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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationTue, 07 Dec 2010 19:00:52 +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/07/t1291748318k2kn01gb2lrhoes.htm/, Retrieved Thu, 02 May 2024 23:12:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=106625, Retrieved Thu, 02 May 2024 23:12:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact230
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD  [Structural Time Series Models] [HPC Retail Sales] [2008-03-06 16:52:55] [74be16979710d4c4e7c6647856088456]
- R  D    [Structural Time Series Models] [HPC Retail Sales] [2008-03-08 11:33:35] [74be16979710d4c4e7c6647856088456]
-  M D        [Structural Time Series Models] [] [2010-12-07 19:00:52] [d42b17bf3b3c0d56878eb3f5a4351e6d] [Current]
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Dataseries X:
103,48
103,93
103,89
104,4
104,79
104,77
105,13
105,26
104,96
104,75
105,01
105,15
105,2
105,77
105,78
106,26
106,13
106,12
106,57
106,44
106,54
107,1
108,1
108,4
108,84
109,62
110,42
110,67
111,66
112,28
112,87
112,18
112,36
112,16
111,49
111,25
111,36
111,74
111,1
111,33
111,25
111,04
110,97
111,31
111,02
111,07
111,36
111,54
112,05
112,52
112,94
113,33
113,78
113,77
113,82
113,89
114,25
114,41




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106625&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106625&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106625&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'RServer@AstonUniversity' @ vre.aston.ac.uk







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1103.48103.48000
2103.93103.8739150119630.06715470681405720.02051636832304091.05673372968296
3103.89103.8786585982220.04411070697743920.0204037216127486-0.212745217106742
4104.4104.3288966053870.2127666431894110.0185327486458151.29441439184492
5104.79104.7464363451680.299877064783550.01780852815006330.642593675078283
6104.77104.7844231378910.1877522747086220.0183498153606676-0.820063166157854
7105.13105.0962679135760.2409937502660820.01821154529680720.388621717198104
8105.26105.252359643820.2045367784058070.0182610280463516-0.265976068843939
9104.96104.9989037092440.007804261514746360.0183989511065719-1.43510339524529
10104.75104.762163590399-0.09725813774541220.0184368095504773-0.766372861116432
11105.01104.9552520034520.02749206584565580.01841375206863290.909976860503057
12105.15105.1150358930020.08433480399747860.01840836788405310.41463227353378
13105.2105.3313719912860.138908704581269-0.1472596649199980.47449730748307
14105.77105.7289927945460.2407108068491840.01946588896619980.655989540727187
15105.78105.7839255765310.1610161284267020.0193336915853151-0.582603370340138
16106.26106.208418229860.2744541590910590.01877334418246570.822739281923393
17106.13106.1519633349330.1322563008142880.0193121816321711-1.03451463989318
18106.12106.1209361541710.06212497764874690.0194675510226062-0.511135457885316
19106.57106.5098323424190.2024848488722940.01929992431327231.02359417310278
20106.44106.4530657838420.09111153333833870.0193694853632611-0.812346035400039
21106.54106.5232466974190.0821189148544460.0193723871055168-0.0655943654371804
22107.1107.0277944311370.2636218366964430.01934228147614521.32394345266921
23108.1107.9928960232930.5650330739404430.01931663706189262.19860238118453
24108.4108.400395468510.4973429843254340.019319588531805-0.493756381673127
25108.84109.0145596143470.546622951315751-0.1889096296018560.393645454873374
26109.62109.598596461020.5618089977477490.01775162525654160.103005724369347
