FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationTue, 21 Dec 2010 12:03:05 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t129293297560wcfuq4vue7gs3.htm/, Retrieved Sat, 18 May 2024 08:17:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113337, Retrieved Sat, 18 May 2024 08:17:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
-  M D  [Classical Decomposition] [WS8 - Classical D...] [2010-11-28 09:41:43] [8ef49741e164ec6343c90c7935194465]
- R  D      [Classical Decomposition] [Paper; Classical ...] [2010-12-21 12:03:05] [50e0b5177c9c80b42996aa89930b928a] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
108,35
109,87
111,30
115,50
116,22
116,63
116,84
116,63
117,03
117,00
117,14
116,64
117,24
117,52
117,83
119,79
120,86
120,75
120,63
120,89
120,23
121,19
120,79
120,09
120,86
121,10
121,47
122,01
123,94
125,78
125,31
125,79
126,12
125,57
125,44
126,12
126,01
126,50
126,13
126,66
126,33
126,61
126,36
126,83
125,90
126,29
126,37
125,11

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113337&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113337&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113337&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1108.35NANA-0.71075231481482NA
2109.87NANA-0.647974537037033NA
3111.3NANA-0.809502314814815NA
4115.5NANA-0.0517245370370397NA
5116.22NANA0.581053240740726NA
6116.63NANA1.00521990740740NA
7116.84116.264942129630115.2995833333330.9653587962962970.575057870370372
8116.63116.654525462963115.988750.665775462962966-0.0245254629629699
9117.03116.831747685185116.5795833333330.2521643518518570.198252314814809
10117117.048275462963117.0304166666670.0178587962962966-0.0482754629629625
11117.14116.994942129630117.4025-0.4075578703703620.145057870370380
12116.64116.907581018519117.7675-0.859918981481479-0.267581018518513
13117.24117.386331018519118.097083333333-0.71075231481482-0.146331018518509
14117.52117.784525462963118.4325-0.647974537037033-0.264525462962965
15117.83117.933831018519118.743333333333-0.809502314814815-0.103831018518505
16119.79118.999525462963119.05125-0.05172453703703970.79047453703707
17120.86119.958969907407119.3779166666670.5810532407407260.90103009259262
18120.75120.678969907407119.673751.005219907407400.0710300925926077
19120.63120.933692129630119.9683333333330.965358796296297-0.303692129629624
20120.89120.934108796296120.2683333333330.665775462962966-0.0441087962962854
21120.23120.821331018519120.5691666666670.252164351851857-0.591331018518503
22121.19120.831192129630120.8133333333330.01785879629629660.358807870370384
23120.79120.626608796296121.034166666667-0.4075578703703620.163391203703725
24120.09120.512164351852121.372083333333-0.859918981481479-0.422164351851833
25120.86121.065914351852121.776666666667-0.71075231481482-0.205914351851845
26121.1121.527858796296122.175833333333-0.647974537037033-0.427858796296277
27121.47121.815914351852122.625416666667-0.809502314814815-0.345914351851832
28122.01123.001608796296123.053333333333-0.0517245370370397-0.991608796296276
29123.94124.010636574074123.4295833333330.581053240740726-0.0706365740740722
30125.78124.879803240741123.8745833333331.005219907407400.90019675925926
31125.31125.305775462963124.3404166666670.9653587962962970.00422453703703241
32125.79125.445775462963124.780.6657754629629660.344224537037036
33126.12125.451331018519125.1991666666670.2521643518518570.668668981481474
34125.57125.604942129630125.5870833333330.0178587962962966-0.0349421296296271
35125.44125.472858796296125.880416666667-0.407557870370362-0.0328587962962956
36126.12125.154664351852126.014583333333-0.8599189814814790.965335648148155
