25rg06gf3.htm/, Retrieved Tue, 01 Jun 2010 20:48:25 +0200

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/Jun/01/t1275418097o2gd4u25rg06gf3.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

1954 2302 3054 2414 2226 2725 2589 3470 2400 3180 4009 3924 2072 2434 2956 2828 2687 2629 3150 4119 3030 3055 3821 4001 2529 2472 3134 2789 2758 2993 3282 3437 2804 3076 3782 3889 2271 2452 3084 2522 2769 3438 2839 3746 2632 2851 3871 3618 2389 2344 2678 2492 2858 2246 2800 3869 3007 3023 3907 4209 2353 2570 2903 2910 3782 2759 2931 3641 2794 3070 3576 4106 2452 2206 2488 2416 2534 2521 3093 3903 2907 3025 3812 4209

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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 1954 NA NA 0.779071606859315 NA 2 2302 NA NA 0.79920607921292 NA 3 3054 NA NA 0.949131913406112 NA 4 2414 NA NA 0.877488064859084 NA 5 2226 NA NA 0.956479894077186 NA 6 2725 NA NA 0.912968112749082 NA 7 2589 2779.23381909022 2858.83333333333 0.972156644000545 0.931551704004345 8 3470 3533.24392817090 2869.25 1.23141724428715 0.982100322124195 9 2400 2645.52283233676 2870.66666666667 0.921570889109415 0.907193077551369 10 3180 2919.81920343566 2883.83333333333 1.01247848469132 1.08910852982205 11 4009 3716.54112258151 2920.29166666667 1.27266093486604 1.07869114528063 12 3924 3861.2690221391 2935.5 1.31537013188183 1.0162462075295 13 2072 2302.05921431842 2954.875 0.779071606859315 0.90006372864456 14 2434 2401.84736980793 3005.29166666667 0.79920607921292 1.01338662506046 15 2956 2902.99905147871 3058.58333333333 0.949131913406112 1.01825730824621 16 2828 2702.33418174166 3079.625 0.877488064859084 1.04650269352599 17 2687 2933.12530184553 3066.58333333333 0.956479894077186 0.91608769605217 18 2629 2795.47032089966 3061.95833333333 0.912968112749082 0.940449977359773 19 3150 2998.33362273185 3084.20833333333 0.972156644000545 1.05058355618544 20 4119 3823.34530730422 3104.83333333333 1.23141724428715 1.07732879688658 21 3030 2869.61815353853 3113.83333333333 0.921570889109415 1.05588961244328 22 3055 3158.55319280517 3119.625 1.01247848469132 0.967214991648376 23 3821 3971.92175017797 3120.95833333333 1.27266093486604 0.962002839010813 24 4001 4129.05645815471 3139.08333333333 1.31537013188183 0.96898650830947 25 2529 2461.67150977372 3159.75 0.779071606859315 1.02735072082484 26 2472 2506.97626947773 3136.83333333333 0.79920607921292 0.986048424189904 27 3134 2941.35979964554 3099 0.949131913406112 1.06549358578222 28 2789 2711.84030244430 3090.45833333333 0.877488064859084 1.02845289137644 29 2758 2955.24389939607 3089.70833333333 0.956479894077186 0.933256304348898 30 2993 2815.06109498573 3083.41666666667 0.912968112749082 1.06320960682922 31 3282 2982.57658379367 3068 0.972156644000545 1.10039085595766 32 3437 3763.72418905998 3056.41666666667 1.23141724428715 0.913191250833504 33 2804 2814.0167098956 3053.5 0.921570889109415 0.99644042273794 34 3076 3078.22989968632 3040.29166666667 1.01247848469132 0.99927559027136 35 3782 3855.68538479353 3029.625 1.27266093486604 0.980889160437171 36 3889 4010.07026830823 3048.625 1.31537013188183 0.969808442194878 37 2271 2375.16210009538 3048.70833333333 0.779071606859315 0.956145266846755 38 2452 2432.08399980482 3043.125 0.79920607921292 1.00818886197877 39 3084 2893.745015323 3048.83333333333 0.949131913406112 1.06574697620888 40 2522 2660.79974667166 3032.29166666667 0.877488064859084 0.947835327763662 41 2769 2894.90595941136 3026.625 0.956479894077186 0.956507754940349 42 3438 2756.28877272751 3019.04166666667 0.912968112749082 1.24732939234008 43 2839 2928.78391615897 3012.66666666667 0.972156644000545 