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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 computationThu, 24 Nov 2011 10:06:37 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322147228kgfzsyzpetfhl6v.htm/, Retrieved Fri, 19 Apr 2024 13:58:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146928, Retrieved Fri, 19 Apr 2024 13:58:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92
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  [Univariate Data Series] [] [2011-11-24 14:29:18] [86f7284edee3dbb8ea5c7e2dec87d892]
- RMPD      [Structural Time Series Models] [] [2011-11-24 15:06:37] [79818163420d1233b8d9d93d595e6c9e] [Current]
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Dataseries X:
579
572
560
551
537
541
588
607
599
578
563
566
561
554
540
526
512
505
554
584
569
540
522
526
527
516
503
489
479
475
524
552
532
511
492
492
493
481
462
457
442
439
488
521
501
485
464
460
467
460
448
443
436
431
484
510
513
503
471
471




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time75 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 75 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146928&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]75 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146928&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146928&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 time75 seconds
R Server'George Udny Yule' @ yule.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1579579000
2572573.78227118693-5.36502395261287-1.78227118693004-0.580403680188635
3560561.020891519191-10.4690351756845-1.02089151919112-0.507111229394633
4551550.407714961456-10.5746212218060.592285038544122-0.0102470824093044
5537537.624441689448-12.1601416721724-0.624441689448259-0.147838492444449
6541537.436001149707-3.619433235324913.56399885029270.804388067722987
7588576.82139570378927.162754193477611.17860429621112.89717647843952
8607609.31156456282430.9787212892291-2.311564562824180.358928301259342
9599608.6014184982838.28266571490497-9.6014184982826-2.1349852817983
10578584.546794268224-14.8732248262237-6.54679426822425-2.17820156481726
11563562.343732515529-20.12149622829440.656267484471447-0.493685757713156
12566560.278059930097-7.193072039251555.721940069903161.21613130063793
13561560.321470476319-2.025124601287840.6785295236812620.487630970225853
14554555.358793568843-4.13100150319866-1.35879356884296-0.201215976134185
15540542.119698607822-10.4307456765014-2.11969860782193-0.589274346758467
16526523.34917698619-16.18059738551852.65082301381014-0.549444814242493
17512512.309301949196-12.6115514274468-0.3093019491960250.335767084725368
18505512.378826190646-3.87787595342705-7.37882619064610.819488472203607
19554539.30904061506517.328110522841214.69095938493521.99626014187738
20584575.23410176800530.15033783465848.765898231995221.20641327156394
21569578.07127870937111.3106242704455-9.07127870937059-1.77205436861136
22540552.959184520573-13.8116136810554-12.9591845205729-2.36332264380833
23522527.861957594521-21.5928080909369-5.86195759452108-0.731957995167003
24526518.63512479902-13.07568590768737.364875200979870.801444897398918
25527521.592134063021-2.031831224557145.407865936979291.04201008281554
26516515.649929479022-4.728844253867920.350070520977527-0.254072064783417
27503502.546601421248-10.44632548236460.453398578751639-0.536529910866877
28489486.388132693152-14.33428886654542.61186730684841-0.367696779576464