27110.42110.3752532700460.6540272805571820.017851564663810.673642701863089
28110.67110.6935582282240.5095826087892910.0183192319478328-1.04969404269016
29111.66111.5933666868260.6772667442444950.01790289839053631.22104152281305
30112.28112.2630415441350.6740054868698270.0179076310698452-0.0237757695965795
31112.87112.8615155739750.6415578948999440.0179330122335132-0.236647995490579
32112.18112.3110137573180.1294134307792240.0181425174185119-3.73561412849186
33112.36112.3528133664240.09176941443411120.0181504731993205-0.274586305285415
34112.16112.175509953511-0.02384452549524320.0181630331419322-0.843328331237461
35111.49111.547439850187-0.2834708804007880.0181775007011105-1.89380913622189
36111.25111.235397803403-0.2957476063763880.0181778512986996-0.0895509590826834
37111.36111.433201285786-0.086226373888755-0.1342169651096671.62498585041849
38111.74111.6881710244410.05433451304789580.01612803915960240.97392945897528
39111.1111.15732384563-0.1969112441027840.0159258200750152-1.834638742933
40111.33111.275111774956-0.06155665952911280.01559988304057330.98458133416731
41111.25111.232097947643-0.05358868588454550.01558517186662580.0580468408749427
42111.04111.041486937155-0.1124538838136940.0156486864920975-0.429209414913862
43110.97110.951540990476-0.102784088061870.01564306281967370.0705269254169159
44111.31111.2448455489010.06739400036173230.01559130524030211.24130311966288
45111.02111.03863009701-0.05016714227704380.015609777377275-0.8575273141104
46111.07111.047059169843-0.02498950755744010.01560774381040450.183654572447691
47111.36111.3085453945020.0981054107687040.01560264401698990.89789931886991
48111.54111.5113013512410.1430727762275570.01560168926681650.328008536002872
49112.05112.1180545337310.340523530686371-0.1255571396063251.50853262492872
50112.52112.5033086649860.3591121676860810.01182443919904380.130307665725181
51112.94112.9208464288690.3842011571544090.0118404745525760.183163962727882
52113.33113.3167121573560.3892171967539560.01183085965603120.0365078715921515
53113.78113.7612852235750.4130038344601830.01179590453001560.173331500971737
54113.77113.8043395076510.254065491851860.0119323906637665-1.15897875629395
55113.82113.8358667895490.1584557438899510.0119766431916989-0.697348796286899
56113.89113.8909483695180.1140403842663350.0119873938080219-0.323973999545938
57114.25114.2121045780020.2030324994683240.01197626537988140.649137096645285
58114.41114.3999268312390.1964969129287430.0119766854838765-0.0476729010209747

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 103.48 & 103.48 & 0 & 0 & 0 \tabularnewline
2 & 103.93 & 103.873915011963 & 0.0671547068140572 & 0.0205163683230409 & 1.05673372968296 \tabularnewline
3 & 103.89 & 103.878658598222 & 0.0441107069774392 & 0.0204037216127486 & -0.212745217106742 \tabularnewline
4 & 104.4 & 104.328896605387 & 0.212766643189411 & 0.018532748645815 & 1.29441439184492 \tabularnewline
5 & 104.79 & 104.746436345168 & 0.29987706478355 & 0.0178085281500633 & 0.642593675078283 \tabularnewline
6 & 104.77 & 104.784423137891 & 0.187752274708622 & 0.0183498153606676 & -0.820063166157854 \tabularnewline
7 & 105.13 & 105.096267913576 & 0.240993750266082 & 0.0182115452968072 & 0.388621717198104 \tabularnewline
8 & 105.26 & 105.25235964382 & 0.204536778405807 & 0.0182610280463516 & -0.265976068843939 \tabularnewline
9 & 104.96 & 104.998903709244 & 0.00780426151474636 & 0.0183989511065719 & -1.43510339524529 \tabularnewline
10 & 104.75 & 104.762163590399 & -0.0972581377454122 & 0.0184368095504773 & -0.766372861116432 \tabularnewline
