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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 11 Dec 2009 09:22:46 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/11/t1260548626801oow9zg8nhcc0.htm/, Retrieved Mon, 29 Apr 2024 01:54:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66471, Retrieved Mon, 29 Apr 2024 01:54:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
- R  D      [Structural Time Series Models] [shwws9vr1] [2009-12-11 16:22:46] [d447d4b3e35da686436a520338c962fc] [Current]
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Dataseries X:
102.1
102.86
102.99
103.73
105.02
104.43
104.63
104.93
105.87
105.66
106.76
106
107.22
107.33
107.11
108.86
107.72
107.88
108.38
107.72
108.41
109.9
111.45
112.18
113.34
113.46
114.06
115.54
116.39
115.94
116.97
115.94
115.91
116.43
116.26
116.35
117.9
117.7
117.53
117.86
117.65
116.51
115.93
115.31
115




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66471&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66471&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66471&T=0

As an alternative you can also use a 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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1102.1102.1000
2102.86102.7651514570350.064210660289750.04729817221037920.811371258374927
3102.99102.9373429002850.08308504315616820.03756182997754580.189765895420029
4103.73103.5880039979620.2119516448153530.0677148641343350.954909891260168
5105.02104.8127068590190.4701987972164560.08125817212200511.65487858208892
6104.43104.5405616329290.270205063345090-0.0205258726395394-1.19559366289446
7104.63104.6134116138140.2156074385792030.0402172397717256-0.315576160168867
8104.93104.8801393986160.2299329066810370.04377900013261620.081455708723675
9105.87105.7275221684960.4040503694342760.06924931447699640.982188462873182
10105.66105.7059692363830.2836632523946290.00443160071438382-0.676473794049252
11106.76106.6091777223910.4591742087232520.07750616066761950.984369587066293
12106106.1331276809320.194036528702973-0.0224910439118621-1.48569097016466
13107.22107.1251122952670.4111947617594920.004518429295176101.45667327957439
14107.33107.3427669084530.3587216214451860.003821206714052-0.260008449393046
15107.11107.2307107163950.227692677879219-0.0680454006315661-0.739681292772364
16108.86108.6424708812810.561357593691550.08358388555261111.86501983542233
17107.72107.9338392154720.203509707592572-0.070446551532578-1.99747126935286
18107.88107.9342054686260.146308209952554-0.0312448268648332-0.319764662433646
19108.38108.3198495712430.2136862987592390.03307819896455320.376964778916922
20107.72107.8890395389100.0322513933179768-0.0961080204849495-1.01545858717818
21108.41108.2653897136300.1291240490668350.1056624496303890.542271327969
22109.9109.7143262014060.5007154489065630.03625885323724282.08026990939708
23111.45111.1936136543700.7762548362922930.1455878914989561.54261026183656
24112.18112.2522793263140.855671935194427-0.1042037934275100.444689780661887
25113.34113.2248107592440.8880899731401580.1020490691702360.196644175735931
26113.46113.5202127889150.725146290757188-0.00276096361689073-0.85787675926491
27114.06114.2448206012990.724996985899211-0.184762113724858-0.00083967605545427
28115.54115.2857356040740.8135198961723320.2193995386410740.495729607223135
29116.39116.3980292695920.897292134789788-0.04091892425522960.467859690556891
