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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 computationWed, 02 Dec 2009 13:16:44 -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/02/t1259785080pt1hpkhb3fe7qsi.htm/, Retrieved Sun, 28 Apr 2024 07:11:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62571, Retrieved Sun, 28 Apr 2024 07:11:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137
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]
-   PD      [Structural Time Series Models] [] [2009-12-02 20:16:44] [4672b66a35a4d755714bdcf00037725e] [Current]
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Dataseries X:
17409
11514
31514
27071
29462
26105
22397
23843
21705
18089
20764
25316
17704
15548
28029
29383
36438
32034
22679
24319
18004
17537
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698
31956
29506
34506
27165
26736
23691
18157
17328
18205
20995
17382
9367




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62571&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]2 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=62571&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62571&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11740917409000
21151412506.2913059122-244.387739386135-992.291305912165-1.02575576745848
33151425092.0483989720263.4559193206726421.951601027973.76129702035886
42707127652.2129060086302.462594822305-581.2129060086170.741875577877264
52946229050.3180734758311.562729471478411.6819265241720.355364829596963
62610527110.5237053057295.116204126061-1005.52370530575-0.729445538109279
72239723562.9421303995264.372364381204-1165.94213039951-1.24419095901657
82384323171.2353446991258.794670874886671.764655300916-0.212356228078968
92170522014.8067871875246.482159104604-309.806787187482-0.458005659444351
101808919115.2362452726218.841494314162-1026.23624527259-1.01803450880119
112076419741.6170308696222.4514355152691022.382969130420.131859719083486
122531623451.9675313528253.622583621371864.032468647171.12836445405244
131770420987.7645475884339.995907711357-3283.76454758839-0.976674646361794
141554821350.3355673948340.221565663245-5802.33556739480.00699642041598322
152802922150.6916096930350.7072674402265878.308390306970.138600590819406
162938327461.3429708618439.1512748213441921.657029138151.57609701786548
173643832980.9961770256495.5257357697073457.003822974411.64104499550511
183203432995.2004504311491.70721780234-961.200450431071-0.155627092140527
192267927228.8152803244445.706650485012-4549.81528032437-2.02217823630513
202431923951.9360236159416.94240254346367.063976384116-1.20227708216880
211800419245.3871065936374.523783314291-1241.38710659358-1.65448116236522
221753718338.7646718214363.548564422382-801.764671821442-0.413763642385118
232036619417.4341971657368.880213836674948.5658028343280.230803776638622
242278219857.9552832287369.0773686016612924.04471677130.0231366955959056
251916921581.0356239563362.788673990991-2412.035623956340.448329681305185
261380720963.9299932715356.229060112604-7156.92999327152-0.312401526011202
272974324394.844008536404.369508494355348.155991463970.95332870143285
282559125753.5233548705419.710702964736-162.5233548704710.301844145863279
292909625789.8036672149414.8294769518763306.19633278512-0.123231459848063
302648225505.0202581335407.88740658539976.97974186654-0.225707514196883
312240525465.3240553517403.974609611703-3060.32405535166-0.144413026627387
