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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, 04 Dec 2009 08:06:07 -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/04/t1259939288pkptiqcnht5im2l.htm/, Retrieved Sun, 28 Apr 2024 08:48:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63717, Retrieved Sun, 28 Apr 2024 08:48:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKVN WS9
Estimated Impact85
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]
-    D      [Structural Time Series Models] [WS9 Ad Hoc Foreca...] [2009-12-04 15:06:07] [f1100e00818182135823a11ccbd0f3b9] [Current]
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Dataseries X:
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556




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=63717&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=63717&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63717&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
194879487000
287009410.6622196060514.8108814183293-642.752915342371-2.62458917471795
396279348.402960950178.02078819678196293.120717707555-0.664671325135038
489479192.92784714829-8.50367448345273-221.564622870867-1.26282999082043
592839172.06254172394-9.6570099828324113.156136112896-0.112903261111508
688299078.49383991027-16.3160406007255-231.291809831184-0.889906262034115
799479246.42952599754-4.29398424098241655.5955399677762.13703771174858
896289394.13197568563.88784536467991194.2867152915421.84896929418436
993189434.33417008785.54722596344084-126.1184829002540.452657917888549
1096059488.818976054087.52115489412555102.7603094078760.617558702801716
1186409323.678938419861.14200281874054-635.897744727849-2.19217046498565
1292149234.35550439917-2.006051657110584.79386592307969-1.15197052002735
1395679230.45376913785-2.00196394364269337.109387507222-0.0260403334206382
1485479186.98962196303-2.07779769166592-627.844170684221-0.562301320083703
1591859074.75238580067-4.15498867485569139.693470646838-1.37964439611391
1694709172.04499784956-0.88651791322982273.0174580445501.19150265866024
1791239181.4710330038-0.485173195438105-60.92722207415480.119021321536663
1892789308.785959607064.68018419285406-61.56605566809411.49787747031796
19101709429.393461030969.16153687268503711.8719070066241.39504374988515
2094349435.22592344819.04431862735718-0.376278508238181-0.0410484120550867
2196559501.9218270196510.8372983815244138.0190824817540.723823830116898
2294299452.069189359729.21038879562885-6.9508097652243-0.771232255723555
2387399401.89118088077.87844906618502-646.931275747359-0.760751721349243
2495529410.675953459547.89468763914617141.0781806572590.0116841337970853
2596879395.032115845667.5793584161868298.412950089261-0.305638803709976
2690199444.029421646488.11940645079544-436.3186831776150.5353335057332
2796729514.8304253919.12571451795844140.4158756043060.796654602451242
2892069441.2378033437.46306392857916-213.647486248494-1.03183614132802
2990699385.909721883355.99446764474676-300.778691902143-0.774889456941147
3097889491.082044045728.4899684483007271.5283783020551.22357523621751
31103129554.949888226349.89540179112183742.7847143014010.687999759794703
32101059698.5228161187413.1495405034614371.6654290402151.67702598767539
3398639741.861121915713.8276660028135113.1871631861650.382258350719207
3496569735.3138163336813.4174294774220-73.8942293904185-0.259921477184466