30115.94116.2515566857100.604801409303994-0.196582891669543-1.63528753513486
31116.97116.8445246464640.6014860111413430.126779821009020-0.0185506130058656
32115.94116.3004019858850.280522780961716-0.234065698926343-1.79648518578687
33115.91116.0036492990620.118780668329578-0.0299736903632424-0.90543196184608
34116.43116.3995249954280.196424933136498-9.530930384552e-050.434686592306393
35116.26116.2379255508050.09611012786667530.0615700667171395-0.561600288915193
36116.35116.4358111080180.1245715588933-0.09701172134221190.159495425282006
37117.9117.4178215199020.3631987535106210.3871266083505251.39661150407570
38117.7117.7275512459820.348419018330608-0.0221249495079620-0.0798484402436809
39117.53117.8268744287110.279472100006796-0.270231867062211-0.386690010795041
40117.86117.7732807844390.1864878730129090.122969793321466-0.521370824042164
41117.65117.6633671947820.1036385423551620.0188137010861343-0.462937653020033
42116.51116.923002540769-0.132163703558486-0.321356449630923-1.31836801891653
43115.93115.88208675318-0.3860046449974040.146662301323176-1.42032071118767
44115.31115.466804163221-0.394182837006596-0.153621309674475-0.0457758208463296
45115115.065385319931-0.396204205777226-0.0645984848540805-0.0113159082216976

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 102.1 & 102.1 & 0 & 0 & 0 \tabularnewline
2 & 102.86 & 102.765151457035 & 0.06421066028975 & 0.0472981722103792 & 0.811371258374927 \tabularnewline
3 & 102.99 & 102.937342900285 & 0.0830850431561682 & 0.0375618299775458 & 0.189765895420029 \tabularnewline
4 & 103.73 & 103.588003997962 & 0.211951644815353 & 0.067714864134335 & 0.954909891260168 \tabularnewline
5 & 105.02 & 104.812706859019 & 0.470198797216456 & 0.0812581721220051 & 1.65487858208892 \tabularnewline
6 & 104.43 & 104.540561632929 & 0.270205063345090 & -0.0205258726395394 & -1.19559366289446 \tabularnewline
7 & 104.63 & 104.613411613814 & 0.215607438579203 & 0.0402172397717256 & -0.315576160168867 \tabularnewline
8 & 104.93 & 104.880139398616 & 0.229932906681037 & 0.0437790001326162 & 0.081455708723675 \tabularnewline
9 & 105.87 & 105.727522168496 & 0.404050369434276 & 0.0692493144769964 & 0.982188462873182 \tabularnewline
10 & 105.66 & 105.705969236383 & 0.283663252394629 & 0.00443160071438382 & -0.676473794049252 \tabularnewline
11 & 106.76 & 106.609177722391 & 0.459174208723252 & 0.0775061606676195 & 0.984369587066293 \tabularnewline
12 & 106 & 106.133127680932 & 0.194036528702973 & -0.0224910439118621 & -1.48569097016466 \tabularnewline
13 & 107.22 & 107.125112295267 & 0.411194761759492 & 0.00451842929517610 & 1.45667327957439 \tabularnewline
14 & 107.33 & 107.342766908453 & 0.358721621445186 & 0.003821206714052 & -0.260008449393046 \tabularnewline
15 & 107.11 & 107.230710716395 & 0.227692677879219 & -0.0680454006315661 & -0.739681292772364 \tabularnewline
16 & 108.86 & 108.642470881281 & 0.56135759369155 & 0.0835838855526111 & 1.86501983542233 \tabularnewline
17 & 107.72 & 107.933839215472 & 0.203509707592572 & -0.070446551532578 & -1.99747126935286 \tabularnewline
18 & 107.88 & 107.934205468626 & 0.146308209952554 & -0.0312448268648332 & -0.319764662433646 \tabularnewline
19 & 108.38 & 108.319849571243 & 0.213686298759239 & 0.0330781989645532 & 0.376964778916922 \tabularnewline
20 & 107.72 & 107.889039538910 & 0.0322513933179768 & -0.0961080204849495 & -1.01545858717818 \tabularnewline