11 & 105.01 & 104.955252003452 & 0.0274920658456558 & 0.0184137520686329 & 0.909976860503057 \tabularnewline
12 & 105.15 & 105.115035893002 & 0.0843348039974786 & 0.0184083678840531 & 0.41463227353378 \tabularnewline
13 & 105.2 & 105.331371991286 & 0.138908704581269 & -0.147259664919998 & 0.47449730748307 \tabularnewline
14 & 105.77 & 105.728992794546 & 0.240710806849184 & 0.0194658889661998 & 0.655989540727187 \tabularnewline
15 & 105.78 & 105.783925576531 & 0.161016128426702 & 0.0193336915853151 & -0.582603370340138 \tabularnewline
16 & 106.26 & 106.20841822986 & 0.274454159091059 & 0.0187733441824657 & 0.822739281923393 \tabularnewline
17 & 106.13 & 106.151963334933 & 0.132256300814288 & 0.0193121816321711 & -1.03451463989318 \tabularnewline
18 & 106.12 & 106.120936154171 & 0.0621249776487469 & 0.0194675510226062 & -0.511135457885316 \tabularnewline
19 & 106.57 & 106.509832342419 & 0.202484848872294 & 0.0192999243132723 & 1.02359417310278 \tabularnewline
20 & 106.44 & 106.453065783842 & 0.0911115333383387 & 0.0193694853632611 & -0.812346035400039 \tabularnewline
21 & 106.54 & 106.523246697419 & 0.082118914854446 & 0.0193723871055168 & -0.0655943654371804 \tabularnewline
22 & 107.1 & 107.027794431137 & 0.263621836696443 & 0.0193422814761452 & 1.32394345266921 \tabularnewline
23 & 108.1 & 107.992896023293 & 0.565033073940443 & 0.0193166370618926 & 2.19860238118453 \tabularnewline
24 & 108.4 & 108.40039546851 & 0.497342984325434 & 0.019319588531805 & -0.493756381673127 \tabularnewline
25 & 108.84 & 109.014559614347 & 0.546622951315751 & -0.188909629601856 & 0.393645454873374 \tabularnewline
26 & 109.62 & 109.59859646102 & 0.561808997747749 & 0.0177516252565416 & 0.103005724369347 \tabularnewline
27 & 110.42 & 110.375253270046 & 0.654027280557182 & 0.01785156466381 & 0.673642701863089 \tabularnewline
28 & 110.67 & 110.693558228224 & 0.509582608789291 & 0.0183192319478328 & -1.04969404269016 \tabularnewline
29 & 111.66 & 111.593366686826 & 0.677266744244495 & 0.0179028983905363 & 1.22104152281305 \tabularnewline
30 & 112.28 & 112.263041544135 & 0.674005486869827 & 0.0179076310698452 & -0.0237757695965795 \tabularnewline
31 & 112.87 & 112.861515573975 & 0.641557894899944 & 0.0179330122335132 & -0.236647995490579 \tabularnewline
32 & 112.18 & 112.311013757318 & 0.129413430779224 & 0.0181425174185119 & -3.73561412849186 \tabularnewline
33 & 112.36 & 112.352813366424 & 0.0917694144341112 & 0.0181504731993205 & -0.274586305285415 \tabularnewline
34 & 112.16 & 112.175509953511 & -0.0238445254952432 & 0.0181630331419322 & -0.843328331237461 \tabularnewline
35 & 111.49 & 111.547439850187 & -0.283470880400788 & 0.0181775007011105 & -1.89380913622189 \tabularnewline
36 & 111.25 & 111.235397803403 & -0.295747606376388 & 0.0181778512986996 & -0.0895509590826834 \tabularnewline
37 & 111.36 & 111.433201285786 & -0.086226373888755 & -0.134216965109667 & 1.62498585041849 \tabularnewline
38 & 111.74 & 111.688171024441 & 0.0543345130478958 & 0.0161280391596024 & 0.97392945897528 \tabularnewline
39 & 111.1 & 111.15732384563 & -0.196911244102784 & 0.0159258200750152 & -1.834638742933 \tabularnewline
40 & 111.33 & 111.275111774956 & -0.0615566595291128 & 0.0155998830405733 & 0.98458133416731 \tabularnewline
41 & 111.25 & 111.232097947643 & -0.0535886858845455 & 0.0155851718666258 & 0.0580468408749427 \tabularnewline
42 & 111.04 & 111.041486937155 & -0.112453883813694 & 0.0156486864920975 & -0.429209414913862 \tabularnewline