3592959782.6197439868214.0178772153166-496.7022445268050.434673243278042
3699469811.360306505514.2471905854922130.6732111379780.189519212212617
3797019751.4937820990613.2010167130039-30.4799533687047-0.955296321157828
3890499684.3655051630312.0932150878748-613.716658893921-1.03318116150092
39101909732.8140189327612.6213954112782447.4485736492550.46510085549231
4097069802.9946510796713.5334303953238-112.2899741402240.731760978539261
4197659921.0810820301815.3286554513203-183.6889484390681.32326995921778
4298939929.7152891841915.2076123486711-34.9521925049193-0.0846274532953867
4399949820.5747931766312.9225934420333206.216609388381-1.57492203231377
44104339843.6123694098513.1051449576991586.7095529227660.128594746605431
45100739880.1427499781713.5085258476598186.6230459362730.299105764645484
46101129964.7120289185514.6518360874417128.2802660606800.910962070429564
4792669950.6898988655714.2251414897267-676.987541670659-0.3687439599434
4898209881.3358665017313.0701928574185-38.8190975296027-1.07702120683888
49100979910.4921328930913.2808037652791182.1685879958940.207439205435006
5091159905.3767510741213.0450385086054-785.417047391779-0.237059055135468
51104119938.632240280913.3070737087724466.9305017079910.259948829123201
5296789935.92312302713.0925556965901-253.626582992084-0.205530061161791
531040810066.419187814714.7240773602836310.1643860972271.50389692829314
541015310125.660740745515.360019252428115.44162861629780.569875235783153
551036810166.002402101315.7206339303438195.3191407457440.319984469395506
561058110157.491548157915.3740444908598429.996697332405-0.310858783965662
571059710218.692954471416.011928202627366.0072595066590.589101515682277
581068010303.774591882316.9350992352333357.6400706052490.889703993818281
59973810351.256764583217.3246716653247-621.4952143015560.394172004921958
60955610221.098998751915.5262153117186-625.256179984303-1.90539595655157

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 9487 & 9487 & 0 & 0 & 0 \tabularnewline
2 & 8700 & 9410.66221960605 & 14.8108814183293 & -642.752915342371 & -2.62458917471795 \tabularnewline
3 & 9627 & 9348.40296095017 & 8.02078819678196 & 293.120717707555 & -0.664671325135038 \tabularnewline
4 & 8947 & 9192.92784714829 & -8.50367448345273 & -221.564622870867 & -1.26282999082043 \tabularnewline
5 & 9283 & 9172.06254172394 & -9.6570099828324 & 113.156136112896 & -0.112903261111508 \tabularnewline
6 & 8829 & 9078.49383991027 & -16.3160406007255 & -231.291809831184 & -0.889906262034115 \tabularnewline
7 & 9947 & 9246.42952599754 & -4.29398424098241 & 655.595539967776 & 2.13703771174858 \tabularnewline
8 & 9628 & 9394.1319756856 & 3.88784536467991 & 194.286715291542 & 1.84896929418436 \tabularnewline
9 & 9318 & 9434.3341700878 & 5.54722596344084 & -126.118482900254 & 0.452657917888549 \tabularnewline
10 & 9605 & 9488.81897605408 & 7.52115489412555 & 102.760309407876 & 0.617558702801716 \tabularnewline
11 & 8640 & 9323.67893841986 & 1.14200281874054 & -635.897744727849 & -2.19217046498565 \tabularnewline
12 & 9214 & 9234.35550439917 & -2.00605165711058 & 4.79386592307969 & -1.15197052002735 \tabularnewline
13 & 9567 & 9230.45376913785 & -2.00196394364269 & 337.109387507222 & -0.0260403334206382 \tabularnewline
14 & 8547 & 9186.98962196303 & -2.07779769166592 & -627.844170684221 & -0.562301320083703 \tabularnewline