21 & 108.41 & 108.265389713630 & 0.129124049066835 & 0.105662449630389 & 0.542271327969 \tabularnewline
22 & 109.9 & 109.714326201406 & 0.500715448906563 & 0.0362588532372428 & 2.08026990939708 \tabularnewline
23 & 111.45 & 111.193613654370 & 0.776254836292293 & 0.145587891498956 & 1.54261026183656 \tabularnewline
24 & 112.18 & 112.252279326314 & 0.855671935194427 & -0.104203793427510 & 0.444689780661887 \tabularnewline
25 & 113.34 & 113.224810759244 & 0.888089973140158 & 0.102049069170236 & 0.196644175735931 \tabularnewline
26 & 113.46 & 113.520212788915 & 0.725146290757188 & -0.00276096361689073 & -0.85787675926491 \tabularnewline
27 & 114.06 & 114.244820601299 & 0.724996985899211 & -0.184762113724858 & -0.00083967605545427 \tabularnewline
28 & 115.54 & 115.285735604074 & 0.813519896172332 & 0.219399538641074 & 0.495729607223135 \tabularnewline
29 & 116.39 & 116.398029269592 & 0.897292134789788 & -0.0409189242552296 & 0.467859690556891 \tabularnewline
30 & 115.94 & 116.251556685710 & 0.604801409303994 & -0.196582891669543 & -1.63528753513486 \tabularnewline
31 & 116.97 & 116.844524646464 & 0.601486011141343 & 0.126779821009020 & -0.0185506130058656 \tabularnewline
32 & 115.94 & 116.300401985885 & 0.280522780961716 & -0.234065698926343 & -1.79648518578687 \tabularnewline
33 & 115.91 & 116.003649299062 & 0.118780668329578 & -0.0299736903632424 & -0.90543196184608 \tabularnewline
34 & 116.43 & 116.399524995428 & 0.196424933136498 & -9.530930384552e-05 & 0.434686592306393 \tabularnewline
35 & 116.26 & 116.237925550805 & 0.0961101278666753 & 0.0615700667171395 & -0.561600288915193 \tabularnewline
36 & 116.35 & 116.435811108018 & 0.1245715588933 & -0.0970117213422119 & 0.159495425282006 \tabularnewline
37 & 117.9 & 117.417821519902 & 0.363198753510621 & 0.387126608350525 & 1.39661150407570 \tabularnewline
38 & 117.7 & 117.727551245982 & 0.348419018330608 & -0.0221249495079620 & -0.0798484402436809 \tabularnewline
39 & 117.53 & 117.826874428711 & 0.279472100006796 & -0.270231867062211 & -0.386690010795041 \tabularnewline
40 & 117.86 & 117.773280784439 & 0.186487873012909 & 0.122969793321466 & -0.521370824042164 \tabularnewline
41 & 117.65 & 117.663367194782 & 0.103638542355162 & 0.0188137010861343 & -0.462937653020033 \tabularnewline
42 & 116.51 & 116.923002540769 & -0.132163703558486 & -0.321356449630923 & -1.31836801891653 \tabularnewline
43 & 115.93 & 115.88208675318 & -0.386004644997404 & 0.146662301323176 & -1.42032071118767 \tabularnewline
44 & 115.31 & 115.466804163221 & -0.394182837006596 & -0.153621309674475 & -0.0457758208463296 \tabularnewline
45 & 115 & 115.065385319931 & -0.396204205777226 & -0.0645984848540805 & -0.0113159082216976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66471&T=1

[TABLE]
[ROW][C]Structural Time Series Model[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Level[/C][C]Slope[/C][C]Seasonal[/C][C]Stand. Residuals[/C][/ROW]
[ROW][C]1[/C][C]102.1[/C][C]102.1[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]102.86[/C][C]102.765151457035[/C][C]0.06421066028975[/C][C]0.0472981722103792[/C][C]0.811371258374927[/C][/ROW]
[ROW][C]3[/C][C]102.99[/C][C]102.937342900285[/C][C]0.0830850431561682[/C][C]0.0375618299775458[/C][C]0.189765895420029[/C][/ROW]
[ROW][C]4[/C][C]103.73[/C][C]103.588003997962[/C][C]0.211951644815353[/C][C]0.067714864134335[/C][C]0.954909891260168[/C][/ROW]