43 & 110.97 & 110.951540990476 & -0.10278408806187 & 0.0156430628196737 & 0.0705269254169159 \tabularnewline
44 & 111.31 & 111.244845548901 & 0.0673940003617323 & 0.0155913052403021 & 1.24130311966288 \tabularnewline
45 & 111.02 & 111.03863009701 & -0.0501671422770438 & 0.015609777377275 & -0.8575273141104 \tabularnewline
46 & 111.07 & 111.047059169843 & -0.0249895075574401 & 0.0156077438104045 & 0.183654572447691 \tabularnewline
47 & 111.36 & 111.308545394502 & 0.098105410768704 & 0.0156026440169899 & 0.89789931886991 \tabularnewline
48 & 111.54 & 111.511301351241 & 0.143072776227557 & 0.0156016892668165 & 0.328008536002872 \tabularnewline
49 & 112.05 & 112.118054533731 & 0.340523530686371 & -0.125557139606325 & 1.50853262492872 \tabularnewline
50 & 112.52 & 112.503308664986 & 0.359112167686081 & 0.0118244391990438 & 0.130307665725181 \tabularnewline
51 & 112.94 & 112.920846428869 & 0.384201157154409 & 0.011840474552576 & 0.183163962727882 \tabularnewline
52 & 113.33 & 113.316712157356 & 0.389217196753956 & 0.0118308596560312 & 0.0365078715921515 \tabularnewline
53 & 113.78 & 113.761285223575 & 0.413003834460183 & 0.0117959045300156 & 0.173331500971737 \tabularnewline
54 & 113.77 & 113.804339507651 & 0.25406549185186 & 0.0119323906637665 & -1.15897875629395 \tabularnewline
55 & 113.82 & 113.835866789549 & 0.158455743889951 & 0.0119766431916989 & -0.697348796286899 \tabularnewline
56 & 113.89 & 113.890948369518 & 0.114040384266335 & 0.0119873938080219 & -0.323973999545938 \tabularnewline
57 & 114.25 & 114.212104578002 & 0.203032499468324 & 0.0119762653798814 & 0.649137096645285 \tabularnewline
58 & 114.41 & 114.399926831239 & 0.196496912928743 & 0.0119766854838765 & -0.0476729010209747 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106625&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]103.48[/C][C]103.48[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]103.93[/C][C]103.873915011963[/C][C]0.0671547068140572[/C][C]0.0205163683230409[/C][C]1.05673372968296[/C][/ROW]
[ROW][C]3[/C][C]103.89[/C][C]103.878658598222[/C][C]0.0441107069774392[/C][C]0.0204037216127486[/C][C]-0.212745217106742[/C][/ROW]
[ROW][C]4[/C][C]104.4[/C][C]104.328896605387[/C][C]0.212766643189411[/C][C]0.018532748645815[/C][C]1.29441439184492[/C][/ROW]
[ROW][C]5[/C][C]104.79[/C][C]104.746436345168[/C][C]0.29987706478355[/C][C]0.0178085281500633[/C][C]0.642593675078283[/C][/ROW]
[ROW][C]6[/C][C]104.77[/C][C]104.784423137891[/C][C]0.187752274708622[/C][C]0.0183498153606676[/C][C]-0.820063166157854[/C][/ROW]
[ROW][C]7[/C][C]105.13[/C][C]105.096267913576[/C][C]0.240993750266082[/C][C]0.0182115452968072[/C][C]0.388621717198104[/C][/ROW]
[ROW][C]8[/C][C]105.26[/C][C]105.25235964382[/C][C]0.204536778405807[/C][C]0.0182610280463516[/C][C]-0.265976068843939[/C][/ROW]
[ROW][C]9[/C][C]104.96[/C][C]104.998903709244[/C][C]0.00780426151474636[/C][C]0.0183989511065719[/C][C]-1.43510339524529[/C][/ROW]
[ROW][C]10[/C][C]104.75[/C][C]104.762163590399[/C][C]-0.0972581377454122[/C][C]0.0184368095504773[/C][C]-0.766372861116432[/C][/ROW]
[ROW][C]11[/C][C]105.01[/C][C]104.955252003452[/C][C]0.0274920658456558[/C][C]0.0184137520686329[/C][C]0.909976860503057[/C][/ROW]
[ROW][C]12[/C][C]105.15[/C][C]105.115035893002[/C][C]0.0843348039974786[/C][C]0.0184083678840531[/C][C]0.41463227353378[/C][/ROW]
[ROW][C]13[/C][C]105.2[/C][C]105.331371991286[/C][C]0.138908704581269[/C][C]-0.147259664919998[/C][C]0.47449730748307[/C][/ROW]
[ROW][C]14[/C][C]105.77[/C][C]105.728992794546[/C][C]0.240710806849184[/C][C]0.0194658889661998[/C][C]0.655989540727187[/C][/ROW]
