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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 06:44:57 -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/t1259934372kfxla38c20ahi4i.htm/, Retrieved Sun, 28 Apr 2024 12:49:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63506, Retrieved Sun, 28 Apr 2024 12:49:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
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] [Ad hoc techniek 3] [2009-12-04 13:44:57] [82f29a5d509ab8039aab37a0145f886d] [Current]
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Dataseries X:
562
561
555
544
537
543
594
611
613
611
594
595
591
589
584
573
567
569
621
629
628
612
595
597
593
590
580
574
573
573
620
626
620
588
566
557
561
549
532
526
511
499
555
565
542
527
510
514
517
508
493
490
469
478
528
534
518
506
502
516
528




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=63506&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=63506&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63506&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
1562562000
2561561.339634174108-0.68843476512112-0.339634174107586-0.102939394850636
3555556.30167002153-3.53750737950236-1.30167002153033-0.361798347297018
4544546.172760882981-8.09272615414227-2.17276088298104-0.590130913529773
5537536.735724180028-9.011800359879640.264275819972285-0.111732493912854
6543539.04938506565-1.377671969833023.95061493435010.937184990104925
7594580.49430860194527.588328094379213.50569139805523.56271197220082
8611613.8826297872531.5182883941516-2.882629787250270.482653410588391
9613621.98847807324715.6535592313344-8.98847807324663-1.94848619216758
10611616.1151595276531.06969186910092-5.11515952765285-1.79122630472807
11594598.374965528423-11.6711826619275-4.37496552842256-1.56482868994634
12595591.465311091863-8.446083650890353.534688908137520.39610552935425
13591589.01389056506-4.398771146735871.98610943494010.498878384092907
14589587.295628129704-2.581211875934711.704371870295750.226855833739574
15584582.462954841753-4.06266752592251.53704515824712-0.180502321925800
16573573.14714247557-7.48180281364099-0.147142475570026-0.426441453102353
17567568.395244040748-5.68477039906724-1.395244040748070.221656478094008
18569576.3039656132243.19725889977592-7.303965613224151.08661119861124
19621604.10932140727819.222673723571316.89067859272231.96869232053014
20629626.74034332003321.44659470892912.259656679967010.273316763573875
21628635.04930276418112.8657686441724-7.049302764181-1.05381569760183
22612619.824942476355-5.4820767200239-7.82494247635457-2.25358414995367
23595602.87003233706-12.9721761514677-7.87003233706008-0.91996411628555
24597593.55878515879-10.58494996858563.44121484120950.293329406862227
25593589.646999973984-6.232482735513013.353000026015760.536273166054448
26590585.735052774875-4.716935472544.264947225125170.186443412766483
27580576.128187072909-7.882543069105853.87181292709132-0.3876692107905
28574571.679609754655-5.670527667405742.320390245344910.272859198586681
29573576.3875728518371.05036947512808-3.387572851837110.82879433616291
30573588.0214805555757.8982255336608-15.02148055557500.839675465019677
31620604.2965545476513.300068268188415.70344545235000.66276192593235
32626619.48233614382514.51634069722916.517663856174650.149429548313448
33620621.400570973076.38149809770838-1.40057097307061-0.999203236606412
34588600.450234103075-11.2715551472926-12.4502341030745-2.16813123617085