[ROW][C]5[/C][C]105.02[/C][C]104.812706859019[/C][C]0.470198797216456[/C][C]0.0812581721220051[/C][C]1.65487858208892[/C][/ROW]
[ROW][C]6[/C][C]104.43[/C][C]104.540561632929[/C][C]0.270205063345090[/C][C]-0.0205258726395394[/C][C]-1.19559366289446[/C][/ROW]
[ROW][C]7[/C][C]104.63[/C][C]104.613411613814[/C][C]0.215607438579203[/C][C]0.0402172397717256[/C][C]-0.315576160168867[/C][/ROW]
[ROW][C]8[/C][C]104.93[/C][C]104.880139398616[/C][C]0.229932906681037[/C][C]0.0437790001326162[/C][C]0.081455708723675[/C][/ROW]
[ROW][C]9[/C][C]105.87[/C][C]105.727522168496[/C][C]0.404050369434276[/C][C]0.0692493144769964[/C][C]0.982188462873182[/C][/ROW]
[ROW][C]10[/C][C]105.66[/C][C]105.705969236383[/C][C]0.283663252394629[/C][C]0.00443160071438382[/C][C]-0.676473794049252[/C][/ROW]
[ROW][C]11[/C][C]106.76[/C][C]106.609177722391[/C][C]0.459174208723252[/C][C]0.0775061606676195[/C][C]0.984369587066293[/C][/ROW]
[ROW][C]12[/C][C]106[/C][C]106.133127680932[/C][C]0.194036528702973[/C][C]-0.0224910439118621[/C][C]-1.48569097016466[/C][/ROW]
[ROW][C]13[/C][C]107.22[/C][C]107.125112295267[/C][C]0.411194761759492[/C][C]0.00451842929517610[/C][C]1.45667327957439[/C][/ROW]
[ROW][C]14[/C][C]107.33[/C][C]107.342766908453[/C][C]0.358721621445186[/C][C]0.003821206714052[/C][C]-0.260008449393046[/C][/ROW]
[ROW][C]15[/C][C]107.11[/C][C]107.230710716395[/C][C]0.227692677879219[/C][C]-0.0680454006315661[/C][C]-0.739681292772364[/C][/ROW]
[ROW][C]16[/C][C]108.86[/C][C]108.642470881281[/C][C]0.56135759369155[/C][C]0.0835838855526111[/C][C]1.86501983542233[/C][/ROW]
[ROW][C]17[/C][C]107.72[/C][C]107.933839215472[/C][C]0.203509707592572[/C][C]-0.070446551532578[/C][C]-1.99747126935286[/C][/ROW]
[ROW][C]18[/C][C]107.88[/C][C]107.934205468626[/C][C]0.146308209952554[/C][C]-0.0312448268648332[/C][C]-0.319764662433646[/C][/ROW]
[ROW][C]19[/C][C]108.38[/C][C]108.319849571243[/C][C]0.213686298759239[/C][C]0.0330781989645532[/C][C]0.376964778916922[/C][/ROW]
[ROW][C]20[/C][C]107.72[/C][C]107.889039538910[/C][C]0.0322513933179768[/C][C]-0.0961080204849495[/C][C]-1.01545858717818[/C][/ROW]
[ROW][C]21[/C][C]108.41[/C][C]108.265389713630[/C][C]0.129124049066835[/C][C]0.105662449630389[/C][C]0.542271327969[/C][/ROW]
[ROW][C]22[/C][C]109.9[/C][C]109.714326201406[/C][C]0.500715448906563[/C][C]0.0362588532372428[/C][C]2.08026990939708[/C][/ROW]
[ROW][C]23[/C][C]111.45[/C][C]111.193613654370[/C][C]0.776254836292293[/C][C]0.145587891498956[/C][C]1.54261026183656[/C][/ROW]
[ROW][C]24[/C][C]112.18[/C][C]112.252279326314[/C][C]0.855671935194427[/C][C]-0.104203793427510[/C][C]0.444689780661887[/C][/ROW]
[ROW][C]25[/C][C]113.34[/C][C]113.224810759244[/C][C]0.888089973140158[/C][C]0.102049069170236[/C][C]0.196644175735931[/C][/ROW]
[ROW][C]26[/C][C]113.46[/C][C]113.520212788915[/C][C]0.725146290757188[/C][C]-0.00276096361689073[/C][C]-0.85787675926491[/C][/ROW]
[ROW][C]27[/C][C]114.06[/C][C]114.244820601299[/C][C]0.724996985899211[/C][C]-0.184762113724858[/C][C]-0.00083967605545427[/C][/ROW]
[ROW][C]28[/C][C]115.54[/C][C]115.285735604074[/C][C]0.813519896172332[/C][C]0.219399538641074[/C][C]0.495729607223135[/C][/ROW]
[ROW][C]29[/C][C]116.39[/C][C]116.398029269592[/C][C]0.897292134789788[/C][C]-0.0409189242552296[/C][C]0.467859690556891[/C][/ROW]
[ROW][C]30[/C][C]115.94[/C][C]116.251556685710[/C][C]0.604801409303994[/C][C]-0.196582891669543[/C][C]-1.63528753513486[/C][/ROW]