[ROW][C]15[/C][C]105.78[/C][C]105.783925576531[/C][C]0.161016128426702[/C][C]0.0193336915853151[/C][C]-0.582603370340138[/C][/ROW]
[ROW][C]16[/C][C]106.26[/C][C]106.20841822986[/C][C]0.274454159091059[/C][C]0.0187733441824657[/C][C]0.822739281923393[/C][/ROW]
[ROW][C]17[/C][C]106.13[/C][C]106.151963334933[/C][C]0.132256300814288[/C][C]0.0193121816321711[/C][C]-1.03451463989318[/C][/ROW]
[ROW][C]18[/C][C]106.12[/C][C]106.120936154171[/C][C]0.0621249776487469[/C][C]0.0194675510226062[/C][C]-0.511135457885316[/C][/ROW]
[ROW][C]19[/C][C]106.57[/C][C]106.509832342419[/C][C]0.202484848872294[/C][C]0.0192999243132723[/C][C]1.02359417310278[/C][/ROW]
[ROW][C]20[/C][C]106.44[/C][C]106.453065783842[/C][C]0.0911115333383387[/C][C]0.0193694853632611[/C][C]-0.812346035400039[/C][/ROW]
[ROW][C]21[/C][C]106.54[/C][C]106.523246697419[/C][C]0.082118914854446[/C][C]0.0193723871055168[/C][C]-0.0655943654371804[/C][/ROW]
[ROW][C]22[/C][C]107.1[/C][C]107.027794431137[/C][C]0.263621836696443[/C][C]0.0193422814761452[/C][C]1.32394345266921[/C][/ROW]
[ROW][C]23[/C][C]108.1[/C][C]107.992896023293[/C][C]0.565033073940443[/C][C]0.0193166370618926[/C][C]2.19860238118453[/C][/ROW]
[ROW][C]24[/C][C]108.4[/C][C]108.40039546851[/C][C]0.497342984325434[/C][C]0.019319588531805[/C][C]-0.493756381673127[/C][/ROW]
[ROW][C]25[/C][C]108.84[/C][C]109.014559614347[/C][C]0.546622951315751[/C][C]-0.188909629601856[/C][C]0.393645454873374[/C][/ROW]
[ROW][C]26[/C][C]109.62[/C][C]109.59859646102[/C][C]0.561808997747749[/C][C]0.0177516252565416[/C][C]0.103005724369347[/C][/ROW]
[ROW][C]27[/C][C]110.42[/C][C]110.375253270046[/C][C]0.654027280557182[/C][C]0.01785156466381[/C][C]0.673642701863089[/C][/ROW]
[ROW][C]28[/C][C]110.67[/C][C]110.693558228224[/C][C]0.509582608789291[/C][C]0.0183192319478328[/C][C]-1.04969404269016[/C][/ROW]
[ROW][C]29[/C][C]111.66[/C][C]111.593366686826[/C][C]0.677266744244495[/C][C]0.0179028983905363[/C][C]1.22104152281305[/C][/ROW]
[ROW][C]30[/C][C]112.28[/C][C]112.263041544135[/C][C]0.674005486869827[/C][C]0.0179076310698452[/C][C]-0.0237757695965795[/C][/ROW]
[ROW][C]31[/C][C]112.87[/C][C]112.861515573975[/C][C]0.641557894899944[/C][C]0.0179330122335132[/C][C]-0.236647995490579[/C][/ROW]
[ROW][C]32[/C][C]112.18[/C][C]112.311013757318[/C][C]0.129413430779224[/C][C]0.0181425174185119[/C][C]-3.73561412849186[/C][/ROW]
[ROW][C]33[/C][C]112.36[/C][C]112.352813366424[/C][C]0.0917694144341112[/C][C]0.0181504731993205[/C][C]-0.274586305285415[/C][/ROW]
[ROW][C]34[/C][C]112.16[/C][C]112.175509953511[/C][C]-0.0238445254952432[/C][C]0.0181630331419322[/C][C]-0.843328331237461[/C][/ROW]
[ROW][C]35[/C][C]111.49[/C][C]111.547439850187[/C][C]-0.283470880400788[/C][C]0.0181775007011105[/C][C]-1.89380913622189[/C][/ROW]
[ROW][C]36[/C][C]111.25[/C][C]111.235397803403[/C][C]-0.295747606376388[/C][C]0.0181778512986996[/C][C]-0.0895509590826834[/C][/ROW]
[ROW][C]37[/C][C]111.36[/C][C]111.433201285786[/C][C]-0.086226373888755[/C][C]-0.134216965109667[/C][C]1.62498585041849[/C][/ROW]
[ROW][C]38[/C][C]111.74[/C][C]111.688171024441[/C][C]0.0543345130478958[/C][C]0.0161280391596024[/C][C]0.97392945897528[/C][/ROW]
[ROW][C]39[/C][C]111.1[/C][C]111.15732384563[/C][C]-0.196911244102784[/C][C]0.0159258200750152[/C][C]-1.834638742933[/C][/ROW]
[ROW][C]40[/C][C]111.33[/C][C]111.275111774956[/C][C]-0.0615566595291128[/C][C]0.0155998830405733[/C][C]0.98458133416731[/C][/ROW]
[ROW][C]41[/C][C]111.25[/C][C]111.232097947643[/C][C]-0.0535886858845455[/C][C]0.0155851718666258[/C][C]0.0580468408749427[/C][/ROW]