322704425071.3118895142397.0656878326751972.68811048584-0.257400474720513
331797021178.021495698358.987482530739-3208.02149569799-1.38367003912921
341873019966.7604933765345.493809491176-1236.76049337648-0.50618789646751
351968419136.3917322311337.246330197892547.608267768923-0.378656367123165
361978517904.5314534989330.7069080922561880.46854650105-0.506312571618937
371847919608.7327298361334.635187056392-1129.732729836070.445890115329529
381069819524.0990299617331.391787168035-8826.09902996174-0.133945274356414
393195624308.0969214647389.2640550203077647.903078535271.39988119559272
402950628166.3317945716439.6312560865591339.668205428361.09826822047231
413450630461.5796827833464.1203341044244044.420317216670.594305123307022
422716528128.7238796734432.466524820806-963.723879673433-0.900295610737329
432673628755.7281315107434.435342018757-2019.728131510700.0626797013560997
442369123498.5204122548379.494343463104192.479587745232-1.83369999111623
451815721292.4082253792355.157143192920-3135.40822537921-0.832689979903131
461732818991.8576227201331.757250943064-1663.85762272014-0.85453855715057
471820517557.7686653879318.433972507718647.231334612083-0.56779656990192
482099518658.8026655763323.2442763651392336.197334423660.252016929465789
491738218869.7802341722322.543282116104-1487.78023417223-0.0361841810728551
50936719849.1267170278328.428850430367-10482.12671702780.209801690485282

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 17409 & 17409 & 0 & 0 & 0 \tabularnewline
2 & 11514 & 12506.2913059122 & -244.387739386135 & -992.291305912165 & -1.02575576745848 \tabularnewline
3 & 31514 & 25092.0483989720 & 263.455919320672 & 6421.95160102797 & 3.76129702035886 \tabularnewline
4 & 27071 & 27652.2129060086 & 302.462594822305 & -581.212906008617 & 0.741875577877264 \tabularnewline
5 & 29462 & 29050.3180734758 & 311.562729471478 & 411.681926524172 & 0.355364829596963 \tabularnewline
6 & 26105 & 27110.5237053057 & 295.116204126061 & -1005.52370530575 & -0.729445538109279 \tabularnewline
7 & 22397 & 23562.9421303995 & 264.372364381204 & -1165.94213039951 & -1.24419095901657 \tabularnewline
8 & 23843 & 23171.2353446991 & 258.794670874886 & 671.764655300916 & -0.212356228078968 \tabularnewline
9 & 21705 & 22014.8067871875 & 246.482159104604 & -309.806787187482 & -0.458005659444351 \tabularnewline
10 & 18089 & 19115.2362452726 & 218.841494314162 & -1026.23624527259 & -1.01803450880119 \tabularnewline
11 & 20764 & 19741.6170308696 & 222.451435515269 & 1022.38296913042 & 0.131859719083486 \tabularnewline
12 & 25316 & 23451.9675313528 & 253.62258362137 & 1864.03246864717 & 1.12836445405244 \tabularnewline
13 & 17704 & 20987.7645475884 & 339.995907711357 & -3283.76454758839 & -0.976674646361794 \tabularnewline
14 & 15548 & 21350.3355673948 & 340.221565663245 & -5802.3355673948 & 0.00699642041598322 \tabularnewline
15 & 28029 & 22150.6916096930 & 350.707267440226 & 5878.30839030697 & 0.138600590819406 \tabularnewline
16 & 29383 & 27461.3429708618 & 439.151274821344 & 1921.65702913815 & 1.57609701786548 \tabularnewline
17 & 36438 & 32980.9961770256 & 495.525735769707 & 3457.00382297441 & 1.64104499550511 \tabularnewline
18 & 32034 & 32995.2004504311 & 491.70721780234 & -961.200450431071 & -0.155627092140527 \tabularnewline
19 & 22679 & 27228.8152803244 & 445.706650485012 & -4549.81528032437 & -2.02217823630513 \tabularnewline