15 & 9185 & 9074.75238580067 & -4.15498867485569 & 139.693470646838 & -1.37964439611391 \tabularnewline
16 & 9470 & 9172.04499784956 & -0.88651791322982 & 273.017458044550 & 1.19150265866024 \tabularnewline
17 & 9123 & 9181.4710330038 & -0.485173195438105 & -60.9272220741548 & 0.119021321536663 \tabularnewline
18 & 9278 & 9308.78595960706 & 4.68018419285406 & -61.5660556680941 & 1.49787747031796 \tabularnewline
19 & 10170 & 9429.39346103096 & 9.16153687268503 & 711.871907006624 & 1.39504374988515 \tabularnewline
20 & 9434 & 9435.2259234481 & 9.04431862735718 & -0.376278508238181 & -0.0410484120550867 \tabularnewline
21 & 9655 & 9501.92182701965 & 10.8372983815244 & 138.019082481754 & 0.723823830116898 \tabularnewline
22 & 9429 & 9452.06918935972 & 9.21038879562885 & -6.9508097652243 & -0.771232255723555 \tabularnewline
23 & 8739 & 9401.8911808807 & 7.87844906618502 & -646.931275747359 & -0.760751721349243 \tabularnewline
24 & 9552 & 9410.67595345954 & 7.89468763914617 & 141.078180657259 & 0.0116841337970853 \tabularnewline
25 & 9687 & 9395.03211584566 & 7.5793584161868 & 298.412950089261 & -0.305638803709976 \tabularnewline
26 & 9019 & 9444.02942164648 & 8.11940645079544 & -436.318683177615 & 0.5353335057332 \tabularnewline
27 & 9672 & 9514.830425391 & 9.12571451795844 & 140.415875604306 & 0.796654602451242 \tabularnewline
28 & 9206 & 9441.237803343 & 7.46306392857916 & -213.647486248494 & -1.03183614132802 \tabularnewline
29 & 9069 & 9385.90972188335 & 5.99446764474676 & -300.778691902143 & -0.774889456941147 \tabularnewline
30 & 9788 & 9491.08204404572 & 8.4899684483007 & 271.528378302055 & 1.22357523621751 \tabularnewline
31 & 10312 & 9554.94988822634 & 9.89540179112183 & 742.784714301401 & 0.687999759794703 \tabularnewline
32 & 10105 & 9698.52281611874 & 13.1495405034614 & 371.665429040215 & 1.67702598767539 \tabularnewline
33 & 9863 & 9741.8611219157 & 13.8276660028135 & 113.187163186165 & 0.382258350719207 \tabularnewline
34 & 9656 & 9735.31381633368 & 13.4174294774220 & -73.8942293904185 & -0.259921477184466 \tabularnewline
35 & 9295 & 9782.61974398682 & 14.0178772153166 & -496.702244526805 & 0.434673243278042 \tabularnewline
36 & 9946 & 9811.3603065055 & 14.2471905854922 & 130.673211137978 & 0.189519212212617 \tabularnewline
37 & 9701 & 9751.49378209906 & 13.2010167130039 & -30.4799533687047 & -0.955296321157828 \tabularnewline
38 & 9049 & 9684.36550516303 & 12.0932150878748 & -613.716658893921 & -1.03318116150092 \tabularnewline
39 & 10190 & 9732.81401893276 & 12.6213954112782 & 447.448573649255 & 0.46510085549231 \tabularnewline
40 & 9706 & 9802.99465107967 & 13.5334303953238 & -112.289974140224 & 0.731760978539261 \tabularnewline
41 & 9765 & 9921.08108203018 & 15.3286554513203 & -183.688948439068 & 1.32326995921778 \tabularnewline
42 & 9893 & 9929.71528918419 & 15.2076123486711 & -34.9521925049193 & -0.0846274532953867 \tabularnewline
43 & 9994 & 9820.57479317663 & 12.9225934420333 & 206.216609388381 & -1.57492203231377 \tabularnewline
44 & 10433 & 9843.61236940985 & 13.1051449576991 & 586.709552922766 & 0.128594746605431 \tabularnewline
45 & 10073 & 9880.14274997817 & 13.5085258476598 & 186.623045936273 & 0.299105764645484 \tabularnewline
46 & 10112 & 9964.71202891855 & 14.6518360874417 & 128.280266060680 & 0.910962070429564 \tabularnewline
47 & 9266 & 9950.68989886557 & 14.2251414897267 & -676.987541670659 & -0.3687439599434 \tabularnewline