37126.01125.382164351852126.092916666667-0.710752314814820.627835648148164
38126.5125.532025462963126.18-0.6479745370370330.967974537037023
39126.13125.404664351852126.214166666667-0.8095023148148150.725335648148146
40126.66126.183275462963126.235-0.05172453703703970.476724537037015
41126.33126.884803240741126.303750.581053240740726-0.554803240740767
42126.61127.305636574074126.3004166666671.00521990740740-0.695636574074086
43126.36NANA0.965358796296297NA
44126.83NANA0.665775462962966NA
45125.9NANA0.252164351851857NA
46126.29NANA0.0178587962962966NA
47126.37NANA-0.407557870370362NA
48125.11NANA-0.859918981481479NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 108.35 & NA & NA & -0.71075231481482 & NA \tabularnewline
2 & 109.87 & NA & NA & -0.647974537037033 & NA \tabularnewline
3 & 111.3 & NA & NA & -0.809502314814815 & NA \tabularnewline
4 & 115.5 & NA & NA & -0.0517245370370397 & NA \tabularnewline
5 & 116.22 & NA & NA & 0.581053240740726 & NA \tabularnewline
6 & 116.63 & NA & NA & 1.00521990740740 & NA \tabularnewline
7 & 116.84 & 116.264942129630 & 115.299583333333 & 0.965358796296297 & 0.575057870370372 \tabularnewline
8 & 116.63 & 116.654525462963 & 115.98875 & 0.665775462962966 & -0.0245254629629699 \tabularnewline
9 & 117.03 & 116.831747685185 & 116.579583333333 & 0.252164351851857 & 0.198252314814809 \tabularnewline
10 & 117 & 117.048275462963 & 117.030416666667 & 0.0178587962962966 & -0.0482754629629625 \tabularnewline
11 & 117.14 & 116.994942129630 & 117.4025 & -0.407557870370362 & 0.145057870370380 \tabularnewline
12 & 116.64 & 116.907581018519 & 117.7675 & -0.859918981481479 & -0.267581018518513 \tabularnewline
13 & 117.24 & 117.386331018519 & 118.097083333333 & -0.71075231481482 & -0.146331018518509 \tabularnewline
14 & 117.52 & 117.784525462963 & 118.4325 & -0.647974537037033 & -0.264525462962965 \tabularnewline
15 & 117.83 & 117.933831018519 & 118.743333333333 & -0.809502314814815 & -0.103831018518505 \tabularnewline
16 & 119.79 & 118.999525462963 & 119.05125 & -0.0517245370370397 & 0.79047453703707 \tabularnewline
17 & 120.86 & 119.958969907407 & 119.377916666667 & 0.581053240740726 & 0.90103009259262 \tabularnewline
18 & 120.75 & 120.678969907407 & 119.67375 & 1.00521990740740 & 0.0710300925926077 \tabularnewline
19 & 120.63 & 120.933692129630 & 119.968333333333 & 0.965358796296297 & -0.303692129629624 \tabularnewline
20 & 120.89 & 120.934108796296 & 120.268333333333 & 0.665775462962966 & -0.0441087962962854 \tabularnewline
21 & 120.23 & 120.821331018519 & 120.569166666667 & 0.252164351851857 & -0.591331018518503 \tabularnewline
22 & 121.19 & 120.831192129630 & 120.813333333333 & 0.0178587962962966 & 0.358807870370384 \tabularnewline
23 & 120.79 & 120.626608796296 & 121.034166666667 & -0.407557870370362 & 0.163391203703725 \tabularnewline
24 & 120.09 & 120.512164351852 & 121.372083333333 & -0.859918981481479 & -0.422164351851833 \tabularnewline
25 & 120.86 & 121.065914351852 & 121.776666666667 & -0.71075231481482 & -0.205914351851845 \tabularnewline
26 & 121.1 & 121.527858796296 & 122.175833333333 & -0.647974537037033 & -0.427858796296277 \tabularnewline
27 & 121.47 & 121.815914351852 & 122.625416666667 & -0.809502314814815 & -0.345914351851832 \tabularnewline
28 & 122.01 & 123.001608796296 & 123.053333333333 & -0.0517245370370397 & -0.991608796296276 \tabularnewline
29 & 123.94 & 124.010636574074 & 123.429583333333 & 0.581053240740726 & -0.0706365740740722 \tabularnewline
30 & 125.78 & 124.879803240741 & 123.874583333333 & 1.00521990740740 & 0.90019675925926 \tabularnewline