29479479.95159261594-8.93140187628924-0.95159261593990.509392536298835
30475489.6575367067253.78416606079326-14.6575367067251.1938034639648
31524513.30933456464117.308663422202310.69066543535911.27189894424479
32552535.24110208855420.458753074540616.75889791144570.296415992092518
33532535.0877009466486.40310665304253-3.08770094664837-1.32213558584483
34511523.889974814094-5.59954163012646-12.8899748140936-1.12907812399216
35492505.40826807151-14.3799326543555-13.4082680715101-0.825957409020663
36492490.159387809134-14.97183572398781.84061219086566-0.055715077814562
37493483.727982821278-9.150075143745159.272017178722160.548613452503316
38481476.277815521165-7.991669785206954.722184478834730.108954130784912
39462459.857730683978-13.70700307331922.14226931602165-0.536989025024042
40457452.930551602342-9.116922924436044.069448397657540.432817957930105
41442448.338888121374-6.04436953307094-6.338888121373660.289574272635316
42439457.6361157654194.36702249830156-18.63611576541890.978466942791326
43488476.82231282624814.405086144071911.17768717375220.943636888810621
44521497.36976475474618.566200097971723.63023524525350.391461334167004
45501502.5387117957089.48406729649796-1.53871179570817-0.854359678919988
46485497.234315621802-0.542456953220673-12.2343156218023-0.943164385877942
47464481.459492785245-10.8663945644042-17.4594927852446-0.971243532705321
48460462.985279004004-16.0220635968311-2.98527900400395-0.485298692417457
49467454.973248195436-10.59054399124312.02675180456420.511400129119689
50460450.089952214207-6.723174062933459.910047785792840.363646111755285
51448445.661188003657-5.172932989163712.338811996343230.145728527252624
52443439.458258251332-5.868234986854723.54174174866806-0.0654925513678355
53436444.264695763071.35044100365705-8.264695763070330.679991718824429
54431454.0078670106447.02709314111017-23.00786701064380.533807926001461
55484473.15779464242415.216354533621710.84220535757570.769838415804638
56510484.75344677197112.771354364984625.2465532280293-0.229966114076402
57513507.02181554663419.18691722395935.978184453365970.603517030384766
58503513.90013024622810.8707553946949-10.9001302462276-0.782318111235499
59471494.067492200091-9.87112884909107-23.0674922000909-1.95153713552498
60471477.296227028301-14.5332271311501-6.29622702830081-0.438795500268371

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 579 & 579 & 0 & 0 & 0 \tabularnewline
2 & 572 & 573.78227118693 & -5.36502395261287 & -1.78227118693004 & -0.580403680188635 \tabularnewline
3 & 560 & 561.020891519191 & -10.4690351756845 & -1.02089151919112 & -0.507111229394633 \tabularnewline
4 & 551 & 550.407714961456 & -10.574621221806 & 0.592285038544122 & -0.0102470824093044 \tabularnewline
5 & 537 & 537.624441689448 & -12.1601416721724 & -0.624441689448259 & -0.147838492444449 \tabularnewline
6 & 541 & 537.436001149707 & -3.61943323532491 & 3.5639988502927 & 0.804388067722987 \tabularnewline
7 & 588 & 576.821395703789 & 27.1627541934776 & 11.1786042962111 & 2.89717647843952 \tabularnewline
8 & 607 & 609.311564562824 & 30.9787212892291 & -2.31156456282418 & 0.358928301259342 \tabularnewline
9 & 599 & 608.601418498283 & 8.28266571490497 & -9.6014184982826 & -2.1349852817983 \tabularnewline
10 & 578 & 584.546794268224 & -14.8732248262237 & -6.54679426822425 & -2.17820156481726 \tabularnewline
11 & 563 & 562.343732515529 & -20.1214962282944 & 0.656267484471447 & -0.493685757713156 \tabularnewline