20 & 24319 & 23951.9360236159 & 416.94240254346 & 367.063976384116 & -1.20227708216880 \tabularnewline
21 & 18004 & 19245.3871065936 & 374.523783314291 & -1241.38710659358 & -1.65448116236522 \tabularnewline
22 & 17537 & 18338.7646718214 & 363.548564422382 & -801.764671821442 & -0.413763642385118 \tabularnewline
23 & 20366 & 19417.4341971657 & 368.880213836674 & 948.565802834328 & 0.230803776638622 \tabularnewline
24 & 22782 & 19857.9552832287 & 369.077368601661 & 2924.0447167713 & 0.0231366955959056 \tabularnewline
25 & 19169 & 21581.0356239563 & 362.788673990991 & -2412.03562395634 & 0.448329681305185 \tabularnewline
26 & 13807 & 20963.9299932715 & 356.229060112604 & -7156.92999327152 & -0.312401526011202 \tabularnewline
27 & 29743 & 24394.844008536 & 404.36950849435 & 5348.15599146397 & 0.95332870143285 \tabularnewline
28 & 25591 & 25753.5233548705 & 419.710702964736 & -162.523354870471 & 0.301844145863279 \tabularnewline
29 & 29096 & 25789.8036672149 & 414.829476951876 & 3306.19633278512 & -0.123231459848063 \tabularnewline
30 & 26482 & 25505.0202581335 & 407.88740658539 & 976.97974186654 & -0.225707514196883 \tabularnewline
31 & 22405 & 25465.3240553517 & 403.974609611703 & -3060.32405535166 & -0.144413026627387 \tabularnewline
32 & 27044 & 25071.3118895142 & 397.065687832675 & 1972.68811048584 & -0.257400474720513 \tabularnewline
33 & 17970 & 21178.021495698 & 358.987482530739 & -3208.02149569799 & -1.38367003912921 \tabularnewline
34 & 18730 & 19966.7604933765 & 345.493809491176 & -1236.76049337648 & -0.50618789646751 \tabularnewline
35 & 19684 & 19136.3917322311 & 337.246330197892 & 547.608267768923 & -0.378656367123165 \tabularnewline
36 & 19785 & 17904.5314534989 & 330.706908092256 & 1880.46854650105 & -0.506312571618937 \tabularnewline
37 & 18479 & 19608.7327298361 & 334.635187056392 & -1129.73272983607 & 0.445890115329529 \tabularnewline
38 & 10698 & 19524.0990299617 & 331.391787168035 & -8826.09902996174 & -0.133945274356414 \tabularnewline
39 & 31956 & 24308.0969214647 & 389.264055020307 & 7647.90307853527 & 1.39988119559272 \tabularnewline
40 & 29506 & 28166.3317945716 & 439.631256086559 & 1339.66820542836 & 1.09826822047231 \tabularnewline
41 & 34506 & 30461.5796827833 & 464.120334104424 & 4044.42031721667 & 0.594305123307022 \tabularnewline
42 & 27165 & 28128.7238796734 & 432.466524820806 & -963.723879673433 & -0.900295610737329 \tabularnewline
43 & 26736 & 28755.7281315107 & 434.435342018757 & -2019.72813151070 & 0.0626797013560997 \tabularnewline
44 & 23691 & 23498.5204122548 & 379.494343463104 & 192.479587745232 & -1.83369999111623 \tabularnewline
45 & 18157 & 21292.4082253792 & 355.157143192920 & -3135.40822537921 & -0.832689979903131 \tabularnewline
46 & 17328 & 18991.8576227201 & 331.757250943064 & -1663.85762272014 & -0.85453855715057 \tabularnewline
47 & 18205 & 17557.7686653879 & 318.433972507718 & 647.231334612083 & -0.56779656990192 \tabularnewline
48 & 20995 & 18658.8026655763 & 323.244276365139 & 2336.19733442366 & 0.252016929465789 \tabularnewline
49 & 17382 & 18869.7802341722 & 322.543282116104 & -1487.78023417223 & -0.0361841810728551 \tabularnewline
50 & 9367 & 19849.1267170278 & 328.428850430367 & -10482.1267170278 & 0.209801690485282 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62571&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]17409[/C][C]17409[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]11514[/C][C]12506.2913059122[/C][C]-244.387739386135[/C][C]-992.291305912165[/C][C]-1.02575576745848[/C][/ROW]
