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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, 09 Dec 2010 12:02:42 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/09/t1291896083teevhzlsgjvz6t9.htm/, Retrieved Sun, 28 Apr 2024 20:34:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107181, Retrieved Sun, 28 Apr 2024 20:34:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD  [Structural Time Series Models] [HPC Retail Sales] [2008-03-06 16:52:55] [74be16979710d4c4e7c6647856088456]
- R  D    [Structural Time Series Models] [HPC Retail Sales] [2008-03-08 11:33:35] [74be16979710d4c4e7c6647856088456]
-  M D        [Structural Time Series Models] [workshop 8- struc...] [2010-12-09 12:02:42] [36a5183bc8f6439b2481209b0fbe6bda] [Current]
- RMP           [Exponential Smoothing] [workshop 8- expon...] [2010-12-09 12:09:50] [1df589bc3feb749f1946d8c1ee38b85f]
-                 [Exponential Smoothing] [workshop 8- multi...] [2010-12-09 12:13:47] [1df589bc3feb749f1946d8c1ee38b85f]
-    D            [Exponential Smoothing] [workshop- exponen...] [2010-12-09 16:15:55] [1df589bc3feb749f1946d8c1ee38b85f]
-    D          [Structural Time Series Models] [workshop 8 - stru...] [2010-12-09 16:12:03] [1df589bc3feb749f1946d8c1ee38b85f]
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Dataseries X:
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
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 time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107181&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]5 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=107181&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107181&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
197009700000
290819420.62455281495-4.5890426297954-334.626271902039-1.94825201291792
390849148.2077517124-22.2303618909331-61.4190688909295-1.35872117365695
497439343.40355518114-8.8193715342712396.7083849351671.35850437495548
585879095.70202034107-19.3928504723650-504.874476723729-1.70098600051497
697319271.86407345205-13.6836322275629455.7850360802611.45024067345611
795639434.40844999122-10.0399239606939125.5140932926071.31890702427521
899989698.13795425041-5.33632031363061295.0604656373962.05127007984087
994379674.89595659924-5.62859801659247-237.581978828241-0.134046595619623
10100389807.97758753645-3.35094274695094227.5934809566931.03721999324648
1199189889.12380139853-1.9454851109925027.39829186792180.631326425854686
1292529667.85545584089-5.6050188046502-412.021472076373-1.63810580704716
1397379552.0412545418-3.61040227495166186.943252254166-0.867707542115775
1490359409.52966529666-2.68610765998603-372.045359372442-1.07943661012622
1591339311.8021903386-4.17279616924874-177.283195680046-0.672370905652503
1694879154.66553084616-8.34497094258953334.647008087387-1.05021514512539
1787009193.55821813077-6.9846990075786-494.2897316040540.333489306211107
1896279253.4586044127-5.31403601802089372.4637230323580.487484467042734
1989479171.21697982802-6.8558937647209-222.945610987148-0.570707897539624
2092839097.55670913712-7.93365258961034186.560772068260-0.499253744665062
2188299105.02135657368-7.72594706235133-276.2802347638930.115351198940874
2299479330.49146897202-5.08649185514184612.5802749847941.74711149030179