12 & 566 & 560.278059930097 & -7.19307203925155 & 5.72194006990316 & 1.21613130063793 \tabularnewline
13 & 561 & 560.321470476319 & -2.02512460128784 & 0.678529523681262 & 0.487630970225853 \tabularnewline
14 & 554 & 555.358793568843 & -4.13100150319866 & -1.35879356884296 & -0.201215976134185 \tabularnewline
15 & 540 & 542.119698607822 & -10.4307456765014 & -2.11969860782193 & -0.589274346758467 \tabularnewline
16 & 526 & 523.34917698619 & -16.1805973855185 & 2.65082301381014 & -0.549444814242493 \tabularnewline
17 & 512 & 512.309301949196 & -12.6115514274468 & -0.309301949196025 & 0.335767084725368 \tabularnewline
18 & 505 & 512.378826190646 & -3.87787595342705 & -7.3788261906461 & 0.819488472203607 \tabularnewline
19 & 554 & 539.309040615065 & 17.3281105228412 & 14.6909593849352 & 1.99626014187738 \tabularnewline
20 & 584 & 575.234101768005 & 30.1503378346584 & 8.76589823199522 & 1.20641327156394 \tabularnewline
21 & 569 & 578.071278709371 & 11.3106242704455 & -9.07127870937059 & -1.77205436861136 \tabularnewline
22 & 540 & 552.959184520573 & -13.8116136810554 & -12.9591845205729 & -2.36332264380833 \tabularnewline
23 & 522 & 527.861957594521 & -21.5928080909369 & -5.86195759452108 & -0.731957995167003 \tabularnewline
24 & 526 & 518.63512479902 & -13.0756859076873 & 7.36487520097987 & 0.801444897398918 \tabularnewline
25 & 527 & 521.592134063021 & -2.03183122455714 & 5.40786593697929 & 1.04201008281554 \tabularnewline
26 & 516 & 515.649929479022 & -4.72884425386792 & 0.350070520977527 & -0.254072064783417 \tabularnewline
27 & 503 & 502.546601421248 & -10.4463254823646 & 0.453398578751639 & -0.536529910866877 \tabularnewline
28 & 489 & 486.388132693152 & -14.3342888665454 & 2.61186730684841 & -0.367696779576464 \tabularnewline
29 & 479 & 479.95159261594 & -8.93140187628924 & -0.9515926159399 & 0.509392536298835 \tabularnewline
30 & 475 & 489.657536706725 & 3.78416606079326 & -14.657536706725 & 1.1938034639648 \tabularnewline
31 & 524 & 513.309334564641 & 17.3086634222023 & 10.6906654353591 & 1.27189894424479 \tabularnewline
32 & 552 & 535.241102088554 & 20.4587530745406 & 16.7588979114457 & 0.296415992092518 \tabularnewline
33 & 532 & 535.087700946648 & 6.40310665304253 & -3.08770094664837 & -1.32213558584483 \tabularnewline
34 & 511 & 523.889974814094 & -5.59954163012646 & -12.8899748140936 & -1.12907812399216 \tabularnewline
35 & 492 & 505.40826807151 & -14.3799326543555 & -13.4082680715101 & -0.825957409020663 \tabularnewline
36 & 492 & 490.159387809134 & -14.9718357239878 & 1.84061219086566 & -0.055715077814562 \tabularnewline
37 & 493 & 483.727982821278 & -9.15007514374515 & 9.27201717872216 & 0.548613452503316 \tabularnewline
38 & 481 & 476.277815521165 & -7.99166978520695 & 4.72218447883473 & 0.108954130784912 \tabularnewline
39 & 462 & 459.857730683978 & -13.7070030733192 & 2.14226931602165 & -0.536989025024042 \tabularnewline
40 & 457 & 452.930551602342 & -9.11692292443604 & 4.06944839765754 & 0.432817957930105 \tabularnewline
41 & 442 & 448.338888121374 & -6.04436953307094 & -6.33888812137366 & 0.289574272635316 \tabularnewline
42 & 439 & 457.636115765419 & 4.36702249830156 & -18.6361157654189 & 0.978466942791326 \tabularnewline
43 & 488 & 476.822312826248 & 14.4050861440719 & 11.1776871737522 & 0.943636888810621 \tabularnewline
44 & 521 & 497.369764754746 & 18.5662000979717 & 23.6302352452535 & 0.391461334167004 \tabularnewline