[ROW][C]31[/C][C]116.97[/C][C]116.844524646464[/C][C]0.601486011141343[/C][C]0.126779821009020[/C][C]-0.0185506130058656[/C][/ROW]
[ROW][C]32[/C][C]115.94[/C][C]116.300401985885[/C][C]0.280522780961716[/C][C]-0.234065698926343[/C][C]-1.79648518578687[/C][/ROW]
[ROW][C]33[/C][C]115.91[/C][C]116.003649299062[/C][C]0.118780668329578[/C][C]-0.0299736903632424[/C][C]-0.90543196184608[/C][/ROW]
[ROW][C]34[/C][C]116.43[/C][C]116.399524995428[/C][C]0.196424933136498[/C][C]-9.530930384552e-05[/C][C]0.434686592306393[/C][/ROW]
[ROW][C]35[/C][C]116.26[/C][C]116.237925550805[/C][C]0.0961101278666753[/C][C]0.0615700667171395[/C][C]-0.561600288915193[/C][/ROW]
[ROW][C]36[/C][C]116.35[/C][C]116.435811108018[/C][C]0.1245715588933[/C][C]-0.0970117213422119[/C][C]0.159495425282006[/C][/ROW]
[ROW][C]37[/C][C]117.9[/C][C]117.417821519902[/C][C]0.363198753510621[/C][C]0.387126608350525[/C][C]1.39661150407570[/C][/ROW]
[ROW][C]38[/C][C]117.7[/C][C]117.727551245982[/C][C]0.348419018330608[/C][C]-0.0221249495079620[/C][C]-0.0798484402436809[/C][/ROW]
[ROW][C]39[/C][C]117.53[/C][C]117.826874428711[/C][C]0.279472100006796[/C][C]-0.270231867062211[/C][C]-0.386690010795041[/C][/ROW]
[ROW][C]40[/C][C]117.86[/C][C]117.773280784439[/C][C]0.186487873012909[/C][C]0.122969793321466[/C][C]-0.521370824042164[/C][/ROW]
[ROW][C]41[/C][C]117.65[/C][C]117.663367194782[/C][C]0.103638542355162[/C][C]0.0188137010861343[/C][C]-0.462937653020033[/C][/ROW]
[ROW][C]42[/C][C]116.51[/C][C]116.923002540769[/C][C]-0.132163703558486[/C][C]-0.321356449630923[/C][C]-1.31836801891653[/C][/ROW]
[ROW][C]43[/C][C]115.93[/C][C]115.88208675318[/C][C]-0.386004644997404[/C][C]0.146662301323176[/C][C]-1.42032071118767[/C][/ROW]
[ROW][C]44[/C][C]115.31[/C][C]115.466804163221[/C][C]-0.394182837006596[/C][C]-0.153621309674475[/C][C]-0.0457758208463296[/C][/ROW]
[ROW][C]45[/C][C]115[/C][C]115.065385319931[/C][C]-0.396204205777226[/C][C]-0.0645984848540805[/C][C]-0.0113159082216976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66471&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66471&T=1

As an alternative you can also use a QR Code:  

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

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1102.1102.1000
2102.86102.7651514570350.064210660289750.04729817221037920.811371258374927
3102.99102.9373429002850.08308504315616820.03756182997754580.189765895420029
4103.73103.5880039979620.2119516448153530.0677148641343350.954909891260168
5105.02104.8127068590190.4701987972164560.08125817212200511.65487858208892
6104.43104.5405616329290.270205063345090-0.0205258726395394-1.19559366289446
7104.63104.6134116138140.2156074385792030.0402172397717256-0.315576160168867
8104.93104.8801393986160.2299329066810370.04377900013261620.081455708723675
9105.87105.7275221684960.4040503694342760.06924931447699640.982188462873182
10105.66105.7059692363830.2836632523946290.00443160071438382-0.676473794049252
11106.76106.6091777223910.4591742087232520.07750616066761950.984369587066293
12106106.1331276809320.194036528702973-0.0224910439118621-1.48569097016466
13107.22107.1251122952670.4111947617594920.004518429295176101.45667327957439
14107.33107.3427669084530.3587216214451860.003821206714052-0.260008449393046
15107.11107.2307107163950.227692677879219-0.0680454006315661-0.739681292772364
16108.86108.6424708812810.561357593691550.08358388555261111.86501983542233