2396289424.08534386672-4.23982836968727202.2503907528360.738833540386894
2493189518.27043512378-3.77030289426937-201.9348515495240.737829998929544
2596059459.6525333162-3.83436376185997146.280162888563-0.413336377929872
2686409243.96375879017-4.5673021732229-600.392639825714-1.58347703966637
2792149214.56358112121-4.81574726388101-0.157408460500335-0.181087518937939
2895679224.46897353754-4.57916825516746342.2964654638070.105542558958506
2985479155.45177696454-5.81034756066413-607.427922744883-0.463382439639212
3091858982.89970567253-8.99400504744033204.786549860404-1.21588207581558
3194709195.98429437877-5.18028867497022270.3794909051941.64089527434201
3291239183.67228549114-5.28384289359201-60.5541585213881-0.0531211458806797
3392789382.238653821-2.87829799995906-107.6368611160591.52376779855306
34101709524.17096509914-1.54558636518991643.4076573815851.08343245497791
3594349459.87903816316-1.97218082081931-24.8282625863602-0.469477382632173
3696559554.28603843822-1.4971418164170599.09844209023150.721210944559087
3794299426.43881059238-2.046589058599064.67679322160564-0.944253596916056
3887399353.99551485272-2.45262076854173-613.825140780271-0.522791335292416
3995529400.4538714729-2.0255327633396150.7437093160060.359400179017050
4096879364.41978943982-2.42987756385864323.131332353283-0.247868711532408
4190199439.20439028914-1.34601321892296-421.4508419876910.562529215139126
4296729526.26882372462-0.047847268485069144.2969670518200.648008784330493
4392069354.29953962991-2.45049008702385-145.485846509328-1.27002892558780
4490699291.74798991872-3.19410451169397-221.756334733109-0.446692690210052
4597889527.3417025869-0.719835326329942256.6965501139551.78077301638989
46103129624.511097137840.096027957588888685.8597870709150.731020408457668
47101059836.23184526941.49713127116114265.2409342760261.58093275393686
4898639824.504981512341.4237357828263838.7154690254109-0.0987486279718978
4996569747.284836308630.997551636934981-89.9762517528914-0.586130706721582
5092959801.800005113741.33567199133678-507.68614499150.397175228268162
5199469816.170475885931.43942942766346129.6152475589620.096192398012906
5297019676.922683202960.065689968782605126.3750423304587-1.03376292817842
5390499568.96478398853-1.13769998998178-518.205210398125-0.793200338741033
54101909686.239676118570.250703430598917501.8277898474430.871968273634657
5597069795.057315726631.49455614989545-90.83765730320480.803048404502364
5697659964.837820382483.26280650936327-202.6118802677061.24985211411604
5798939928.374504672822.89852693427271-34.7169593287432-0.295842154417687
5899949736.867826754551.38879256112751260.358382812888-1.44974137909044
59104339841.258171068342.06803443808084590.0302673792870.768443002439589
60100739925.00863817952.54992803660270146.6340136989280.609127917613946
611011210052.85943286913.2801345084146957.06154181480540.932957672188076
6292669964.61454470272.69702650226427-697.100793514692-0.679604846605334
6398209805.627217455231.5097542334838517.0356306578212-1.19665516212745
64100979863.865650548211.98797486111284232.2035348082970.418831124021567
6591159841.262161059161.75920451005729-725.859346237903-0.181460233955533
66104119901.61220556342.3291757362672508.4272679715910.432962988604558
6796789895.063257502942.24374033344239-216.917323822984-0.0657746447297351