48 & 9820 & 9881.33586650173 & 13.0701928574185 & -38.8190975296027 & -1.07702120683888 \tabularnewline
49 & 10097 & 9910.49213289309 & 13.2808037652791 & 182.168587995894 & 0.207439205435006 \tabularnewline
50 & 9115 & 9905.37675107412 & 13.0450385086054 & -785.417047391779 & -0.237059055135468 \tabularnewline
51 & 10411 & 9938.6322402809 & 13.3070737087724 & 466.930501707991 & 0.259948829123201 \tabularnewline
52 & 9678 & 9935.923123027 & 13.0925556965901 & -253.626582992084 & -0.205530061161791 \tabularnewline
53 & 10408 & 10066.4191878147 & 14.7240773602836 & 310.164386097227 & 1.50389692829314 \tabularnewline
54 & 10153 & 10125.6607407455 & 15.3600192524281 & 15.4416286162978 & 0.569875235783153 \tabularnewline
55 & 10368 & 10166.0024021013 & 15.7206339303438 & 195.319140745744 & 0.319984469395506 \tabularnewline
56 & 10581 & 10157.4915481579 & 15.3740444908598 & 429.996697332405 & -0.310858783965662 \tabularnewline
57 & 10597 & 10218.6929544714 & 16.011928202627 & 366.007259506659 & 0.589101515682277 \tabularnewline
58 & 10680 & 10303.7745918823 & 16.9350992352333 & 357.640070605249 & 0.889703993818281 \tabularnewline
59 & 9738 & 10351.2567645832 & 17.3246716653247 & -621.495214301556 & 0.394172004921958 \tabularnewline
60 & 9556 & 10221.0989987519 & 15.5262153117186 & -625.256179984303 & -1.90539595655157 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63717&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]9487[/C][C]9487[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]8700[/C][C]9410.66221960605[/C][C]14.8108814183293[/C][C]-642.752915342371[/C][C]-2.62458917471795[/C][/ROW]
[ROW][C]3[/C][C]9627[/C][C]9348.40296095017[/C][C]8.02078819678196[/C][C]293.120717707555[/C][C]-0.664671325135038[/C][/ROW]
[ROW][C]4[/C][C]8947[/C][C]9192.92784714829[/C][C]-8.50367448345273[/C][C]-221.564622870867[/C][C]-1.26282999082043[/C][/ROW]
[ROW][C]5[/C][C]9283[/C][C]9172.06254172394[/C][C]-9.6570099828324[/C][C]113.156136112896[/C][C]-0.112903261111508[/C][/ROW]
[ROW][C]6[/C][C]8829[/C][C]9078.49383991027[/C][C]-16.3160406007255[/C][C]-231.291809831184[/C][C]-0.889906262034115[/C][/ROW]
[ROW][C]7[/C][C]9947[/C][C]9246.42952599754[/C][C]-4.29398424098241[/C][C]655.595539967776[/C][C]2.13703771174858[/C][/ROW]
[ROW][C]8[/C][C]9628[/C][C]9394.1319756856[/C][C]3.88784536467991[/C][C]194.286715291542[/C][C]1.84896929418436[/C][/ROW]
[ROW][C]9[/C][C]9318[/C][C]9434.3341700878[/C][C]5.54722596344084[/C][C]-126.118482900254[/C][C]0.452657917888549[/C][/ROW]
[ROW][C]10[/C][C]9605[/C][C]9488.81897605408[/C][C]7.52115489412555[/C][C]102.760309407876[/C][C]0.617558702801716[/C][/ROW]
[ROW][C]11[/C][C]8640[/C][C]9323.67893841986[/C][C]1.14200281874054[/C][C]-635.897744727849[/C][C]-2.19217046498565[/C][/ROW]
[ROW][C]12[/C][C]9214[/C][C]9234.35550439917[/C][C]-2.00605165711058[/C][C]4.79386592307969[/C][C]-1.15197052002735[/C][/ROW]
[ROW][C]13[/C][C]9567[/C][C]9230.45376913785[/C][C]-2.00196394364269[/C][C]337.109387507222[/C][C]-0.0260403334206382[/C][/ROW]
[ROW][C]14[/C][C]8547[/C][C]9186.98962196303[/C][C]-2.07779769166592[/C][C]-627.844170684221[/C][C]-0.562301320083703[/C][/ROW]
[ROW][C]15[/C][C]9185[/C][C]9074.75238580067[/C][C]-4.15498867485569[/C][C]139.693470646838[/C][C]-1.37964439611391[/C][/ROW]
[ROW][C]16[/C][C]9470[/C][C]9172.04499784956[/C][C]-0.88651791322982[/C][C]273.017458044550[/C][C]1.19150265866024[/C][/ROW]