17107.72107.9338392154720.203509707592572-0.070446551532578-1.99747126935286
18107.88107.9342054686260.146308209952554-0.0312448268648332-0.319764662433646
19108.38108.3198495712430.2136862987592390.03307819896455320.376964778916922
20107.72107.8890395389100.0322513933179768-0.0961080204849495-1.01545858717818
21108.41108.2653897136300.1291240490668350.1056624496303890.542271327969
22109.9109.7143262014060.5007154489065630.03625885323724282.08026990939708
23111.45111.1936136543700.7762548362922930.1455878914989561.54261026183656
24112.18112.2522793263140.855671935194427-0.1042037934275100.444689780661887
25113.34113.2248107592440.8880899731401580.1020490691702360.196644175735931
26113.46113.5202127889150.725146290757188-0.00276096361689073-0.85787675926491
27114.06114.2448206012990.724996985899211-0.184762113724858-0.00083967605545427
28115.54115.2857356040740.8135198961723320.2193995386410740.495729607223135
29116.39116.3980292695920.897292134789788-0.04091892425522960.467859690556891
30115.94116.2515566857100.604801409303994-0.196582891669543-1.63528753513486
31116.97116.8445246464640.6014860111413430.126779821009020-0.0185506130058656
32115.94116.3004019858850.280522780961716-0.234065698926343-1.79648518578687
33115.91116.0036492990620.118780668329578-0.0299736903632424-0.90543196184608
34116.43116.3995249954280.196424933136498-9.530930384552e-050.434686592306393
35116.26116.2379255508050.09611012786667530.0615700667171395-0.561600288915193
36116.35116.4358111080180.1245715588933-0.09701172134221190.159495425282006
37117.9117.4178215199020.3631987535106210.3871266083505251.39661150407570
38117.7117.7275512459820.348419018330608-0.0221249495079620-0.0798484402436809
39117.53117.8268744287110.279472100006796-0.270231867062211-0.386690010795041
40117.86117.7732807844390.1864878730129090.122969793321466-0.521370824042164
41117.65117.6633671947820.1036385423551620.0188137010861343-0.462937653020033
42116.51116.923002540769-0.132163703558486-0.321356449630923-1.31836801891653
43115.93115.88208675318-0.3860046449974040.146662301323176-1.42032071118767
44115.31115.466804163221-0.394182837006596-0.153621309674475-0.0457758208463296
45115115.065385319931-0.396204205777226-0.0645984848540805-0.0113159082216976



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
nx <- length(x)
x <- ts(x,frequency=par1)
m <- StructTS(x,type='BSM')
m$coef
m$fitted
m$resid
mylevel <- as.numeric(m$fitted[,'level'])
myslope <- as.numeric(m$fitted[,'slope'])
myseas <- as.numeric(m$fitted[,'sea'])
myresid <- as.numeric(m$resid)
myfit <- mylevel+myseas
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel,main='Level')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')
grid()
dev.off()
bitmap(file='test5.png')
op <- par(mfrow = c(2,2))
hist(m$resid,main='Residual Histogram')
plot(density(m$resid),main='Residual Kernel Density')
qqnorm(m$resid,main='Residual Normal QQ Plot')
qqline(m$resid)
plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Structural Time Series Model',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Level',header=TRUE)
a<-table.element(a,'Slope',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Stand. Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,mylevel[i])
a<-table.element(a,myslope[i])
a<-table.element(a,myseas[i])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')