35566577.750520223269-18.6492876437219-11.7505202232692-0.906194760502808
36557556.446677170891-20.3621949186740.553322829109256-0.210538900179225
37561551.744964624392-10.24909239318749.255035375607881.24420719543982
38549542.17575217975-9.810151296806336.82424782024970.0539067498620301
39532528.98393872936-11.98384938071843.01606127063958-0.266600580262813
40526524.104180887514-7.42803736922041.895819112485840.560577218702211
41511518.840730796659-6.03627972179115-7.840730796658960.171377174725085
42499517.654312954973-2.91597887486632-18.65431295497310.383108997287234
43555535.26975542731810.267376707724719.73024457268161.61746664236007
44565553.00462031741615.059046471661911.99537968258370.58841122804794
45542542.38985254972-1.42450813442845-0.389852549719771-2.02469278590887
46527534.957312465606-5.28304715631302-7.95731246560575-0.473936141250455
47510523.486083315946-9.25659062853895-13.4860833159456-0.488141262593637
48514517.587458824783-7.1002426387394-3.587458824783070.265035687803791
49517508.241128634468-8.543432235446948.75887136553227-0.177396633467984
50508499.142652389282-8.899840163434418.8573476107175-0.0437556608483276
51493490.833980783745-8.521237008640922.166019216255090.0464625792082002
52490486.328502361258-5.952676785546923.67149763874220.315796315503607
53469480.072604406743-6.14691457445949-11.0726044067432-0.0238976370997597
54478497.7028018358149.09701864089559-19.70280183581381.87259509707129
55528511.03892731471711.8118521085516.96107268528290.333178849774093
56534515.5051441169377.1118917046654018.4948558830632-0.577010952359098
57518517.4110638082193.780918392412260.588936191781156-0.409113871218731
58506514.369684157575-0.585149174345899-8.36968415757522-0.536337412367532
59502515.8005975612150.70519518619107-13.80059756121480.158541580600858
60516518.2318843868851.8102892578221-2.231884386884960.135812597659675
61528518.8688393135131.058827694703069.1311606864867-0.0923275176192655

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 562 & 562 & 0 & 0 & 0 \tabularnewline
2 & 561 & 561.339634174108 & -0.68843476512112 & -0.339634174107586 & -0.102939394850636 \tabularnewline
3 & 555 & 556.30167002153 & -3.53750737950236 & -1.30167002153033 & -0.361798347297018 \tabularnewline
4 & 544 & 546.172760882981 & -8.09272615414227 & -2.17276088298104 & -0.590130913529773 \tabularnewline
5 & 537 & 536.735724180028 & -9.01180035987964 & 0.264275819972285 & -0.111732493912854 \tabularnewline
6 & 543 & 539.04938506565 & -1.37767196983302 & 3.9506149343501 & 0.937184990104925 \tabularnewline
7 & 594 & 580.494308601945 & 27.5883280943792 & 13.5056913980552 & 3.56271197220082 \tabularnewline
8 & 611 & 613.88262978725 & 31.5182883941516 & -2.88262978725027 & 0.482653410588391 \tabularnewline
9 & 613 & 621.988478073247 & 15.6535592313344 & -8.98847807324663 & -1.94848619216758 \tabularnewline
10 & 611 & 616.115159527653 & 1.06969186910092 & -5.11515952765285 & -1.79122630472807 \tabularnewline
11 & 594 & 598.374965528423 & -11.6711826619275 & -4.37496552842256 & -1.56482868994634 \tabularnewline
12 & 595 & 591.465311091863 & -8.44608365089035 & 3.53468890813752 & 0.39610552935425 \tabularnewline
13 & 591 & 589.01389056506 & -4.39877114673587 & 1.9861094349401 & 0.498878384092907 \tabularnewline
14 & 589 & 587.295628129704 & -2.58121187593471 & 1.70437187029575 & 0.226855833739574 \tabularnewline