[ROW][C]3[/C][C]31514[/C][C]25092.0483989720[/C][C]263.455919320672[/C][C]6421.95160102797[/C][C]3.76129702035886[/C][/ROW]
[ROW][C]4[/C][C]27071[/C][C]27652.2129060086[/C][C]302.462594822305[/C][C]-581.212906008617[/C][C]0.741875577877264[/C][/ROW]
[ROW][C]5[/C][C]29462[/C][C]29050.3180734758[/C][C]311.562729471478[/C][C]411.681926524172[/C][C]0.355364829596963[/C][/ROW]
[ROW][C]6[/C][C]26105[/C][C]27110.5237053057[/C][C]295.116204126061[/C][C]-1005.52370530575[/C][C]-0.729445538109279[/C][/ROW]
[ROW][C]7[/C][C]22397[/C][C]23562.9421303995[/C][C]264.372364381204[/C][C]-1165.94213039951[/C][C]-1.24419095901657[/C][/ROW]
[ROW][C]8[/C][C]23843[/C][C]23171.2353446991[/C][C]258.794670874886[/C][C]671.764655300916[/C][C]-0.212356228078968[/C][/ROW]
[ROW][C]9[/C][C]21705[/C][C]22014.8067871875[/C][C]246.482159104604[/C][C]-309.806787187482[/C][C]-0.458005659444351[/C][/ROW]
[ROW][C]10[/C][C]18089[/C][C]19115.2362452726[/C][C]218.841494314162[/C][C]-1026.23624527259[/C][C]-1.01803450880119[/C][/ROW]
[ROW][C]11[/C][C]20764[/C][C]19741.6170308696[/C][C]222.451435515269[/C][C]1022.38296913042[/C][C]0.131859719083486[/C][/ROW]
[ROW][C]12[/C][C]25316[/C][C]23451.9675313528[/C][C]253.62258362137[/C][C]1864.03246864717[/C][C]1.12836445405244[/C][/ROW]
[ROW][C]13[/C][C]17704[/C][C]20987.7645475884[/C][C]339.995907711357[/C][C]-3283.76454758839[/C][C]-0.976674646361794[/C][/ROW]
[ROW][C]14[/C][C]15548[/C][C]21350.3355673948[/C][C]340.221565663245[/C][C]-5802.3355673948[/C][C]0.00699642041598322[/C][/ROW]
[ROW][C]15[/C][C]28029[/C][C]22150.6916096930[/C][C]350.707267440226[/C][C]5878.30839030697[/C][C]0.138600590819406[/C][/ROW]
[ROW][C]16[/C][C]29383[/C][C]27461.3429708618[/C][C]439.151274821344[/C][C]1921.65702913815[/C][C]1.57609701786548[/C][/ROW]
[ROW][C]17[/C][C]36438[/C][C]32980.9961770256[/C][C]495.525735769707[/C][C]3457.00382297441[/C][C]1.64104499550511[/C][/ROW]
[ROW][C]18[/C][C]32034[/C][C]32995.2004504311[/C][C]491.70721780234[/C][C]-961.200450431071[/C][C]-0.155627092140527[/C][/ROW]
[ROW][C]19[/C][C]22679[/C][C]27228.8152803244[/C][C]445.706650485012[/C][C]-4549.81528032437[/C][C]-2.02217823630513[/C][/ROW]
[ROW][C]20[/C][C]24319[/C][C]23951.9360236159[/C][C]416.94240254346[/C][C]367.063976384116[/C][C]-1.20227708216880[/C][/ROW]
[ROW][C]21[/C][C]18004[/C][C]19245.3871065936[/C][C]374.523783314291[/C][C]-1241.38710659358[/C][C]-1.65448116236522[/C][/ROW]
[ROW][C]22[/C][C]17537[/C][C]18338.7646718214[/C][C]363.548564422382[/C][C]-801.764671821442[/C][C]-0.413763642385118[/C][/ROW]
[ROW][C]23[/C][C]20366[/C][C]19417.4341971657[/C][C]368.880213836674[/C][C]948.565802834328[/C][C]0.230803776638622[/C][/ROW]
[ROW][C]24[/C][C]22782[/C][C]19857.9552832287[/C][C]369.077368601661[/C][C]2924.0447167713[/C][C]0.0231366955959056[/C][/ROW]
[ROW][C]25[/C][C]19169[/C][C]21581.0356239563[/C][C]362.788673990991[/C][C]-2412.03562395634[/C][C]0.448329681305185[/C][/ROW]
[ROW][C]26[/C][C]13807[/C][C]20963.9299932715[/C][C]356.229060112604[/C][C]-7156.92999327152[/C][C]-0.312401526011202[/C][/ROW]
[ROW][C]27[/C][C]29743[/C][C]24394.844008536[/C][C]404.36950849435[/C][C]5348.15599146397[/C][C]0.95332870143285[/C][/ROW]