45 & 501 & 502.538711795708 & 9.48406729649796 & -1.53871179570817 & -0.854359678919988 \tabularnewline
46 & 485 & 497.234315621802 & -0.542456953220673 & -12.2343156218023 & -0.943164385877942 \tabularnewline
47 & 464 & 481.459492785245 & -10.8663945644042 & -17.4594927852446 & -0.971243532705321 \tabularnewline
48 & 460 & 462.985279004004 & -16.0220635968311 & -2.98527900400395 & -0.485298692417457 \tabularnewline
49 & 467 & 454.973248195436 & -10.590543991243 & 12.0267518045642 & 0.511400129119689 \tabularnewline
50 & 460 & 450.089952214207 & -6.72317406293345 & 9.91004778579284 & 0.363646111755285 \tabularnewline
51 & 448 & 445.661188003657 & -5.17293298916371 & 2.33881199634323 & 0.145728527252624 \tabularnewline
52 & 443 & 439.458258251332 & -5.86823498685472 & 3.54174174866806 & -0.0654925513678355 \tabularnewline
53 & 436 & 444.26469576307 & 1.35044100365705 & -8.26469576307033 & 0.679991718824429 \tabularnewline
54 & 431 & 454.007867010644 & 7.02709314111017 & -23.0078670106438 & 0.533807926001461 \tabularnewline
55 & 484 & 473.157794642424 & 15.2163545336217 & 10.8422053575757 & 0.769838415804638 \tabularnewline
56 & 510 & 484.753446771971 & 12.7713543649846 & 25.2465532280293 & -0.229966114076402 \tabularnewline
57 & 513 & 507.021815546634 & 19.1869172239593 & 5.97818445336597 & 0.603517030384766 \tabularnewline
58 & 503 & 513.900130246228 & 10.8707553946949 & -10.9001302462276 & -0.782318111235499 \tabularnewline
59 & 471 & 494.067492200091 & -9.87112884909107 & -23.0674922000909 & -1.95153713552498 \tabularnewline
60 & 471 & 477.296227028301 & -14.5332271311501 & -6.29622702830081 & -0.438795500268371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146928&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]579[/C][C]579[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]572[/C][C]573.78227118693[/C][C]-5.36502395261287[/C][C]-1.78227118693004[/C][C]-0.580403680188635[/C][/ROW]
[ROW][C]3[/C][C]560[/C][C]561.020891519191[/C][C]-10.4690351756845[/C][C]-1.02089151919112[/C][C]-0.507111229394633[/C][/ROW]
[ROW][C]4[/C][C]551[/C][C]550.407714961456[/C][C]-10.574621221806[/C][C]0.592285038544122[/C][C]-0.0102470824093044[/C][/ROW]
[ROW][C]5[/C][C]537[/C][C]537.624441689448[/C][C]-12.1601416721724[/C][C]-0.624441689448259[/C][C]-0.147838492444449[/C][/ROW]
[ROW][C]6[/C][C]541[/C][C]537.436001149707[/C][C]-3.61943323532491[/C][C]3.5639988502927[/C][C]0.804388067722987[/C][/ROW]
[ROW][C]7[/C][C]588[/C][C]576.821395703789[/C][C]27.1627541934776[/C][C]11.1786042962111[/C][C]2.89717647843952[/C][/ROW]
[ROW][C]8[/C][C]607[/C][C]609.311564562824[/C][C]30.9787212892291[/C][C]-2.31156456282418[/C][C]0.358928301259342[/C][/ROW]
[ROW][C]9[/C][C]599[/C][C]608.601418498283[/C][C]8.28266571490497[/C][C]-9.6014184982826[/C][C]-2.1349852817983[/C][/ROW]
[ROW][C]10[/C][C]578[/C][C]584.546794268224[/C][C]-14.8732248262237[/C][C]-6.54679426822425[/C][C]-2.17820156481726[/C][/ROW]
[ROW][C]11[/C][C]563[/C][C]562.343732515529[/C][C]-20.1214962282944[/C][C]0.656267484471447[/C][C]-0.493685757713156[/C][/ROW]
[ROW][C]12[/C][C]566[/C][C]560.278059930097[/C][C]-7.19307203925155[/C][C]5.72194006990316[/C][C]1.21613130063793[/C][/ROW]
[ROW][C]13[/C][C]561[/C][C]560.321470476319[/C][C]-2.02512460128784[/C][C]0.678529523681262[/C][C]0.487630970225853[/C][/ROW]
[ROW][C]14[/C][C]554[/C][C]555.358793568843[/C][C]-4.13100150319866[/C][C]-1.35879356884296[/C][C]-0.201215976134185[/C][/ROW]