681040810131.57375659304.36484884925401272.5629337803091.74014104808724
691015310180.03756834284.72613312181003-27.76688861343380.328215053005083
701036810200.94747659534.84359730176286166.7843357439430.120589854460093
711058110156.28394818084.52340528299437425.537312848946-0.369042073997497
721059710267.04953649915.15994571093786328.1879559706250.79171878968827
731068010388.66273884895.84920868267741289.4073553899860.86687876213114
74973810404.42575585705.91101679723834-666.589716397170.0736698387168638
75955610104.89350414813.82775190582163-543.854804388791-2.26531949085490

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 9700 & 9700 & 0 & 0 & 0 \tabularnewline
2 & 9081 & 9420.62455281495 & -4.5890426297954 & -334.626271902039 & -1.94825201291792 \tabularnewline
3 & 9084 & 9148.2077517124 & -22.2303618909331 & -61.4190688909295 & -1.35872117365695 \tabularnewline
4 & 9743 & 9343.40355518114 & -8.8193715342712 & 396.708384935167 & 1.35850437495548 \tabularnewline
5 & 8587 & 9095.70202034107 & -19.3928504723650 & -504.874476723729 & -1.70098600051497 \tabularnewline
6 & 9731 & 9271.86407345205 & -13.6836322275629 & 455.785036080261 & 1.45024067345611 \tabularnewline
7 & 9563 & 9434.40844999122 & -10.0399239606939 & 125.514093292607 & 1.31890702427521 \tabularnewline
8 & 9998 & 9698.13795425041 & -5.33632031363061 & 295.060465637396 & 2.05127007984087 \tabularnewline
9 & 9437 & 9674.89595659924 & -5.62859801659247 & -237.581978828241 & -0.134046595619623 \tabularnewline
10 & 10038 & 9807.97758753645 & -3.35094274695094 & 227.593480956693 & 1.03721999324648 \tabularnewline
11 & 9918 & 9889.12380139853 & -1.94548511099250 & 27.3982918679218 & 0.631326425854686 \tabularnewline
12 & 9252 & 9667.85545584089 & -5.6050188046502 & -412.021472076373 & -1.63810580704716 \tabularnewline
13 & 9737 & 9552.0412545418 & -3.61040227495166 & 186.943252254166 & -0.867707542115775 \tabularnewline
14 & 9035 & 9409.52966529666 & -2.68610765998603 & -372.045359372442 & -1.07943661012622 \tabularnewline
15 & 9133 & 9311.8021903386 & -4.17279616924874 & -177.283195680046 & -0.672370905652503 \tabularnewline
16 & 9487 & 9154.66553084616 & -8.34497094258953 & 334.647008087387 & -1.05021514512539 \tabularnewline
17 & 8700 & 9193.55821813077 & -6.9846990075786 & -494.289731604054 & 0.333489306211107 \tabularnewline
18 & 9627 & 9253.4586044127 & -5.31403601802089 & 372.463723032358 & 0.487484467042734 \tabularnewline
19 & 8947 & 9171.21697982802 & -6.8558937647209 & -222.945610987148 & -0.570707897539624 \tabularnewline
20 & 9283 & 9097.55670913712 & -7.93365258961034 & 186.560772068260 & -0.499253744665062 \tabularnewline
21 & 8829 & 9105.02135657368 & -7.72594706235133 & -276.280234763893 & 0.115351198940874 \tabularnewline
22 & 9947 & 9330.49146897202 & -5.08649185514184 & 612.580274984794 & 1.74711149030179 \tabularnewline
23 & 9628 & 9424.08534386672 & -4.23982836968727 & 202.250390752836 & 0.738833540386894 \tabularnewline
24 & 9318 & 9518.27043512378 & -3.77030289426937 & -201.934851549524 & 0.737829998929544 \tabularnewline
25 & 9605 & 9459.6525333162 & -3.83436376185997 & 146.280162888563 & -0.413336377929872 \tabularnewline
26 & 8640 & 9243.96375879017 & -4.5673021732229 & -600.392639825714 & -1.58347703966637 \tabularnewline