[ROW][C]28[/C][C]25591[/C][C]25753.5233548705[/C][C]419.710702964736[/C][C]-162.523354870471[/C][C]0.301844145863279[/C][/ROW]
[ROW][C]29[/C][C]29096[/C][C]25789.8036672149[/C][C]414.829476951876[/C][C]3306.19633278512[/C][C]-0.123231459848063[/C][/ROW]
[ROW][C]30[/C][C]26482[/C][C]25505.0202581335[/C][C]407.88740658539[/C][C]976.97974186654[/C][C]-0.225707514196883[/C][/ROW]
[ROW][C]31[/C][C]22405[/C][C]25465.3240553517[/C][C]403.974609611703[/C][C]-3060.32405535166[/C][C]-0.144413026627387[/C][/ROW]
[ROW][C]32[/C][C]27044[/C][C]25071.3118895142[/C][C]397.065687832675[/C][C]1972.68811048584[/C][C]-0.257400474720513[/C][/ROW]
[ROW][C]33[/C][C]17970[/C][C]21178.021495698[/C][C]358.987482530739[/C][C]-3208.02149569799[/C][C]-1.38367003912921[/C][/ROW]
[ROW][C]34[/C][C]18730[/C][C]19966.7604933765[/C][C]345.493809491176[/C][C]-1236.76049337648[/C][C]-0.50618789646751[/C][/ROW]
[ROW][C]35[/C][C]19684[/C][C]19136.3917322311[/C][C]337.246330197892[/C][C]547.608267768923[/C][C]-0.378656367123165[/C][/ROW]
[ROW][C]36[/C][C]19785[/C][C]17904.5314534989[/C][C]330.706908092256[/C][C]1880.46854650105[/C][C]-0.506312571618937[/C][/ROW]
[ROW][C]37[/C][C]18479[/C][C]19608.7327298361[/C][C]334.635187056392[/C][C]-1129.73272983607[/C][C]0.445890115329529[/C][/ROW]
[ROW][C]38[/C][C]10698[/C][C]19524.0990299617[/C][C]331.391787168035[/C][C]-8826.09902996174[/C][C]-0.133945274356414[/C][/ROW]
[ROW][C]39[/C][C]31956[/C][C]24308.0969214647[/C][C]389.264055020307[/C][C]7647.90307853527[/C][C]1.39988119559272[/C][/ROW]
[ROW][C]40[/C][C]29506[/C][C]28166.3317945716[/C][C]439.631256086559[/C][C]1339.66820542836[/C][C]1.09826822047231[/C][/ROW]
[ROW][C]41[/C][C]34506[/C][C]30461.5796827833[/C][C]464.120334104424[/C][C]4044.42031721667[/C][C]0.594305123307022[/C][/ROW]
[ROW][C]42[/C][C]27165[/C][C]28128.7238796734[/C][C]432.466524820806[/C][C]-963.723879673433[/C][C]-0.900295610737329[/C][/ROW]
[ROW][C]43[/C][C]26736[/C][C]28755.7281315107[/C][C]434.435342018757[/C][C]-2019.72813151070[/C][C]0.0626797013560997[/C][/ROW]
[ROW][C]44[/C][C]23691[/C][C]23498.5204122548[/C][C]379.494343463104[/C][C]192.479587745232[/C][C]-1.83369999111623[/C][/ROW]
[ROW][C]45[/C][C]18157[/C][C]21292.4082253792[/C][C]355.157143192920[/C][C]-3135.40822537921[/C][C]-0.832689979903131[/C][/ROW]
[ROW][C]46[/C][C]17328[/C][C]18991.8576227201[/C][C]331.757250943064[/C][C]-1663.85762272014[/C][C]-0.85453855715057[/C][/ROW]
[ROW][C]47[/C][C]18205[/C][C]17557.7686653879[/C][C]318.433972507718[/C][C]647.231334612083[/C][C]-0.56779656990192[/C][/ROW]
[ROW][C]48[/C][C]20995[/C][C]18658.8026655763[/C][C]323.244276365139[/C][C]2336.19733442366[/C][C]0.252016929465789[/C][/ROW]
[ROW][C]49[/C][C]17382[/C][C]18869.7802341722[/C][C]322.543282116104[/C][C]-1487.78023417223[/C][C]-0.0361841810728551[/C][/ROW]
[ROW][C]50[/C][C]9367[/C][C]19849.1267170278[/C][C]328.428850430367[/C][C]-10482.1267170278[/C][C]0.209801690485282[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62571&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62571&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
11740917409000
21151412506.2913059122-244.387739386135-992.291305912165-1.02575576745848
33151425092.0483989720263.4559193206726421.951601027973.76129702035886
42707127652.2129060086302.462594822305-581.2129060086170.741875577877264
52946229050.3180734758311.562729471478411.6819265241720.355364829596963
62610527110.5237053057295.116204126061-1005.52370530575-0.729445538109279