[ROW][C]15[/C][C]540[/C][C]542.119698607822[/C][C]-10.4307456765014[/C][C]-2.11969860782193[/C][C]-0.589274346758467[/C][/ROW]
[ROW][C]16[/C][C]526[/C][C]523.34917698619[/C][C]-16.1805973855185[/C][C]2.65082301381014[/C][C]-0.549444814242493[/C][/ROW]
[ROW][C]17[/C][C]512[/C][C]512.309301949196[/C][C]-12.6115514274468[/C][C]-0.309301949196025[/C][C]0.335767084725368[/C][/ROW]
[ROW][C]18[/C][C]505[/C][C]512.378826190646[/C][C]-3.87787595342705[/C][C]-7.3788261906461[/C][C]0.819488472203607[/C][/ROW]
[ROW][C]19[/C][C]554[/C][C]539.309040615065[/C][C]17.3281105228412[/C][C]14.6909593849352[/C][C]1.99626014187738[/C][/ROW]
[ROW][C]20[/C][C]584[/C][C]575.234101768005[/C][C]30.1503378346584[/C][C]8.76589823199522[/C][C]1.20641327156394[/C][/ROW]
[ROW][C]21[/C][C]569[/C][C]578.071278709371[/C][C]11.3106242704455[/C][C]-9.07127870937059[/C][C]-1.77205436861136[/C][/ROW]
[ROW][C]22[/C][C]540[/C][C]552.959184520573[/C][C]-13.8116136810554[/C][C]-12.9591845205729[/C][C]-2.36332264380833[/C][/ROW]
[ROW][C]23[/C][C]522[/C][C]527.861957594521[/C][C]-21.5928080909369[/C][C]-5.86195759452108[/C][C]-0.731957995167003[/C][/ROW]
[ROW][C]24[/C][C]526[/C][C]518.63512479902[/C][C]-13.0756859076873[/C][C]7.36487520097987[/C][C]0.801444897398918[/C][/ROW]
[ROW][C]25[/C][C]527[/C][C]521.592134063021[/C][C]-2.03183122455714[/C][C]5.40786593697929[/C][C]1.04201008281554[/C][/ROW]
[ROW][C]26[/C][C]516[/C][C]515.649929479022[/C][C]-4.72884425386792[/C][C]0.350070520977527[/C][C]-0.254072064783417[/C][/ROW]
[ROW][C]27[/C][C]503[/C][C]502.546601421248[/C][C]-10.4463254823646[/C][C]0.453398578751639[/C][C]-0.536529910866877[/C][/ROW]
[ROW][C]28[/C][C]489[/C][C]486.388132693152[/C][C]-14.3342888665454[/C][C]2.61186730684841[/C][C]-0.367696779576464[/C][/ROW]
[ROW][C]29[/C][C]479[/C][C]479.95159261594[/C][C]-8.93140187628924[/C][C]-0.9515926159399[/C][C]0.509392536298835[/C][/ROW]
[ROW][C]30[/C][C]475[/C][C]489.657536706725[/C][C]3.78416606079326[/C][C]-14.657536706725[/C][C]1.1938034639648[/C][/ROW]
[ROW][C]31[/C][C]524[/C][C]513.309334564641[/C][C]17.3086634222023[/C][C]10.6906654353591[/C][C]1.27189894424479[/C][/ROW]
[ROW][C]32[/C][C]552[/C][C]535.241102088554[/C][C]20.4587530745406[/C][C]16.7588979114457[/C][C]0.296415992092518[/C][/ROW]
[ROW][C]33[/C][C]532[/C][C]535.087700946648[/C][C]6.40310665304253[/C][C]-3.08770094664837[/C][C]-1.32213558584483[/C][/ROW]
[ROW][C]34[/C][C]511[/C][C]523.889974814094[/C][C]-5.59954163012646[/C][C]-12.8899748140936[/C][C]-1.12907812399216[/C][/ROW]
[ROW][C]35[/C][C]492[/C][C]505.40826807151[/C][C]-14.3799326543555[/C][C]-13.4082680715101[/C][C]-0.825957409020663[/C][/ROW]
[ROW][C]36[/C][C]492[/C][C]490.159387809134[/C][C]-14.9718357239878[/C][C]1.84061219086566[/C][C]-0.055715077814562[/C][/ROW]
[ROW][C]37[/C][C]493[/C][C]483.727982821278[/C][C]-9.15007514374515[/C][C]9.27201717872216[/C][C]0.548613452503316[/C][/ROW]
[ROW][C]38[/C][C]481[/C][C]476.277815521165[/C][C]-7.99166978520695[/C][C]4.72218447883473[/C][C]0.108954130784912[/C][/ROW]
[ROW][C]39[/C][C]462[/C][C]459.857730683978[/C][C]-13.7070030733192[/C][C]2.14226931602165[/C][C]-0.536989025024042[/C][/ROW]
[ROW][C]40[/C][C]457[/C][C]452.930551602342[/C][C]-9.11692292443604[/C][C]4.06944839765754[/C][C]0.432817957930105[/C][/ROW]
[ROW][C]41[/C][C]442[/C][C]448.338888121374[/C][C]-6.04436953307094[/C][C]-6.33888812137366[/C][C]0.289574272635316[/C][/ROW]