15 & 584 & 582.462954841753 & -4.0626675259225 & 1.53704515824712 & -0.180502321925800 \tabularnewline
16 & 573 & 573.14714247557 & -7.48180281364099 & -0.147142475570026 & -0.426441453102353 \tabularnewline
17 & 567 & 568.395244040748 & -5.68477039906724 & -1.39524404074807 & 0.221656478094008 \tabularnewline
18 & 569 & 576.303965613224 & 3.19725889977592 & -7.30396561322415 & 1.08661119861124 \tabularnewline
19 & 621 & 604.109321407278 & 19.2226737235713 & 16.8906785927223 & 1.96869232053014 \tabularnewline
20 & 629 & 626.740343320033 & 21.4465947089291 & 2.25965667996701 & 0.273316763573875 \tabularnewline
21 & 628 & 635.049302764181 & 12.8657686441724 & -7.049302764181 & -1.05381569760183 \tabularnewline
22 & 612 & 619.824942476355 & -5.4820767200239 & -7.82494247635457 & -2.25358414995367 \tabularnewline
23 & 595 & 602.87003233706 & -12.9721761514677 & -7.87003233706008 & -0.91996411628555 \tabularnewline
24 & 597 & 593.55878515879 & -10.5849499685856 & 3.4412148412095 & 0.293329406862227 \tabularnewline
25 & 593 & 589.646999973984 & -6.23248273551301 & 3.35300002601576 & 0.536273166054448 \tabularnewline
26 & 590 & 585.735052774875 & -4.71693547254 & 4.26494722512517 & 0.186443412766483 \tabularnewline
27 & 580 & 576.128187072909 & -7.88254306910585 & 3.87181292709132 & -0.3876692107905 \tabularnewline
28 & 574 & 571.679609754655 & -5.67052766740574 & 2.32039024534491 & 0.272859198586681 \tabularnewline
29 & 573 & 576.387572851837 & 1.05036947512808 & -3.38757285183711 & 0.82879433616291 \tabularnewline
30 & 573 & 588.021480555575 & 7.8982255336608 & -15.0214805555750 & 0.839675465019677 \tabularnewline
31 & 620 & 604.29655454765 & 13.3000682681884 & 15.7034454523500 & 0.66276192593235 \tabularnewline
32 & 626 & 619.482336143825 & 14.5163406972291 & 6.51766385617465 & 0.149429548313448 \tabularnewline
33 & 620 & 621.40057097307 & 6.38149809770838 & -1.40057097307061 & -0.999203236606412 \tabularnewline
34 & 588 & 600.450234103075 & -11.2715551472926 & -12.4502341030745 & -2.16813123617085 \tabularnewline
35 & 566 & 577.750520223269 & -18.6492876437219 & -11.7505202232692 & -0.906194760502808 \tabularnewline
36 & 557 & 556.446677170891 & -20.362194918674 & 0.553322829109256 & -0.210538900179225 \tabularnewline
37 & 561 & 551.744964624392 & -10.2490923931874 & 9.25503537560788 & 1.24420719543982 \tabularnewline
38 & 549 & 542.17575217975 & -9.81015129680633 & 6.8242478202497 & 0.0539067498620301 \tabularnewline
39 & 532 & 528.98393872936 & -11.9838493807184 & 3.01606127063958 & -0.266600580262813 \tabularnewline
40 & 526 & 524.104180887514 & -7.4280373692204 & 1.89581911248584 & 0.560577218702211 \tabularnewline
41 & 511 & 518.840730796659 & -6.03627972179115 & -7.84073079665896 & 0.171377174725085 \tabularnewline
42 & 499 & 517.654312954973 & -2.91597887486632 & -18.6543129549731 & 0.383108997287234 \tabularnewline
43 & 555 & 535.269755427318 & 10.2673767077247 & 19.7302445726816 & 1.61746664236007 \tabularnewline
44 & 565 & 553.004620317416 & 15.0590464716619 & 11.9953796825837 & 0.58841122804794 \tabularnewline
45 & 542 & 542.38985254972 & -1.42450813442845 & -0.389852549719771 & -2.02469278590887 \tabularnewline
46 & 527 & 534.957312465606 & -5.28304715631302 & -7.95731246560575 & -0.473936141250455 \tabularnewline
47 & 510 & 523.486083315946 & -9.25659062853895 & -13.4860833159456 & -0.488141262593637 \tabularnewline