[ROW][C]17[/C][C]9123[/C][C]9181.4710330038[/C][C]-0.485173195438105[/C][C]-60.9272220741548[/C][C]0.119021321536663[/C][/ROW]
[ROW][C]18[/C][C]9278[/C][C]9308.78595960706[/C][C]4.68018419285406[/C][C]-61.5660556680941[/C][C]1.49787747031796[/C][/ROW]
[ROW][C]19[/C][C]10170[/C][C]9429.39346103096[/C][C]9.16153687268503[/C][C]711.871907006624[/C][C]1.39504374988515[/C][/ROW]
[ROW][C]20[/C][C]9434[/C][C]9435.2259234481[/C][C]9.04431862735718[/C][C]-0.376278508238181[/C][C]-0.0410484120550867[/C][/ROW]
[ROW][C]21[/C][C]9655[/C][C]9501.92182701965[/C][C]10.8372983815244[/C][C]138.019082481754[/C][C]0.723823830116898[/C][/ROW]
[ROW][C]22[/C][C]9429[/C][C]9452.06918935972[/C][C]9.21038879562885[/C][C]-6.9508097652243[/C][C]-0.771232255723555[/C][/ROW]
[ROW][C]23[/C][C]8739[/C][C]9401.8911808807[/C][C]7.87844906618502[/C][C]-646.931275747359[/C][C]-0.760751721349243[/C][/ROW]
[ROW][C]24[/C][C]9552[/C][C]9410.67595345954[/C][C]7.89468763914617[/C][C]141.078180657259[/C][C]0.0116841337970853[/C][/ROW]
[ROW][C]25[/C][C]9687[/C][C]9395.03211584566[/C][C]7.5793584161868[/C][C]298.412950089261[/C][C]-0.305638803709976[/C][/ROW]
[ROW][C]26[/C][C]9019[/C][C]9444.02942164648[/C][C]8.11940645079544[/C][C]-436.318683177615[/C][C]0.5353335057332[/C][/ROW]
[ROW][C]27[/C][C]9672[/C][C]9514.830425391[/C][C]9.12571451795844[/C][C]140.415875604306[/C][C]0.796654602451242[/C][/ROW]
[ROW][C]28[/C][C]9206[/C][C]9441.237803343[/C][C]7.46306392857916[/C][C]-213.647486248494[/C][C]-1.03183614132802[/C][/ROW]
[ROW][C]29[/C][C]9069[/C][C]9385.90972188335[/C][C]5.99446764474676[/C][C]-300.778691902143[/C][C]-0.774889456941147[/C][/ROW]
[ROW][C]30[/C][C]9788[/C][C]9491.08204404572[/C][C]8.4899684483007[/C][C]271.528378302055[/C][C]1.22357523621751[/C][/ROW]
[ROW][C]31[/C][C]10312[/C][C]9554.94988822634[/C][C]9.89540179112183[/C][C]742.784714301401[/C][C]0.687999759794703[/C][/ROW]
[ROW][C]32[/C][C]10105[/C][C]9698.52281611874[/C][C]13.1495405034614[/C][C]371.665429040215[/C][C]1.67702598767539[/C][/ROW]
[ROW][C]33[/C][C]9863[/C][C]9741.8611219157[/C][C]13.8276660028135[/C][C]113.187163186165[/C][C]0.382258350719207[/C][/ROW]
[ROW][C]34[/C][C]9656[/C][C]9735.31381633368[/C][C]13.4174294774220[/C][C]-73.8942293904185[/C][C]-0.259921477184466[/C][/ROW]
[ROW][C]35[/C][C]9295[/C][C]9782.61974398682[/C][C]14.0178772153166[/C][C]-496.702244526805[/C][C]0.434673243278042[/C][/ROW]
[ROW][C]36[/C][C]9946[/C][C]9811.3603065055[/C][C]14.2471905854922[/C][C]130.673211137978[/C][C]0.189519212212617[/C][/ROW]
[ROW][C]37[/C][C]9701[/C][C]9751.49378209906[/C][C]13.2010167130039[/C][C]-30.4799533687047[/C][C]-0.955296321157828[/C][/ROW]
[ROW][C]38[/C][C]9049[/C][C]9684.36550516303[/C][C]12.0932150878748[/C][C]-613.716658893921[/C][C]-1.03318116150092[/C][/ROW]
[ROW][C]39[/C][C]10190[/C][C]9732.81401893276[/C][C]12.6213954112782[/C][C]447.448573649255[/C][C]0.46510085549231[/C][/ROW]
[ROW][C]40[/C][C]9706[/C][C]9802.99465107967[/C][C]13.5334303953238[/C][C]-112.289974140224[/C][C]0.731760978539261[/C][/ROW]
[ROW][C]41[/C][C]9765[/C][C]9921.08108203018[/C][C]15.3286554513203[/C][C]-183.688948439068[/C][C]1.32326995921778[/C][/ROW]
[ROW][C]42[/C][C]9893[/C][C]9929.71528918419[/C][C]15.2076123486711[/C][C]-34.9521925049193[/C][C]-0.0846274532953867[/C][/ROW]