[ROW][C]42[/C][C]439[/C][C]457.636115765419[/C][C]4.36702249830156[/C][C]-18.6361157654189[/C][C]0.978466942791326[/C][/ROW]
[ROW][C]43[/C][C]488[/C][C]476.822312826248[/C][C]14.4050861440719[/C][C]11.1776871737522[/C][C]0.943636888810621[/C][/ROW]
[ROW][C]44[/C][C]521[/C][C]497.369764754746[/C][C]18.5662000979717[/C][C]23.6302352452535[/C][C]0.391461334167004[/C][/ROW]
[ROW][C]45[/C][C]501[/C][C]502.538711795708[/C][C]9.48406729649796[/C][C]-1.53871179570817[/C][C]-0.854359678919988[/C][/ROW]
[ROW][C]46[/C][C]485[/C][C]497.234315621802[/C][C]-0.542456953220673[/C][C]-12.2343156218023[/C][C]-0.943164385877942[/C][/ROW]
[ROW][C]47[/C][C]464[/C][C]481.459492785245[/C][C]-10.8663945644042[/C][C]-17.4594927852446[/C][C]-0.971243532705321[/C][/ROW]
[ROW][C]48[/C][C]460[/C][C]462.985279004004[/C][C]-16.0220635968311[/C][C]-2.98527900400395[/C][C]-0.485298692417457[/C][/ROW]
[ROW][C]49[/C][C]467[/C][C]454.973248195436[/C][C]-10.590543991243[/C][C]12.0267518045642[/C][C]0.511400129119689[/C][/ROW]
[ROW][C]50[/C][C]460[/C][C]450.089952214207[/C][C]-6.72317406293345[/C][C]9.91004778579284[/C][C]0.363646111755285[/C][/ROW]
[ROW][C]51[/C][C]448[/C][C]445.661188003657[/C][C]-5.17293298916371[/C][C]2.33881199634323[/C][C]0.145728527252624[/C][/ROW]
[ROW][C]52[/C][C]443[/C][C]439.458258251332[/C][C]-5.86823498685472[/C][C]3.54174174866806[/C][C]-0.0654925513678355[/C][/ROW]
[ROW][C]53[/C][C]436[/C][C]444.26469576307[/C][C]1.35044100365705[/C][C]-8.26469576307033[/C][C]0.679991718824429[/C][/ROW]
[ROW][C]54[/C][C]431[/C][C]454.007867010644[/C][C]7.02709314111017[/C][C]-23.0078670106438[/C][C]0.533807926001461[/C][/ROW]
[ROW][C]55[/C][C]484[/C][C]473.157794642424[/C][C]15.2163545336217[/C][C]10.8422053575757[/C][C]0.769838415804638[/C][/ROW]
[ROW][C]56[/C][C]510[/C][C]484.753446771971[/C][C]12.7713543649846[/C][C]25.2465532280293[/C][C]-0.229966114076402[/C][/ROW]
[ROW][C]57[/C][C]513[/C][C]507.021815546634[/C][C]19.1869172239593[/C][C]5.97818445336597[/C][C]0.603517030384766[/C][/ROW]
[ROW][C]58[/C][C]503[/C][C]513.900130246228[/C][C]10.8707553946949[/C][C]-10.9001302462276[/C][C]-0.782318111235499[/C][/ROW]
[ROW][C]59[/C][C]471[/C][C]494.067492200091[/C][C]-9.87112884909107[/C][C]-23.0674922000909[/C][C]-1.95153713552498[/C][/ROW]
[ROW][C]60[/C][C]471[/C][C]477.296227028301[/C][C]-14.5332271311501[/C][C]-6.29622702830081[/C][C]-0.438795500268371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146928&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146928&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
1579579000
2572573.78227118693-5.36502395261287-1.78227118693004-0.580403680188635
3560561.020891519191-10.4690351756845-1.02089151919112-0.507111229394633
4551550.407714961456-10.5746212218060.592285038544122-0.0102470824093044
5537537.624441689448-12.1601416721724-0.624441689448259-0.147838492444449
6541537.436001149707-3.619433235324913.56399885029270.804388067722987
7588576.82139570378927.162754193477611.17860429621112.89717647843952
8607609.31156456282430.9787212892291-2.311564562824180.358928301259342
9599608.6014184982838.28266571490497-9.6014184982826-2.1349852817983
10578584.546794268224-14.8732248262237-6.54679426822425-2.17820156481726
11563562.343732515529-20.12149622829440.656267484471447-0.493685757713156
12566560.278059930097-7.193072039251555.721940069903161.21613130063793
13561560.321470476319-2.025124601287840.6785295236812620.487630970225853