31 & 125.31 & 125.305775462963 & 124.340416666667 & 0.965358796296297 & 0.00422453703703241 \tabularnewline
32 & 125.79 & 125.445775462963 & 124.78 & 0.665775462962966 & 0.344224537037036 \tabularnewline
33 & 126.12 & 125.451331018519 & 125.199166666667 & 0.252164351851857 & 0.668668981481474 \tabularnewline
34 & 125.57 & 125.604942129630 & 125.587083333333 & 0.0178587962962966 & -0.0349421296296271 \tabularnewline
35 & 125.44 & 125.472858796296 & 125.880416666667 & -0.407557870370362 & -0.0328587962962956 \tabularnewline
36 & 126.12 & 125.154664351852 & 126.014583333333 & -0.859918981481479 & 0.965335648148155 \tabularnewline
37 & 126.01 & 125.382164351852 & 126.092916666667 & -0.71075231481482 & 0.627835648148164 \tabularnewline
38 & 126.5 & 125.532025462963 & 126.18 & -0.647974537037033 & 0.967974537037023 \tabularnewline
39 & 126.13 & 125.404664351852 & 126.214166666667 & -0.809502314814815 & 0.725335648148146 \tabularnewline
40 & 126.66 & 126.183275462963 & 126.235 & -0.0517245370370397 & 0.476724537037015 \tabularnewline
41 & 126.33 & 126.884803240741 & 126.30375 & 0.581053240740726 & -0.554803240740767 \tabularnewline
42 & 126.61 & 127.305636574074 & 126.300416666667 & 1.00521990740740 & -0.695636574074086 \tabularnewline
43 & 126.36 & NA & NA & 0.965358796296297 & NA \tabularnewline
44 & 126.83 & NA & NA & 0.665775462962966 & NA \tabularnewline
45 & 125.9 & NA & NA & 0.252164351851857 & NA \tabularnewline
46 & 126.29 & NA & NA & 0.0178587962962966 & NA \tabularnewline
47 & 126.37 & NA & NA & -0.407557870370362 & NA \tabularnewline
48 & 125.11 & NA & NA & -0.859918981481479 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113337&T=1

[TABLE]
[ROW][C]Classical Decomposition by Moving Averages[/C][/ROW]
[ROW][C]t[/C][C]Observations[/C][C]Fit[/C][C]Trend[/C][C]Seasonal[/C][C]Random[/C][/ROW]
[ROW][C]1[/C][C]108.35[/C][C]NA[/C][C]NA[/C][C]-0.71075231481482[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]109.87[/C][C]NA[/C][C]NA[/C][C]-0.647974537037033[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]111.3[/C][C]NA[/C][C]NA[/C][C]-0.809502314814815[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]115.5[/C][C]NA[/C][C]NA[/C][C]-0.0517245370370397[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]116.22[/C][C]NA[/C][C]NA[/C][C]0.581053240740726[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]116.63[/C][C]NA[/C][C]NA[/C][C]1.00521990740740[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]116.84[/C][C]116.264942129630[/C][C]115.299583333333[/C][C]0.965358796296297[/C][C]0.575057870370372[/C][/ROW]
[ROW][C]8[/C][C]116.63[/C][C]116.654525462963[/C][C]115.98875[/C][C]0.665775462962966[/C][C]-0.0245254629629699[/C][/ROW]
[ROW][C]9[/C][C]117.03[/C][C]116.831747685185[/C][C]116.579583333333[/C][C]0.252164351851857[/C][C]0.198252314814809[/C][/ROW]
[ROW][C]10[/C][C]117[/C][C]117.048275462963[/C][C]117.030416666667[/C][C]0.0178587962962966[/C][C]-0.0482754629629625[/C][/ROW]
[ROW][C]11[/C][C]117.14[/C][C]116.994942129630[/C][C]117.4025[/C][C]-0.407557870370362[/C][C]0.145057870370380[/C][/ROW]
[ROW][C]12[/C][C]116.64[/C][C]116.907581018519[/C][C]117.7675[/C][C]-0.859918981481479[/C][C]-0.267581018518513[/C][/ROW]
[ROW][C]13[/C][C]117.24[/C][C]117.386331018519[/C][C]118.097083333333[/C][C]-0.71075231481482[/C][C]-0.146331018518509[/C][/ROW]
[ROW][C]14[/C][C]117.52[/C][C]117.784525462963[/C][C]118.4325[/C][C]-0.647974537037033[/C][C]-0.264525462962965[/C][/ROW]
[ROW][C]15[/C][C]117.83[/C][C]117.933831018519[/C][C]118.743333333333[/C][C]-0.809502314814815[/C][C]-0.103831018518505[/C][/ROW]