[ROW][C]43[/C][C]9994[/C][C]9820.57479317663[/C][C]12.9225934420333[/C][C]206.216609388381[/C][C]-1.57492203231377[/C][/ROW]
[ROW][C]44[/C][C]10433[/C][C]9843.61236940985[/C][C]13.1051449576991[/C][C]586.709552922766[/C][C]0.128594746605431[/C][/ROW]
[ROW][C]45[/C][C]10073[/C][C]9880.14274997817[/C][C]13.5085258476598[/C][C]186.623045936273[/C][C]0.299105764645484[/C][/ROW]
[ROW][C]46[/C][C]10112[/C][C]9964.71202891855[/C][C]14.6518360874417[/C][C]128.280266060680[/C][C]0.910962070429564[/C][/ROW]
[ROW][C]47[/C][C]9266[/C][C]9950.68989886557[/C][C]14.2251414897267[/C][C]-676.987541670659[/C][C]-0.3687439599434[/C][/ROW]
[ROW][C]48[/C][C]9820[/C][C]9881.33586650173[/C][C]13.0701928574185[/C][C]-38.8190975296027[/C][C]-1.07702120683888[/C][/ROW]
[ROW][C]49[/C][C]10097[/C][C]9910.49213289309[/C][C]13.2808037652791[/C][C]182.168587995894[/C][C]0.207439205435006[/C][/ROW]
[ROW][C]50[/C][C]9115[/C][C]9905.37675107412[/C][C]13.0450385086054[/C][C]-785.417047391779[/C][C]-0.237059055135468[/C][/ROW]
[ROW][C]51[/C][C]10411[/C][C]9938.6322402809[/C][C]13.3070737087724[/C][C]466.930501707991[/C][C]0.259948829123201[/C][/ROW]
[ROW][C]52[/C][C]9678[/C][C]9935.923123027[/C][C]13.0925556965901[/C][C]-253.626582992084[/C][C]-0.205530061161791[/C][/ROW]
[ROW][C]53[/C][C]10408[/C][C]10066.4191878147[/C][C]14.7240773602836[/C][C]310.164386097227[/C][C]1.50389692829314[/C][/ROW]
[ROW][C]54[/C][C]10153[/C][C]10125.6607407455[/C][C]15.3600192524281[/C][C]15.4416286162978[/C][C]0.569875235783153[/C][/ROW]
[ROW][C]55[/C][C]10368[/C][C]10166.0024021013[/C][C]15.7206339303438[/C][C]195.319140745744[/C][C]0.319984469395506[/C][/ROW]
[ROW][C]56[/C][C]10581[/C][C]10157.4915481579[/C][C]15.3740444908598[/C][C]429.996697332405[/C][C]-0.310858783965662[/C][/ROW]
[ROW][C]57[/C][C]10597[/C][C]10218.6929544714[/C][C]16.011928202627[/C][C]366.007259506659[/C][C]0.589101515682277[/C][/ROW]
[ROW][C]58[/C][C]10680[/C][C]10303.7745918823[/C][C]16.9350992352333[/C][C]357.640070605249[/C][C]0.889703993818281[/C][/ROW]
[ROW][C]59[/C][C]9738[/C][C]10351.2567645832[/C][C]17.3246716653247[/C][C]-621.495214301556[/C][C]0.394172004921958[/C][/ROW]
[ROW][C]60[/C][C]9556[/C][C]10221.0989987519[/C][C]15.5262153117186[/C][C]-625.256179984303[/C][C]-1.90539595655157[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63717&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63717&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
194879487000
287009410.6622196060514.8108814183293-642.752915342371-2.62458917471795
396279348.402960950178.02078819678196293.120717707555-0.664671325135038
489479192.92784714829-8.50367448345273-221.564622870867-1.26282999082043
592839172.06254172394-9.6570099828324113.156136112896-0.112903261111508
688299078.49383991027-16.3160406007255-231.291809831184-0.889906262034115
799479246.42952599754-4.29398424098241655.5955399677762.13703771174858
896289394.13197568563.88784536467991194.2867152915421.84896929418436
993189434.33417008785.54722596344084-126.1184829002540.452657917888549
1096059488.818976054087.52115489412555102.7603094078760.617558702801716
1186409323.678938419861.14200281874054-635.897744727849-2.19217046498565
1292149234.35550439917-2.006051657110584.79386592307969-1.15197052002735
1395679230.45376913785-2.00196394364269337.109387507222-0.0260403334206382
1485479186.98962196303-2.07779769166592-627.844170684221-0.562301320083703