27 & 9214 & 9214.56358112121 & -4.81574726388101 & -0.157408460500335 & -0.181087518937939 \tabularnewline
28 & 9567 & 9224.46897353754 & -4.57916825516746 & 342.296465463807 & 0.105542558958506 \tabularnewline
29 & 8547 & 9155.45177696454 & -5.81034756066413 & -607.427922744883 & -0.463382439639212 \tabularnewline
30 & 9185 & 8982.89970567253 & -8.99400504744033 & 204.786549860404 & -1.21588207581558 \tabularnewline
31 & 9470 & 9195.98429437877 & -5.18028867497022 & 270.379490905194 & 1.64089527434201 \tabularnewline
32 & 9123 & 9183.67228549114 & -5.28384289359201 & -60.5541585213881 & -0.0531211458806797 \tabularnewline
33 & 9278 & 9382.238653821 & -2.87829799995906 & -107.636861116059 & 1.52376779855306 \tabularnewline
34 & 10170 & 9524.17096509914 & -1.54558636518991 & 643.407657381585 & 1.08343245497791 \tabularnewline
35 & 9434 & 9459.87903816316 & -1.97218082081931 & -24.8282625863602 & -0.469477382632173 \tabularnewline
36 & 9655 & 9554.28603843822 & -1.49714181641705 & 99.0984420902315 & 0.721210944559087 \tabularnewline
37 & 9429 & 9426.43881059238 & -2.04658905859906 & 4.67679322160564 & -0.944253596916056 \tabularnewline
38 & 8739 & 9353.99551485272 & -2.45262076854173 & -613.825140780271 & -0.522791335292416 \tabularnewline
39 & 9552 & 9400.4538714729 & -2.0255327633396 & 150.743709316006 & 0.359400179017050 \tabularnewline
40 & 9687 & 9364.41978943982 & -2.42987756385864 & 323.131332353283 & -0.247868711532408 \tabularnewline
41 & 9019 & 9439.20439028914 & -1.34601321892296 & -421.450841987691 & 0.562529215139126 \tabularnewline
42 & 9672 & 9526.26882372462 & -0.047847268485069 & 144.296967051820 & 0.648008784330493 \tabularnewline
43 & 9206 & 9354.29953962991 & -2.45049008702385 & -145.485846509328 & -1.27002892558780 \tabularnewline
44 & 9069 & 9291.74798991872 & -3.19410451169397 & -221.756334733109 & -0.446692690210052 \tabularnewline
45 & 9788 & 9527.3417025869 & -0.719835326329942 & 256.696550113955 & 1.78077301638989 \tabularnewline
46 & 10312 & 9624.51109713784 & 0.096027957588888 & 685.859787070915 & 0.731020408457668 \tabularnewline
47 & 10105 & 9836.2318452694 & 1.49713127116114 & 265.240934276026 & 1.58093275393686 \tabularnewline
48 & 9863 & 9824.50498151234 & 1.42373578282638 & 38.7154690254109 & -0.0987486279718978 \tabularnewline
49 & 9656 & 9747.28483630863 & 0.997551636934981 & -89.9762517528914 & -0.586130706721582 \tabularnewline
50 & 9295 & 9801.80000511374 & 1.33567199133678 & -507.6861449915 & 0.397175228268162 \tabularnewline
51 & 9946 & 9816.17047588593 & 1.43942942766346 & 129.615247558962 & 0.096192398012906 \tabularnewline
52 & 9701 & 9676.92268320296 & 0.0656899687826051 & 26.3750423304587 & -1.03376292817842 \tabularnewline
53 & 9049 & 9568.96478398853 & -1.13769998998178 & -518.205210398125 & -0.793200338741033 \tabularnewline
54 & 10190 & 9686.23967611857 & 0.250703430598917 & 501.827789847443 & 0.871968273634657 \tabularnewline
55 & 9706 & 9795.05731572663 & 1.49455614989545 & -90.8376573032048 & 0.803048404502364 \tabularnewline
56 & 9765 & 9964.83782038248 & 3.26280650936327 & -202.611880267706 & 1.24985211411604 \tabularnewline
57 & 9893 & 9928.37450467282 & 2.89852693427271 & -34.7169593287432 & -0.295842154417687 \tabularnewline