[ROW][C]16[/C][C]119.79[/C][C]118.999525462963[/C][C]119.05125[/C][C]-0.0517245370370397[/C][C]0.79047453703707[/C][/ROW]
[ROW][C]17[/C][C]120.86[/C][C]119.958969907407[/C][C]119.377916666667[/C][C]0.581053240740726[/C][C]0.90103009259262[/C][/ROW]
[ROW][C]18[/C][C]120.75[/C][C]120.678969907407[/C][C]119.67375[/C][C]1.00521990740740[/C][C]0.0710300925926077[/C][/ROW]
[ROW][C]19[/C][C]120.63[/C][C]120.933692129630[/C][C]119.968333333333[/C][C]0.965358796296297[/C][C]-0.303692129629624[/C][/ROW]
[ROW][C]20[/C][C]120.89[/C][C]120.934108796296[/C][C]120.268333333333[/C][C]0.665775462962966[/C][C]-0.0441087962962854[/C][/ROW]
[ROW][C]21[/C][C]120.23[/C][C]120.821331018519[/C][C]120.569166666667[/C][C]0.252164351851857[/C][C]-0.591331018518503[/C][/ROW]
[ROW][C]22[/C][C]121.19[/C][C]120.831192129630[/C][C]120.813333333333[/C][C]0.0178587962962966[/C][C]0.358807870370384[/C][/ROW]
[ROW][C]23[/C][C]120.79[/C][C]120.626608796296[/C][C]121.034166666667[/C][C]-0.407557870370362[/C][C]0.163391203703725[/C][/ROW]
[ROW][C]24[/C][C]120.09[/C][C]120.512164351852[/C][C]121.372083333333[/C][C]-0.859918981481479[/C][C]-0.422164351851833[/C][/ROW]
[ROW][C]25[/C][C]120.86[/C][C]121.065914351852[/C][C]121.776666666667[/C][C]-0.71075231481482[/C][C]-0.205914351851845[/C][/ROW]
[ROW][C]26[/C][C]121.1[/C][C]121.527858796296[/C][C]122.175833333333[/C][C]-0.647974537037033[/C][C]-0.427858796296277[/C][/ROW]
[ROW][C]27[/C][C]121.47[/C][C]121.815914351852[/C][C]122.625416666667[/C][C]-0.809502314814815[/C][C]-0.345914351851832[/C][/ROW]
[ROW][C]28[/C][C]122.01[/C][C]123.001608796296[/C][C]123.053333333333[/C][C]-0.0517245370370397[/C][C]-0.991608796296276[/C][/ROW]
[ROW][C]29[/C][C]123.94[/C][C]124.010636574074[/C][C]123.429583333333[/C][C]0.581053240740726[/C][C]-0.0706365740740722[/C][/ROW]
[ROW][C]30[/C][C]125.78[/C][C]124.879803240741[/C][C]123.874583333333[/C][C]1.00521990740740[/C][C]0.90019675925926[/C][/ROW]
[ROW][C]31[/C][C]125.31[/C][C]125.305775462963[/C][C]124.340416666667[/C][C]0.965358796296297[/C][C]0.00422453703703241[/C][/ROW]
[ROW][C]32[/C][C]125.79[/C][C]125.445775462963[/C][C]124.78[/C][C]0.665775462962966[/C][C]0.344224537037036[/C][/ROW]
[ROW][C]33[/C][C]126.12[/C][C]125.451331018519[/C][C]125.199166666667[/C][C]0.252164351851857[/C][C]0.668668981481474[/C][/ROW]
[ROW][C]34[/C][C]125.57[/C][C]125.604942129630[/C][C]125.587083333333[/C][C]0.0178587962962966[/C][C]-0.0349421296296271[/C][/ROW]
[ROW][C]35[/C][C]125.44[/C][C]125.472858796296[/C][C]125.880416666667[/C][C]-0.407557870370362[/C][C]-0.0328587962962956[/C][/ROW]
[ROW][C]36[/C][C]126.12[/C][C]125.154664351852[/C][C]126.014583333333[/C][C]-0.859918981481479[/C][C]0.965335648148155[/C][/ROW]
[ROW][C]37[/C][C]126.01[/C][C]125.382164351852[/C][C]126.092916666667[/C][C]-0.71075231481482[/C][C]0.627835648148164[/C][/ROW]
[ROW][C]38[/C][C]126.5[/C][C]125.532025462963[/C][C]126.18[/C][C]-0.647974537037033[/C][C]0.967974537037023[/C][/ROW]
[ROW][C]39[/C][C]126.13[/C][C]125.404664351852[/C][C]126.214166666667[/C][C]-0.809502314814815[/C][C]0.725335648148146[/C][/ROW]
[ROW][C]40[/C][C]126.66[/C][C]126.183275462963[/C][C]126.235[/C][C]-0.0517245370370397[/C][C]0.476724537037015[/C][/ROW]
[ROW][C]41[/C][C]126.33[/C][C]126.884803240741[/C][C]126.30375[/C][C]0.581053240740726[/C][C]-0.554803240740767[/C][/ROW]
[ROW][C]42[/C][C]126.61[/C][C]127.305636574074[/C][C]126.300416666667[/C][C]1.00521990740740[/C][C]-0.695636574074086[/C][/ROW]
[ROW][C]43[/C][C]126.36[/C][C]NA[/C][C]NA[/C][C]0.965358796296297[/C][C]NA[/C][/ROW]
[ROW][C]44[/C][C]126.83[/C][C]NA[/C][C]NA[/C][C]0.665775462962966[/C][C]NA[/C][/ROW]
[ROW][C]45[/C][C]125.9[/C][C]NA[/C][C]NA[/C][C]0.252164351851857[/C][C]NA[/C][/ROW]