48 & 514 & 517.587458824783 & -7.1002426387394 & -3.58745882478307 & 0.265035687803791 \tabularnewline
49 & 517 & 508.241128634468 & -8.54343223544694 & 8.75887136553227 & -0.177396633467984 \tabularnewline
50 & 508 & 499.142652389282 & -8.89984016343441 & 8.8573476107175 & -0.0437556608483276 \tabularnewline
51 & 493 & 490.833980783745 & -8.52123700864092 & 2.16601921625509 & 0.0464625792082002 \tabularnewline
52 & 490 & 486.328502361258 & -5.95267678554692 & 3.6714976387422 & 0.315796315503607 \tabularnewline
53 & 469 & 480.072604406743 & -6.14691457445949 & -11.0726044067432 & -0.0238976370997597 \tabularnewline
54 & 478 & 497.702801835814 & 9.09701864089559 & -19.7028018358138 & 1.87259509707129 \tabularnewline
55 & 528 & 511.038927314717 & 11.81185210855 & 16.9610726852829 & 0.333178849774093 \tabularnewline
56 & 534 & 515.505144116937 & 7.11189170466540 & 18.4948558830632 & -0.577010952359098 \tabularnewline
57 & 518 & 517.411063808219 & 3.78091839241226 & 0.588936191781156 & -0.409113871218731 \tabularnewline
58 & 506 & 514.369684157575 & -0.585149174345899 & -8.36968415757522 & -0.536337412367532 \tabularnewline
59 & 502 & 515.800597561215 & 0.70519518619107 & -13.8005975612148 & 0.158541580600858 \tabularnewline
60 & 516 & 518.231884386885 & 1.8102892578221 & -2.23188438688496 & 0.135812597659675 \tabularnewline
61 & 528 & 518.868839313513 & 1.05882769470306 & 9.1311606864867 & -0.0923275176192655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63506&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]562[/C][C]562[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]561[/C][C]561.339634174108[/C][C]-0.68843476512112[/C][C]-0.339634174107586[/C][C]-0.102939394850636[/C][/ROW]
[ROW][C]3[/C][C]555[/C][C]556.30167002153[/C][C]-3.53750737950236[/C][C]-1.30167002153033[/C][C]-0.361798347297018[/C][/ROW]
[ROW][C]4[/C][C]544[/C][C]546.172760882981[/C][C]-8.09272615414227[/C][C]-2.17276088298104[/C][C]-0.590130913529773[/C][/ROW]
[ROW][C]5[/C][C]537[/C][C]536.735724180028[/C][C]-9.01180035987964[/C][C]0.264275819972285[/C][C]-0.111732493912854[/C][/ROW]
[ROW][C]6[/C][C]543[/C][C]539.04938506565[/C][C]-1.37767196983302[/C][C]3.9506149343501[/C][C]0.937184990104925[/C][/ROW]
[ROW][C]7[/C][C]594[/C][C]580.494308601945[/C][C]27.5883280943792[/C][C]13.5056913980552[/C][C]3.56271197220082[/C][/ROW]
[ROW][C]8[/C][C]611[/C][C]613.88262978725[/C][C]31.5182883941516[/C][C]-2.88262978725027[/C][C]0.482653410588391[/C][/ROW]
[ROW][C]9[/C][C]613[/C][C]621.988478073247[/C][C]15.6535592313344[/C][C]-8.98847807324663[/C][C]-1.94848619216758[/C][/ROW]
[ROW][C]10[/C][C]611[/C][C]616.115159527653[/C][C]1.06969186910092[/C][C]-5.11515952765285[/C][C]-1.79122630472807[/C][/ROW]
[ROW][C]11[/C][C]594[/C][C]598.374965528423[/C][C]-11.6711826619275[/C][C]-4.37496552842256[/C][C]-1.56482868994634[/C][/ROW]
[ROW][C]12[/C][C]595[/C][C]591.465311091863[/C][C]-8.44608365089035[/C][C]3.53468890813752[/C][C]0.39610552935425[/C][/ROW]
[ROW][C]13[/C][C]591[/C][C]589.01389056506[/C][C]-4.39877114673587[/C][C]1.9861094349401[/C][C]0.498878384092907[/C][/ROW]
[ROW][C]14[/C][C]589[/C][C]587.295628129704[/C][C]-2.58121187593471[/C][C]1.70437187029575[/C][C]0.226855833739574[/C][/ROW]
[ROW][C]15[/C][C]584[/C][C]582.462954841753[/C][C]-4.0626675259225[/C][C]1.53704515824712[/C][C]-0.180502321925800[/C][/ROW]
[ROW][C]16[/C][C]573[/C][C]573.14714247557[/C][C]-7.48180281364099[/C][C]-0.147142475570026[/C][C]-0.426441453102353[/C][/ROW]