58 & 9994 & 9736.86782675455 & 1.38879256112751 & 260.358382812888 & -1.44974137909044 \tabularnewline
59 & 10433 & 9841.25817106834 & 2.06803443808084 & 590.030267379287 & 0.768443002439589 \tabularnewline
60 & 10073 & 9925.0086381795 & 2.54992803660270 & 146.634013698928 & 0.609127917613946 \tabularnewline
61 & 10112 & 10052.8594328691 & 3.28013450841469 & 57.0615418148054 & 0.932957672188076 \tabularnewline
62 & 9266 & 9964.6145447027 & 2.69702650226427 & -697.100793514692 & -0.679604846605334 \tabularnewline
63 & 9820 & 9805.62721745523 & 1.50975423348385 & 17.0356306578212 & -1.19665516212745 \tabularnewline
64 & 10097 & 9863.86565054821 & 1.98797486111284 & 232.203534808297 & 0.418831124021567 \tabularnewline
65 & 9115 & 9841.26216105916 & 1.75920451005729 & -725.859346237903 & -0.181460233955533 \tabularnewline
66 & 10411 & 9901.6122055634 & 2.3291757362672 & 508.427267971591 & 0.432962988604558 \tabularnewline
67 & 9678 & 9895.06325750294 & 2.24374033344239 & -216.917323822984 & -0.0657746447297351 \tabularnewline
68 & 10408 & 10131.5737565930 & 4.36484884925401 & 272.562933780309 & 1.74014104808724 \tabularnewline
69 & 10153 & 10180.0375683428 & 4.72613312181003 & -27.7668886134338 & 0.328215053005083 \tabularnewline
70 & 10368 & 10200.9474765953 & 4.84359730176286 & 166.784335743943 & 0.120589854460093 \tabularnewline
71 & 10581 & 10156.2839481808 & 4.52340528299437 & 425.537312848946 & -0.369042073997497 \tabularnewline
72 & 10597 & 10267.0495364991 & 5.15994571093786 & 328.187955970625 & 0.79171878968827 \tabularnewline
73 & 10680 & 10388.6627388489 & 5.84920868267741 & 289.407355389986 & 0.86687876213114 \tabularnewline
74 & 9738 & 10404.4257558570 & 5.91101679723834 & -666.58971639717 & 0.0736698387168638 \tabularnewline
75 & 9556 & 10104.8935041481 & 3.82775190582163 & -543.854804388791 & -2.26531949085490 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107181&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]9700[/C][C]9700[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]9081[/C][C]9420.62455281495[/C][C]-4.5890426297954[/C][C]-334.626271902039[/C][C]-1.94825201291792[/C][/ROW]
[ROW][C]3[/C][C]9084[/C][C]9148.2077517124[/C][C]-22.2303618909331[/C][C]-61.4190688909295[/C][C]-1.35872117365695[/C][/ROW]
[ROW][C]4[/C][C]9743[/C][C]9343.40355518114[/C][C]-8.8193715342712[/C][C]396.708384935167[/C][C]1.35850437495548[/C][/ROW]
[ROW][C]5[/C][C]8587[/C][C]9095.70202034107[/C][C]-19.3928504723650[/C][C]-504.874476723729[/C][C]-1.70098600051497[/C][/ROW]
[ROW][C]6[/C][C]9731[/C][C]9271.86407345205[/C][C]-13.6836322275629[/C][C]455.785036080261[/C][C]1.45024067345611[/C][/ROW]
[ROW][C]7[/C][C]9563[/C][C]9434.40844999122[/C][C]-10.0399239606939[/C][C]125.514093292607[/C][C]1.31890702427521[/C][/ROW]
[ROW][C]8[/C][C]9998[/C][C]9698.13795425041[/C][C]-5.33632031363061[/C][C]295.060465637396[/C][C]2.05127007984087[/C][/ROW]
[ROW][C]9[/C][C]9437[/C][C]9674.89595659924[/C][C]-5.62859801659247[/C][C]-237.581978828241[/C][C]-0.134046595619623[/C][/ROW]
[ROW][C]10[/C][C]10038[/C][C]9807.97758753645[/C][C]-3.35094274695094[/C][C]227.593480956693[/C][C]1.03721999324648[/C][/ROW]
[ROW][C]11[/C][C]9918[/C][C]9889.12380139853[/C][C]-1.94548511099250[/C][C]27.3982918679218[/C][C]0.631326425854686[/C][/ROW]