[ROW][C]42[/C][C]111.04[/C][C]111.041486937155[/C][C]-0.112453883813694[/C][C]0.0156486864920975[/C][C]-0.429209414913862[/C][/ROW]
[ROW][C]43[/C][C]110.97[/C][C]110.951540990476[/C][C]-0.10278408806187[/C][C]0.0156430628196737[/C][C]0.0705269254169159[/C][/ROW]
[ROW][C]44[/C][C]111.31[/C][C]111.244845548901[/C][C]0.0673940003617323[/C][C]0.0155913052403021[/C][C]1.24130311966288[/C][/ROW]
[ROW][C]45[/C][C]111.02[/C][C]111.03863009701[/C][C]-0.0501671422770438[/C][C]0.015609777377275[/C][C]-0.8575273141104[/C][/ROW]
[ROW][C]46[/C][C]111.07[/C][C]111.047059169843[/C][C]-0.0249895075574401[/C][C]0.0156077438104045[/C][C]0.183654572447691[/C][/ROW]
[ROW][C]47[/C][C]111.36[/C][C]111.308545394502[/C][C]0.098105410768704[/C][C]0.0156026440169899[/C][C]0.89789931886991[/C][/ROW]
[ROW][C]48[/C][C]111.54[/C][C]111.511301351241[/C][C]0.143072776227557[/C][C]0.0156016892668165[/C][C]0.328008536002872[/C][/ROW]
[ROW][C]49[/C][C]112.05[/C][C]112.118054533731[/C][C]0.340523530686371[/C][C]-0.125557139606325[/C][C]1.50853262492872[/C][/ROW]
[ROW][C]50[/C][C]112.52[/C][C]112.503308664986[/C][C]0.359112167686081[/C][C]0.0118244391990438[/C][C]0.130307665725181[/C][/ROW]
[ROW][C]51[/C][C]112.94[/C][C]112.920846428869[/C][C]0.384201157154409[/C][C]0.011840474552576[/C][C]0.183163962727882[/C][/ROW]
[ROW][C]52[/C][C]113.33[/C][C]113.316712157356[/C][C]0.389217196753956[/C][C]0.0118308596560312[/C][C]0.0365078715921515[/C][/ROW]
[ROW][C]53[/C][C]113.78[/C][C]113.761285223575[/C][C]0.413003834460183[/C][C]0.0117959045300156[/C][C]0.173331500971737[/C][/ROW]
[ROW][C]54[/C][C]113.77[/C][C]113.804339507651[/C][C]0.25406549185186[/C][C]0.0119323906637665[/C][C]-1.15897875629395[/C][/ROW]
[ROW][C]55[/C][C]113.82[/C][C]113.835866789549[/C][C]0.158455743889951[/C][C]0.0119766431916989[/C][C]-0.697348796286899[/C][/ROW]
[ROW][C]56[/C][C]113.89[/C][C]113.890948369518[/C][C]0.114040384266335[/C][C]0.0119873938080219[/C][C]-0.323973999545938[/C][/ROW]
[ROW][C]57[/C][C]114.25[/C][C]114.212104578002[/C][C]0.203032499468324[/C][C]0.0119762653798814[/C][C]0.649137096645285[/C][/ROW]
[ROW][C]58[/C][C]114.41[/C][C]114.399926831239[/C][C]0.196496912928743[/C][C]0.0119766854838765[/C][C]-0.0476729010209747[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106625&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106625&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
1103.48103.48000
2103.93103.8739150119630.06715470681405720.02051636832304091.05673372968296
3103.89103.8786585982220.04411070697743920.0204037216127486-0.212745217106742
4104.4104.3288966053870.2127666431894110.0185327486458151.29441439184492
5104.79104.7464363451680.299877064783550.01780852815006330.642593675078283
6104.77104.7844231378910.1877522747086220.0183498153606676-0.820063166157854
7105.13105.0962679135760.2409937502660820.01821154529680720.388621717198104
8105.26105.252359643820.2045367784058070.0182610280463516-0.265976068843939
9104.96104.9989037092440.007804261514746360.0183989511065719-1.43510339524529
10104.75104.762163590399-0.09725813774541220.0184368095504773-0.766372861116432
11105.01104.9552520034520.02749206584565580.01841375206863290.909976860503057
12105.15105.1150358930020.08433480399747860.01840836788405310.41463227353378
13105.2105.3313719912860.138908704581269-0.1472596649199980.47449730748307
14105.77105.7289927945460.2407108068491840.01946588896619980.655989540727187
15105.78105.7839255765310.1610161284267020.0193336915853151-0.582603370340138
16106.26106.208418229860.2744541590910590.01877334418246570.822739281923393