14554555.358793568843-4.13100150319866-1.35879356884296-0.201215976134185
15540542.119698607822-10.4307456765014-2.11969860782193-0.589274346758467
16526523.34917698619-16.18059738551852.65082301381014-0.549444814242493
17512512.309301949196-12.6115514274468-0.3093019491960250.335767084725368
18505512.378826190646-3.87787595342705-7.37882619064610.819488472203607
19554539.30904061506517.328110522841214.69095938493521.99626014187738
20584575.23410176800530.15033783465848.765898231995221.20641327156394
21569578.07127870937111.3106242704455-9.07127870937059-1.77205436861136
22540552.959184520573-13.8116136810554-12.9591845205729-2.36332264380833
23522527.861957594521-21.5928080909369-5.86195759452108-0.731957995167003
24526518.63512479902-13.07568590768737.364875200979870.801444897398918
25527521.592134063021-2.031831224557145.407865936979291.04201008281554
26516515.649929479022-4.728844253867920.350070520977527-0.254072064783417
27503502.546601421248-10.44632548236460.453398578751639-0.536529910866877
28489486.388132693152-14.33428886654542.61186730684841-0.367696779576464
29479479.95159261594-8.93140187628924-0.95159261593990.509392536298835
30475489.6575367067253.78416606079326-14.6575367067251.1938034639648
31524513.30933456464117.308663422202310.69066543535911.27189894424479
32552535.24110208855420.458753074540616.75889791144570.296415992092518
33532535.0877009466486.40310665304253-3.08770094664837-1.32213558584483
34511523.889974814094-5.59954163012646-12.8899748140936-1.12907812399216
35492505.40826807151-14.3799326543555-13.4082680715101-0.825957409020663
36492490.159387809134-14.97183572398781.84061219086566-0.055715077814562
37493483.727982821278-9.150075143745159.272017178722160.548613452503316
38481476.277815521165-7.991669785206954.722184478834730.108954130784912
39462459.857730683978-13.70700307331922.14226931602165-0.536989025024042
40457452.930551602342-9.116922924436044.069448397657540.432817957930105
41442448.338888121374-6.04436953307094-6.338888121373660.289574272635316
42439457.6361157654194.36702249830156-18.63611576541890.978466942791326
43488476.82231282624814.405086144071911.17768717375220.943636888810621
44521497.36976475474618.566200097971723.63023524525350.391461334167004
45501502.5387117957089.48406729649796-1.53871179570817-0.854359678919988
46485497.234315621802-0.542456953220673-12.2343156218023-0.943164385877942
47464481.459492785245-10.8663945644042-17.4594927852446-0.971243532705321
48460462.985279004004-16.0220635968311-2.98527900400395-0.485298692417457
49467454.973248195436-10.59054399124312.02675180456420.511400129119689
50460450.089952214207-6.723174062933459.910047785792840.363646111755285
51448445.661188003657-5.172932989163712.338811996343230.145728527252624
52443439.458258251332-5.868234986854723.54174174866806-0.0654925513678355
53436444.264695763071.35044100365705-8.264695763070330.679991718824429
54431454.0078670106447.02709314111017-23.00786701064380.533807926001461
55484473.15779464242415.216354533621710.84220535757570.769838415804638
56510484.75344677197112.771354364984625.2465532280293-0.229966114076402
57513507.02181554663419.18691722395935.978184453365970.603517030384766
58503513.90013024622810.8707553946949-10.9001302462276-0.782318111235499
59471494.067492200091-9.87112884909107-23.0674922000909-1.95153713552498
60471477.296227028301-14.5332271311501-6.29622702830081-0.438795500268371



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')