[ROW][C]12[/C][C]9252[/C][C]9667.85545584089[/C][C]-5.6050188046502[/C][C]-412.021472076373[/C][C]-1.63810580704716[/C][/ROW]
[ROW][C]13[/C][C]9737[/C][C]9552.0412545418[/C][C]-3.61040227495166[/C][C]186.943252254166[/C][C]-0.867707542115775[/C][/ROW]
[ROW][C]14[/C][C]9035[/C][C]9409.52966529666[/C][C]-2.68610765998603[/C][C]-372.045359372442[/C][C]-1.07943661012622[/C][/ROW]
[ROW][C]15[/C][C]9133[/C][C]9311.8021903386[/C][C]-4.17279616924874[/C][C]-177.283195680046[/C][C]-0.672370905652503[/C][/ROW]
[ROW][C]16[/C][C]9487[/C][C]9154.66553084616[/C][C]-8.34497094258953[/C][C]334.647008087387[/C][C]-1.05021514512539[/C][/ROW]
[ROW][C]17[/C][C]8700[/C][C]9193.55821813077[/C][C]-6.9846990075786[/C][C]-494.289731604054[/C][C]0.333489306211107[/C][/ROW]
[ROW][C]18[/C][C]9627[/C][C]9253.4586044127[/C][C]-5.31403601802089[/C][C]372.463723032358[/C][C]0.487484467042734[/C][/ROW]
[ROW][C]19[/C][C]8947[/C][C]9171.21697982802[/C][C]-6.8558937647209[/C][C]-222.945610987148[/C][C]-0.570707897539624[/C][/ROW]
[ROW][C]20[/C][C]9283[/C][C]9097.55670913712[/C][C]-7.93365258961034[/C][C]186.560772068260[/C][C]-0.499253744665062[/C][/ROW]
[ROW][C]21[/C][C]8829[/C][C]9105.02135657368[/C][C]-7.72594706235133[/C][C]-276.280234763893[/C][C]0.115351198940874[/C][/ROW]
[ROW][C]22[/C][C]9947[/C][C]9330.49146897202[/C][C]-5.08649185514184[/C][C]612.580274984794[/C][C]1.74711149030179[/C][/ROW]
[ROW][C]23[/C][C]9628[/C][C]9424.08534386672[/C][C]-4.23982836968727[/C][C]202.250390752836[/C][C]0.738833540386894[/C][/ROW]
[ROW][C]24[/C][C]9318[/C][C]9518.27043512378[/C][C]-3.77030289426937[/C][C]-201.934851549524[/C][C]0.737829998929544[/C][/ROW]
[ROW][C]25[/C][C]9605[/C][C]9459.6525333162[/C][C]-3.83436376185997[/C][C]146.280162888563[/C][C]-0.413336377929872[/C][/ROW]
[ROW][C]26[/C][C]8640[/C][C]9243.96375879017[/C][C]-4.5673021732229[/C][C]-600.392639825714[/C][C]-1.58347703966637[/C][/ROW]
[ROW][C]27[/C][C]9214[/C][C]9214.56358112121[/C][C]-4.81574726388101[/C][C]-0.157408460500335[/C][C]-0.181087518937939[/C][/ROW]
[ROW][C]28[/C][C]9567[/C][C]9224.46897353754[/C][C]-4.57916825516746[/C][C]342.296465463807[/C][C]0.105542558958506[/C][/ROW]
[ROW][C]29[/C][C]8547[/C][C]9155.45177696454[/C][C]-5.81034756066413[/C][C]-607.427922744883[/C][C]-0.463382439639212[/C][/ROW]
[ROW][C]30[/C][C]9185[/C][C]8982.89970567253[/C][C]-8.99400504744033[/C][C]204.786549860404[/C][C]-1.21588207581558[/C][/ROW]
[ROW][C]31[/C][C]9470[/C][C]9195.98429437877[/C][C]-5.18028867497022[/C][C]270.379490905194[/C][C]1.64089527434201[/C][/ROW]
[ROW][C]32[/C][C]9123[/C][C]9183.67228549114[/C][C]-5.28384289359201[/C][C]-60.5541585213881[/C][C]-0.0531211458806797[/C][/ROW]
[ROW][C]33[/C][C]9278[/C][C]9382.238653821[/C][C]-2.87829799995906[/C][C]-107.636861116059[/C][C]1.52376779855306[/C][/ROW]
[ROW][C]34[/C][C]10170[/C][C]9524.17096509914[/C][C]-1.54558636518991[/C][C]643.407657381585[/C][C]1.08343245497791[/C][/ROW]
[ROW][C]35[/C][C]9434[/C][C]9459.87903816316[/C][C]-1.97218082081931[/C][C]-24.8282625863602[/C][C]-0.469477382632173[/C][/ROW]
[ROW][C]36[/C][C]9655[/C][C]9554.28603843822[/C][C]-1.49714181641705[/C][C]99.0984420902315[/C][C]0.721210944559087[/C][/ROW]
[ROW][C]37[/C][C]9429[/C][C]9426.43881059238[/C][C]-2.04658905859906[/C][C]4.67679322160564[/C][C]-0.944253596916056[/C][/ROW]