72239723562.9421303995264.372364381204-1165.94213039951-1.24419095901657
82384323171.2353446991258.794670874886671.764655300916-0.212356228078968
92170522014.8067871875246.482159104604-309.806787187482-0.458005659444351
101808919115.2362452726218.841494314162-1026.23624527259-1.01803450880119
112076419741.6170308696222.4514355152691022.382969130420.131859719083486
122531623451.9675313528253.622583621371864.032468647171.12836445405244
131770420987.7645475884339.995907711357-3283.76454758839-0.976674646361794
141554821350.3355673948340.221565663245-5802.33556739480.00699642041598322
152802922150.6916096930350.7072674402265878.308390306970.138600590819406
162938327461.3429708618439.1512748213441921.657029138151.57609701786548
173643832980.9961770256495.5257357697073457.003822974411.64104499550511
183203432995.2004504311491.70721780234-961.200450431071-0.155627092140527
192267927228.8152803244445.706650485012-4549.81528032437-2.02217823630513
202431923951.9360236159416.94240254346367.063976384116-1.20227708216880
211800419245.3871065936374.523783314291-1241.38710659358-1.65448116236522
221753718338.7646718214363.548564422382-801.764671821442-0.413763642385118
232036619417.4341971657368.880213836674948.5658028343280.230803776638622
242278219857.9552832287369.0773686016612924.04471677130.0231366955959056
251916921581.0356239563362.788673990991-2412.035623956340.448329681305185
261380720963.9299932715356.229060112604-7156.92999327152-0.312401526011202
272974324394.844008536404.369508494355348.155991463970.95332870143285
282559125753.5233548705419.710702964736-162.5233548704710.301844145863279
292909625789.8036672149414.8294769518763306.19633278512-0.123231459848063
302648225505.0202581335407.88740658539976.97974186654-0.225707514196883
312240525465.3240553517403.974609611703-3060.32405535166-0.144413026627387
322704425071.3118895142397.0656878326751972.68811048584-0.257400474720513
331797021178.021495698358.987482530739-3208.02149569799-1.38367003912921
341873019966.7604933765345.493809491176-1236.76049337648-0.50618789646751
351968419136.3917322311337.246330197892547.608267768923-0.378656367123165
361978517904.5314534989330.7069080922561880.46854650105-0.506312571618937
371847919608.7327298361334.635187056392-1129.732729836070.445890115329529
381069819524.0990299617331.391787168035-8826.09902996174-0.133945274356414
393195624308.0969214647389.2640550203077647.903078535271.39988119559272
402950628166.3317945716439.6312560865591339.668205428361.09826822047231
413450630461.5796827833464.1203341044244044.420317216670.594305123307022
422716528128.7238796734432.466524820806-963.723879673433-0.900295610737329
432673628755.7281315107434.435342018757-2019.728131510700.0626797013560997
442369123498.5204122548379.494343463104192.479587745232-1.83369999111623
451815721292.4082253792355.157143192920-3135.40822537921-0.832689979903131
461732818991.8576227201331.757250943064-1663.85762272014-0.85453855715057
471820517557.7686653879318.433972507718647.231334612083-0.56779656990192
482099518658.8026655763323.2442763651392336.197334423660.252016929465789
491738218869.7802341722322.543282116104-1487.78023417223-0.0361841810728551
50936719849.1267170278328.428850430367-10482.12671702780.209801690485282



Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 1 ; par4 = 1 ;
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')