17106.13106.1519633349330.1322563008142880.0193121816321711-1.03451463989318
18106.12106.1209361541710.06212497764874690.0194675510226062-0.511135457885316
19106.57106.5098323424190.2024848488722940.01929992431327231.02359417310278
20106.44106.4530657838420.09111153333833870.0193694853632611-0.812346035400039
21106.54106.5232466974190.0821189148544460.0193723871055168-0.0655943654371804
22107.1107.0277944311370.2636218366964430.01934228147614521.32394345266921
23108.1107.9928960232930.5650330739404430.01931663706189262.19860238118453
24108.4108.400395468510.4973429843254340.019319588531805-0.493756381673127
25108.84109.0145596143470.546622951315751-0.1889096296018560.393645454873374
26109.62109.598596461020.5618089977477490.01775162525654160.103005724369347
27110.42110.3752532700460.6540272805571820.017851564663810.673642701863089
28110.67110.6935582282240.5095826087892910.0183192319478328-1.04969404269016
29111.66111.5933666868260.6772667442444950.01790289839053631.22104152281305
30112.28112.2630415441350.6740054868698270.0179076310698452-0.0237757695965795
31112.87112.8615155739750.6415578948999440.0179330122335132-0.236647995490579
32112.18112.3110137573180.1294134307792240.0181425174185119-3.73561412849186
33112.36112.3528133664240.09176941443411120.0181504731993205-0.274586305285415
34112.16112.175509953511-0.02384452549524320.0181630331419322-0.843328331237461
35111.49111.547439850187-0.2834708804007880.0181775007011105-1.89380913622189
36111.25111.235397803403-0.2957476063763880.0181778512986996-0.0895509590826834
37111.36111.433201285786-0.086226373888755-0.1342169651096671.62498585041849
38111.74111.6881710244410.05433451304789580.01612803915960240.97392945897528
39111.1111.15732384563-0.1969112441027840.0159258200750152-1.834638742933
40111.33111.275111774956-0.06155665952911280.01559988304057330.98458133416731
41111.25111.232097947643-0.05358868588454550.01558517186662580.0580468408749427
42111.04111.041486937155-0.1124538838136940.0156486864920975-0.429209414913862
43110.97110.951540990476-0.102784088061870.01564306281967370.0705269254169159
44111.31111.2448455489010.06739400036173230.01559130524030211.24130311966288
45111.02111.03863009701-0.05016714227704380.015609777377275-0.8575273141104
46111.07111.047059169843-0.02498950755744010.01560774381040450.183654572447691
47111.36111.3085453945020.0981054107687040.01560264401698990.89789931886991
48111.54111.5113013512410.1430727762275570.01560168926681650.328008536002872
49112.05112.1180545337310.340523530686371-0.1255571396063251.50853262492872
50112.52112.5033086649860.3591121676860810.01182443919904380.130307665725181
51112.94112.9208464288690.3842011571544090.0118404745525760.183163962727882
52113.33113.3167121573560.3892171967539560.01183085965603120.0365078715921515
53113.78113.7612852235750.4130038344601830.01179590453001560.173331500971737
54113.77113.8043395076510.254065491851860.0119323906637665-1.15897875629395
55113.82113.8358667895490.1584557438899510.0119766431916989-0.697348796286899
56113.89113.8909483695180.1140403842663350.0119873938080219-0.323973999545938
57114.25114.2121045780020.2030324994683240.01197626537988140.649137096645285
58114.41114.3999268312390.1964969129287430.0119766854838765-0.0476729010209747



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$resid)
myfit <- mylevel+myseas
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time')
grid()
dev.off()
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='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')