[ROW][C]38[/C][C]8739[/C][C]9353.99551485272[/C][C]-2.45262076854173[/C][C]-613.825140780271[/C][C]-0.522791335292416[/C][/ROW]
[ROW][C]39[/C][C]9552[/C][C]9400.4538714729[/C][C]-2.0255327633396[/C][C]150.743709316006[/C][C]0.359400179017050[/C][/ROW]
[ROW][C]40[/C][C]9687[/C][C]9364.41978943982[/C][C]-2.42987756385864[/C][C]323.131332353283[/C][C]-0.247868711532408[/C][/ROW]
[ROW][C]41[/C][C]9019[/C][C]9439.20439028914[/C][C]-1.34601321892296[/C][C]-421.450841987691[/C][C]0.562529215139126[/C][/ROW]
[ROW][C]42[/C][C]9672[/C][C]9526.26882372462[/C][C]-0.047847268485069[/C][C]144.296967051820[/C][C]0.648008784330493[/C][/ROW]
[ROW][C]43[/C][C]9206[/C][C]9354.29953962991[/C][C]-2.45049008702385[/C][C]-145.485846509328[/C][C]-1.27002892558780[/C][/ROW]
[ROW][C]44[/C][C]9069[/C][C]9291.74798991872[/C][C]-3.19410451169397[/C][C]-221.756334733109[/C][C]-0.446692690210052[/C][/ROW]
[ROW][C]45[/C][C]9788[/C][C]9527.3417025869[/C][C]-0.719835326329942[/C][C]256.696550113955[/C][C]1.78077301638989[/C][/ROW]
[ROW][C]46[/C][C]10312[/C][C]9624.51109713784[/C][C]0.096027957588888[/C][C]685.859787070915[/C][C]0.731020408457668[/C][/ROW]
[ROW][C]47[/C][C]10105[/C][C]9836.2318452694[/C][C]1.49713127116114[/C][C]265.240934276026[/C][C]1.58093275393686[/C][/ROW]
[ROW][C]48[/C][C]9863[/C][C]9824.50498151234[/C][C]1.42373578282638[/C][C]38.7154690254109[/C][C]-0.0987486279718978[/C][/ROW]
[ROW][C]49[/C][C]9656[/C][C]9747.28483630863[/C][C]0.997551636934981[/C][C]-89.9762517528914[/C][C]-0.586130706721582[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]9801.80000511374[/C][C]1.33567199133678[/C][C]-507.6861449915[/C][C]0.397175228268162[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]9816.17047588593[/C][C]1.43942942766346[/C][C]129.615247558962[/C][C]0.096192398012906[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9676.92268320296[/C][C]0.0656899687826051[/C][C]26.3750423304587[/C][C]-1.03376292817842[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9568.96478398853[/C][C]-1.13769998998178[/C][C]-518.205210398125[/C][C]-0.793200338741033[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]9686.23967611857[/C][C]0.250703430598917[/C][C]501.827789847443[/C][C]0.871968273634657[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9795.05731572663[/C][C]1.49455614989545[/C][C]-90.8376573032048[/C][C]0.803048404502364[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9964.83782038248[/C][C]3.26280650936327[/C][C]-202.611880267706[/C][C]1.24985211411604[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9928.37450467282[/C][C]2.89852693427271[/C][C]-34.7169593287432[/C][C]-0.295842154417687[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]9736.86782675455[/C][C]1.38879256112751[/C][C]260.358382812888[/C][C]-1.44974137909044[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]9841.25817106834[/C][C]2.06803443808084[/C][C]590.030267379287[/C][C]0.768443002439589[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]9925.0086381795[/C][C]2.54992803660270[/C][C]146.634013698928[/C][C]0.609127917613946[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]10052.8594328691[/C][C]3.28013450841469[/C][C]57.0615418148054[/C][C]0.932957672188076[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9964.6145447027[/C][C]2.69702650226427[/C][C]-697.100793514692[/C][C]-0.679604846