[ROW][C]17[/C][C]567[/C][C]568.395244040748[/C][C]-5.68477039906724[/C][C]-1.39524404074807[/C][C]0.221656478094008[/C][/ROW]
[ROW][C]18[/C][C]569[/C][C]576.303965613224[/C][C]3.19725889977592[/C][C]-7.30396561322415[/C][C]1.08661119861124[/C][/ROW]
[ROW][C]19[/C][C]621[/C][C]604.109321407278[/C][C]19.2226737235713[/C][C]16.8906785927223[/C][C]1.96869232053014[/C][/ROW]
[ROW][C]20[/C][C]629[/C][C]626.740343320033[/C][C]21.4465947089291[/C][C]2.25965667996701[/C][C]0.273316763573875[/C][/ROW]
[ROW][C]21[/C][C]628[/C][C]635.049302764181[/C][C]12.8657686441724[/C][C]-7.049302764181[/C][C]-1.05381569760183[/C][/ROW]
[ROW][C]22[/C][C]612[/C][C]619.824942476355[/C][C]-5.4820767200239[/C][C]-7.82494247635457[/C][C]-2.25358414995367[/C][/ROW]
[ROW][C]23[/C][C]595[/C][C]602.87003233706[/C][C]-12.9721761514677[/C][C]-7.87003233706008[/C][C]-0.91996411628555[/C][/ROW]
[ROW][C]24[/C][C]597[/C][C]593.55878515879[/C][C]-10.5849499685856[/C][C]3.4412148412095[/C][C]0.293329406862227[/C][/ROW]
[ROW][C]25[/C][C]593[/C][C]589.646999973984[/C][C]-6.23248273551301[/C][C]3.35300002601576[/C][C]0.536273166054448[/C][/ROW]
[ROW][C]26[/C][C]590[/C][C]585.735052774875[/C][C]-4.71693547254[/C][C]4.26494722512517[/C][C]0.186443412766483[/C][/ROW]
[ROW][C]27[/C][C]580[/C][C]576.128187072909[/C][C]-7.88254306910585[/C][C]3.87181292709132[/C][C]-0.3876692107905[/C][/ROW]
[ROW][C]28[/C][C]574[/C][C]571.679609754655[/C][C]-5.67052766740574[/C][C]2.32039024534491[/C][C]0.272859198586681[/C][/ROW]
[ROW][C]29[/C][C]573[/C][C]576.387572851837[/C][C]1.05036947512808[/C][C]-3.38757285183711[/C][C]0.82879433616291[/C][/ROW]
[ROW][C]30[/C][C]573[/C][C]588.021480555575[/C][C]7.8982255336608[/C][C]-15.0214805555750[/C][C]0.839675465019677[/C][/ROW]
[ROW][C]31[/C][C]620[/C][C]604.29655454765[/C][C]13.3000682681884[/C][C]15.7034454523500[/C][C]0.66276192593235[/C][/ROW]
[ROW][C]32[/C][C]626[/C][C]619.482336143825[/C][C]14.5163406972291[/C][C]6.51766385617465[/C][C]0.149429548313448[/C][/ROW]
[ROW][C]33[/C][C]620[/C][C]621.40057097307[/C][C]6.38149809770838[/C][C]-1.40057097307061[/C][C]-0.999203236606412[/C][/ROW]
[ROW][C]34[/C][C]588[/C][C]600.450234103075[/C][C]-11.2715551472926[/C][C]-12.4502341030745[/C][C]-2.16813123617085[/C][/ROW]
[ROW][C]35[/C][C]566[/C][C]577.750520223269[/C][C]-18.6492876437219[/C][C]-11.7505202232692[/C][C]-0.906194760502808[/C][/ROW]
[ROW][C]36[/C][C]557[/C][C]556.446677170891[/C][C]-20.362194918674[/C][C]0.553322829109256[/C][C]-0.210538900179225[/C][/ROW]
[ROW][C]37[/C][C]561[/C][C]551.744964624392[/C][C]-10.2490923931874[/C][C]9.25503537560788[/C][C]1.24420719543982[/C][/ROW]
[ROW][C]38[/C][C]549[/C][C]542.17575217975[/C][C]-9.81015129680633[/C][C]6.8242478202497[/C][C]0.0539067498620301[/C][/ROW]
[ROW][C]39[/C][C]532[/C][C]528.98393872936[/C][C]-11.9838493807184[/C][C]3.01606127063958[/C][C]-0.266600580262813[/C][/ROW]
[ROW][C]40[/C][C]526[/C][C]524.104180887514[/C][C]-7.4280373692204[/C][C]1.89581911248584[/C][C]0.560577218702211[/C][/ROW]
[ROW][C]41[/C][C]511[/C][C]518.840730796659[/C][C]-6.03627972179115[/C][C]-7.84073079665896[/C][C]0.171377174725085[/C][/ROW]
[ROW][C]42[/C][C]499[/C][C]517.654312954973[/C][C]-2.91597887486632[/C][C]-18.6543129549731[/C][C]0.383108997287234[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]535.269755427318[/C][C]10.2673767077247[/C][C]19.7302445726816[/C][C]1.61746664236007[/C][/ROW]