0.969344301686578 44 3746 3710.36277514087 3013.08333333333 1.23141724428715 1.00960478180136 45 2632 2757.032909919 2991.66666666667 0.921570889109415 0.95464946774151 46 2851 3010.60477422965 2973.5 1.01247848469132 0.946985809762928 47 3871 3787.38591462239 2975.95833333333 1.27266093486604 1.02207699116554 48 3618 3854.03448641375 2930 1.31537013188183 0.938756519370593 49 2389 2242.7199269293 2878.70833333333 0.779071606859315 1.06522440511374 50 2344 2303.47842155814 2882.20833333333 0.79920607921292 1.01759147299259 51 2678 2755.29039745488 2902.95833333333 0.949131913406112 0.971948366122758 52 2492 2567.31070576146 2925.75 0.877488064859084 0.97066552731913 53 2858 2806.71054251166 2934.41666666667 0.956479894077186 1.01827386782908 54 2246 2702.88013813169 2960.54166666667 0.912968112749082 0.830965446197145 55 2800 2900.59137348296 2983.66666666667 0.972156644000545 0.965320391419985 56 3869 3683.8873043887 2991.58333333333 1.23141724428715 1.05024928297637 57 3007 2774.27396530276 3010.375 0.921570889109415 1.08388718547912 58 3023 3075.06590442166 3037.16666666667 1.01247848469132 0.983068361446564 59 3907 3936.44632661858 3093.08333333333 1.27266093486604 0.99251956608186 60 4209 4147.30721873457 3152.95833333333 1.31537013188183 1.01487538251007 61 2353 2477.28540322786 3179.79166666667 0.779071606859315 0.94983000220083 62 2570 2538.07870606043 3175.75 0.79920607921292 1.01257695195320 63 2903 2996.76537509062 3157.375 0.949131913406112 0.968711139060132 64 2910 2764.48958633584 3150.45833333333 0.877488064859084 1.05263554414651 65 3782 3002.03170754801 3138.625 0.956479894077186 1.25981347581737 66 2759 2848.95503617154 3120.54166666667 0.912968112749082 0.968425252406783 67 2931 3033.4932880232 3120.375 0.972156644000545 0.966212785626439 68 3641 3828.88668490351 3109.33333333333 1.23141724428715 0.950929160258435 69 2794 2835.55842942853 3076.875 0.921570889109415 0.985343828927233 70 3070 3076.92211497693 3039 1.01247848469132 0.997750311929173 71 3576 3775.24260820221 2966.41666666667 1.27266093486604 0.947223892904438 72 4106 3820.49254805076 2904.5 1.31537013188183 1.07473053496594 73 2452 2260.34642203449 2901.33333333333 0.779071606859315 1.08478947124972 74 2206 2332.88254522252 2919 0.79920607921292 0.94561125870552 75 2488 2785.34624137941 2934.625 0.949131913406112 0.893246219460259 76 2416 2577.58462852086 2937.45833333333 0.877488064859084 0.93731161074871 77 2534 2817.23182134651 2945.41666666667 0.956479894077186 0.899464495892589 78 2521 2701.96717001894 2959.54166666667 0.912968112749082 0.933023919747451 79 3093 NA NA 0.972156644000545 NA 80 3903 NA NA 1.23141724428715 NA 81 2907 NA NA 0.921570889109415 NA 82 3025 NA NA 1.01247848469132 NA 83 3812 NA NA 1.27266093486604 NA 84 4209 NA NA 1.31537013188183 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/01/t1275418097o2gd4u25rg06gf3/1v7d01275418035.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275418097o2gd4u25rg06gf3/1v7d01275418035.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275418097o2gd4u25rg06gf3/25yu21275418035.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275418097o2gd4u25rg06gf3/25yu21275418035.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275418097o2gd4u25rg06gf3/35yu21275418035.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275418097o2gd4u25rg06gf3/35yu21275418035.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275418097o2gd4u25rg06gf3/4ypu51275418035.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275418097o2gd4u25rg06gf3/4ypu51275418035.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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