[ROW][C]46[/C][C]126.29[/C][C]NA[/C][C]NA[/C][C]0.0178587962962966[/C][C]NA[/C][/ROW]
[ROW][C]47[/C][C]126.37[/C][C]NA[/C][C]NA[/C][C]-0.407557870370362[/C][C]NA[/C][/ROW]
[ROW][C]48[/C][C]125.11[/C][C]NA[/C][C]NA[/C][C]-0.859918981481479[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113337&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113337&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1108.35NANA-0.71075231481482NA
2109.87NANA-0.647974537037033NA
3111.3NANA-0.809502314814815NA
4115.5NANA-0.0517245370370397NA
5116.22NANA0.581053240740726NA
6116.63NANA1.00521990740740NA
7116.84116.264942129630115.2995833333330.9653587962962970.575057870370372
8116.63116.654525462963115.988750.665775462962966-0.0245254629629699
9117.03116.831747685185116.5795833333330.2521643518518570.198252314814809
10117117.048275462963117.0304166666670.0178587962962966-0.0482754629629625
11117.14116.994942129630117.4025-0.4075578703703620.145057870370380
12116.64116.907581018519117.7675-0.859918981481479-0.267581018518513
13117.24117.386331018519118.097083333333-0.71075231481482-0.146331018518509
14117.52117.784525462963118.4325-0.647974537037033-0.264525462962965
15117.83117.933831018519118.743333333333-0.809502314814815-0.103831018518505
16119.79118.999525462963119.05125-0.05172453703703970.79047453703707
17120.86119.958969907407119.3779166666670.5810532407407260.90103009259262
18120.75120.678969907407119.673751.005219907407400.0710300925926077
19120.63120.933692129630119.9683333333330.965358796296297-0.303692129629624
20120.89120.934108796296120.2683333333330.665775462962966-0.0441087962962854
21120.23120.821331018519120.5691666666670.252164351851857-0.591331018518503
22121.19120.831192129630120.8133333333330.01785879629629660.358807870370384
23120.79120.626608796296121.034166666667-0.4075578703703620.163391203703725
24120.09120.512164351852121.372083333333-0.859918981481479-0.422164351851833
25120.86121.065914351852121.776666666667-0.71075231481482-0.205914351851845
26121.1121.527858796296122.175833333333-0.647974537037033-0.427858796296277
27121.47121.815914351852122.625416666667-0.809502314814815-0.345914351851832
28122.01123.001608796296123.053333333333-0.0517245370370397-0.991608796296276
29123.94124.010636574074123.4295833333330.581053240740726-0.0706365740740722
30125.78124.879803240741123.8745833333331.005219907407400.90019675925926
31125.31125.305775462963124.3404166666670.9653587962962970.00422453703703241
32125.79125.445775462963124.780.6657754629629660.344224537037036
33126.12125.451331018519125.1991666666670.2521643518518570.668668981481474
34125.57125.604942129630125.5870833333330.0178587962962966-0.0349421296296271
35125.44125.472858796296125.880416666667-0.407557870370362-0.0328587962962956
36126.12125.154664351852126.014583333333-0.8599189814814790.965335648148155
37126.01125.382164351852126.092916666667-0.710752314814820.627835648148164
38126.5125.532025462963126.18-0.6479745370370330.967974537037023
39126.13125.404664351852126.214166666667-0.8095023148148150.725335648148146
40126.66126.183275462963126.235-0.05172453703703970.476724537037015
41126.33126.884803240741126.303750.581053240740726-0.554803240740767
42126.61127.305636574074126.3004166666671.00521990740740-0.695636574074086
43126.36NANA0.965358796296297NA
44126.83NANA0.665775462962966NA
45125.9NANA0.252164351851857NA
46126.29NANA0.0178587962962966NA
47126.37NANA-0.407557870370362NA
48125.11NANA-0.859918981481479NA


Figures (Output of Computation)

Input Parameters & R Code

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')