[ROW][C]44[/C][C]565[/C][C]553.004620317416[/C][C]15.0590464716619[/C][C]11.9953796825837[/C][C]0.58841122804794[/C][/ROW]
[ROW][C]45[/C][C]542[/C][C]542.38985254972[/C][C]-1.42450813442845[/C][C]-0.389852549719771[/C][C]-2.02469278590887[/C][/ROW]
[ROW][C]46[/C][C]527[/C][C]534.957312465606[/C][C]-5.28304715631302[/C][C]-7.95731246560575[/C][C]-0.473936141250455[/C][/ROW]
[ROW][C]47[/C][C]510[/C][C]523.486083315946[/C][C]-9.25659062853895[/C][C]-13.4860833159456[/C][C]-0.488141262593637[/C][/ROW]
[ROW][C]48[/C][C]514[/C][C]517.587458824783[/C][C]-7.1002426387394[/C][C]-3.58745882478307[/C][C]0.265035687803791[/C][/ROW]
[ROW][C]49[/C][C]517[/C][C]508.241128634468[/C][C]-8.54343223544694[/C][C]8.75887136553227[/C][C]-0.177396633467984[/C][/ROW]
[ROW][C]50[/C][C]508[/C][C]499.142652389282[/C][C]-8.89984016343441[/C][C]8.8573476107175[/C][C]-0.0437556608483276[/C][/ROW]
[ROW][C]51[/C][C]493[/C][C]490.833980783745[/C][C]-8.52123700864092[/C][C]2.16601921625509[/C][C]0.0464625792082002[/C][/ROW]
[ROW][C]52[/C][C]490[/C][C]486.328502361258[/C][C]-5.95267678554692[/C][C]3.6714976387422[/C][C]0.315796315503607[/C][/ROW]
[ROW][C]53[/C][C]469[/C][C]480.072604406743[/C][C]-6.14691457445949[/C][C]-11.0726044067432[/C][C]-0.0238976370997597[/C][/ROW]
[ROW][C]54[/C][C]478[/C][C]497.702801835814[/C][C]9.09701864089559[/C][C]-19.7028018358138[/C][C]1.87259509707129[/C][/ROW]
[ROW][C]55[/C][C]528[/C][C]511.038927314717[/C][C]11.81185210855[/C][C]16.9610726852829[/C][C]0.333178849774093[/C][/ROW]
[ROW][C]56[/C][C]534[/C][C]515.505144116937[/C][C]7.11189170466540[/C][C]18.4948558830632[/C][C]-0.577010952359098[/C][/ROW]
[ROW][C]57[/C][C]518[/C][C]517.411063808219[/C][C]3.78091839241226[/C][C]0.588936191781156[/C][C]-0.409113871218731[/C][/ROW]
[ROW][C]58[/C][C]506[/C][C]514.369684157575[/C][C]-0.585149174345899[/C][C]-8.36968415757522[/C][C]-0.536337412367532[/C][/ROW]
[ROW][C]59[/C][C]502[/C][C]515.800597561215[/C][C]0.70519518619107[/C][C]-13.8005975612148[/C][C]0.158541580600858[/C][/ROW]
[ROW][C]60[/C][C]516[/C][C]518.231884386885[/C][C]1.8102892578221[/C][C]-2.23188438688496[/C][C]0.135812597659675[/C][/ROW]
[ROW][C]61[/C][C]528[/C][C]518.868839313513[/C][C]1.05882769470306[/C][C]9.1311606864867[/C][C]-0.0923275176192655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63506&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63506&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
1562562000
2561561.339634174108-0.68843476512112-0.339634174107586-0.102939394850636
3555556.30167002153-3.53750737950236-1.30167002153033-0.361798347297018
4544546.172760882981-8.09272615414227-2.17276088298104-0.590130913529773
5537536.735724180028-9.011800359879640.264275819972285-0.111732493912854
6543539.04938506565-1.377671969833023.95061493435010.937184990104925
7594580.49430860194527.588328094379213.50569139805523.56271197220082
8611613.8826297872531.5182883941516-2.882629787250270.482653410588391
9613621.98847807324715.6535592313344-8.98847807324663-1.94848619216758
10611616.1151595276531.06969186910092-5.11515952765285-1.79122630472807
11594598.374965528423-11.6711826619275-4.37496552842256-1.56482868994634
12595591.465311091863-8.446083650890353.534688908137520.39610552935425
13591589.01389056506-4.398771146735871.98610943494010.498878384092907
14589587.295628129704-2.581211875934711.704371870295750.226855833739574