1591859074.75238580067-4.15498867485569139.693470646838-1.37964439611391
1694709172.04499784956-0.88651791322982273.0174580445501.19150265866024
1791239181.4710330038-0.485173195438105-60.92722207415480.119021321536663
1892789308.785959607064.68018419285406-61.56605566809411.49787747031796
19101709429.393461030969.16153687268503711.8719070066241.39504374988515
2094349435.22592344819.04431862735718-0.376278508238181-0.0410484120550867
2196559501.9218270196510.8372983815244138.0190824817540.723823830116898
2294299452.069189359729.21038879562885-6.9508097652243-0.771232255723555
2387399401.89118088077.87844906618502-646.931275747359-0.760751721349243
2495529410.675953459547.89468763914617141.0781806572590.0116841337970853
2596879395.032115845667.5793584161868298.412950089261-0.305638803709976
2690199444.029421646488.11940645079544-436.3186831776150.5353335057332
2796729514.8304253919.12571451795844140.4158756043060.796654602451242
2892069441.2378033437.46306392857916-213.647486248494-1.03183614132802
2990699385.909721883355.99446764474676-300.778691902143-0.774889456941147
3097889491.082044045728.4899684483007271.5283783020551.22357523621751
31103129554.949888226349.89540179112183742.7847143014010.687999759794703
32101059698.5228161187413.1495405034614371.6654290402151.67702598767539
3398639741.861121915713.8276660028135113.1871631861650.382258350719207
3496569735.3138163336813.4174294774220-73.8942293904185-0.259921477184466
3592959782.6197439868214.0178772153166-496.7022445268050.434673243278042
3699469811.360306505514.2471905854922130.6732111379780.189519212212617
3797019751.4937820990613.2010167130039-30.4799533687047-0.955296321157828
3890499684.3655051630312.0932150878748-613.716658893921-1.03318116150092
39101909732.8140189327612.6213954112782447.4485736492550.46510085549231
4097069802.9946510796713.5334303953238-112.2899741402240.731760978539261
4197659921.0810820301815.3286554513203-183.6889484390681.32326995921778
4298939929.7152891841915.2076123486711-34.9521925049193-0.0846274532953867
4399949820.5747931766312.9225934420333206.216609388381-1.57492203231377
44104339843.6123694098513.1051449576991586.7095529227660.128594746605431
45100739880.1427499781713.5085258476598186.6230459362730.299105764645484
46101129964.7120289185514.6518360874417128.2802660606800.910962070429564
4792669950.6898988655714.2251414897267-676.987541670659-0.3687439599434
4898209881.3358665017313.0701928574185-38.8190975296027-1.07702120683888
49100979910.4921328930913.2808037652791182.1685879958940.207439205435006
5091159905.3767510741213.0450385086054-785.417047391779-0.237059055135468
51104119938.632240280913.3070737087724466.9305017079910.259948829123201
5296789935.92312302713.0925556965901-253.626582992084-0.205530061161791
531040810066.419187814714.7240773602836310.1643860972271.50389692829314
541015310125.660740745515.360019252428115.44162861629780.569875235783153
551036810166.002402101315.7206339303438195.3191407457440.319984469395506
561058110157.491548157915.3740444908598429.996697332405-0.310858783965662
571059710218.692954471416.011928202627366.0072595066590.589101515682277
581068010303.774591882316.9350992352333357.6400706052490.889703993818281
59973810351.256764583217.3246716653247-621.4952143015560.394172004921958
60955610221.098998751915.5262153117186-625.256179984303-1.90539595655157



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')