15584582.462954841753-4.06266752592251.53704515824712-0.180502321925800
16573573.14714247557-7.48180281364099-0.147142475570026-0.426441453102353
17567568.395244040748-5.68477039906724-1.395244040748070.221656478094008
18569576.3039656132243.19725889977592-7.303965613224151.08661119861124
19621604.10932140727819.222673723571316.89067859272231.96869232053014
20629626.74034332003321.44659470892912.259656679967010.273316763573875
21628635.04930276418112.8657686441724-7.049302764181-1.05381569760183
22612619.824942476355-5.4820767200239-7.82494247635457-2.25358414995367
23595602.87003233706-12.9721761514677-7.87003233706008-0.91996411628555
24597593.55878515879-10.58494996858563.44121484120950.293329406862227
25593589.646999973984-6.232482735513013.353000026015760.536273166054448
26590585.735052774875-4.716935472544.264947225125170.186443412766483
27580576.128187072909-7.882543069105853.87181292709132-0.3876692107905
28574571.679609754655-5.670527667405742.320390245344910.272859198586681
29573576.3875728518371.05036947512808-3.387572851837110.82879433616291
30573588.0214805555757.8982255336608-15.02148055557500.839675465019677
31620604.2965545476513.300068268188415.70344545235000.66276192593235
32626619.48233614382514.51634069722916.517663856174650.149429548313448
33620621.400570973076.38149809770838-1.40057097307061-0.999203236606412
34588600.450234103075-11.2715551472926-12.4502341030745-2.16813123617085
35566577.750520223269-18.6492876437219-11.7505202232692-0.906194760502808
36557556.446677170891-20.3621949186740.553322829109256-0.210538900179225
37561551.744964624392-10.24909239318749.255035375607881.24420719543982
38549542.17575217975-9.810151296806336.82424782024970.0539067498620301
39532528.98393872936-11.98384938071843.01606127063958-0.266600580262813
40526524.104180887514-7.42803736922041.895819112485840.560577218702211
41511518.840730796659-6.03627972179115-7.840730796658960.171377174725085
42499517.654312954973-2.91597887486632-18.65431295497310.383108997287234
43555535.26975542731810.267376707724719.73024457268161.61746664236007
44565553.00462031741615.059046471661911.99537968258370.58841122804794
45542542.38985254972-1.42450813442845-0.389852549719771-2.02469278590887
46527534.957312465606-5.28304715631302-7.95731246560575-0.473936141250455
47510523.486083315946-9.25659062853895-13.4860833159456-0.488141262593637
48514517.587458824783-7.1002426387394-3.587458824783070.265035687803791
49517508.241128634468-8.543432235446948.75887136553227-0.177396633467984
50508499.142652389282-8.899840163434418.8573476107175-0.0437556608483276
51493490.833980783745-8.521237008640922.166019216255090.0464625792082002
52490486.328502361258-5.952676785546923.67149763874220.315796315503607
53469480.072604406743-6.14691457445949-11.0726044067432-0.0238976370997597
54478497.7028018358149.09701864089559-19.70280183581381.87259509707129
55528511.03892731471711.8118521085516.96107268528290.333178849774093
56534515.5051441169377.1118917046654018.4948558830632-0.577010952359098
57518517.4110638082193.780918392412260.588936191781156-0.409113871218731
58506514.369684157575-0.585149174345899-8.36968415757522-0.536337412367532
59502515.8005975612150.70519518619107-13.80059756121480.158541580600858
60516518.2318843868851.8102892578221-2.231884386884960.135812597659675
61528518.8688393135131.058827694703069.1311606864867-0.0923275176192655



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')