605334[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9805.62721745523[/C][C]1.50975423348385[/C][C]17.0356306578212[/C][C]-1.19665516212745[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]9863.86565054821[/C][C]1.98797486111284[/C][C]232.203534808297[/C][C]0.418831124021567[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]9841.26216105916[/C][C]1.75920451005729[/C][C]-725.859346237903[/C][C]-0.181460233955533[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]9901.6122055634[/C][C]2.3291757362672[/C][C]508.427267971591[/C][C]0.432962988604558[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9895.06325750294[/C][C]2.24374033344239[/C][C]-216.917323822984[/C][C]-0.0657746447297351[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]10131.5737565930[/C][C]4.36484884925401[/C][C]272.562933780309[/C][C]1.74014104808724[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]10180.0375683428[/C][C]4.72613312181003[/C][C]-27.7668886134338[/C][C]0.328215053005083[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]10200.9474765953[/C][C]4.84359730176286[/C][C]166.784335743943[/C][C]0.120589854460093[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]10156.2839481808[/C][C]4.52340528299437[/C][C]425.537312848946[/C][C]-0.369042073997497[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]10267.0495364991[/C][C]5.15994571093786[/C][C]328.187955970625[/C][C]0.79171878968827[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]10388.6627388489[/C][C]5.84920868267741[/C][C]289.407355389986[/C][C]0.86687876213114[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]10404.4257558570[/C][C]5.91101679723834[/C][C]-666.58971639717[/C][C]0.0736698387168638[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]10104.8935041481[/C][C]3.82775190582163[/C][C]-543.854804388791[/C][C]-2.26531949085490[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107181&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107181&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
197009700000
290819420.62455281495-4.5890426297954-334.626271902039-1.94825201291792
390849148.2077517124-22.2303618909331-61.4190688909295-1.35872117365695
497439343.40355518114-8.8193715342712396.7083849351671.35850437495548
585879095.70202034107-19.3928504723650-504.874476723729-1.70098600051497
697319271.86407345205-13.6836322275629455.7850360802611.45024067345611
795639434.40844999122-10.0399239606939125.5140932926071.31890702427521
899989698.13795425041-5.33632031363061295.0604656373962.05127007984087
994379674.89595659924-5.62859801659247-237.581978828241-0.134046595619623
10100389807.97758753645-3.35094274695094227.5934809566931.03721999324648
1199189889.12380139853-1.9454851109925027.39829186792180.631326425854686
1292529667.85545584089-5.6050188046502-412.021472076373-1.63810580704716
1397379552.0412545418-3.61040227495166186.943252254166-0.867707542115775
1490359409.52966529666-2.68610765998603-372.045359372442-1.07943661012622
1591339311.8021903386-4.17279616924874-177.283195680046-0.672370905652503
1694879154.66553084616-8.34497094258953334.647008087387-1.05021514512539
1787009193.55821813077-6.9846990075786-494.2897316040540.333489306211107
1896279253.4586044127-5.31403601802089372.4637230323580.487484467042734
1989479171.21697982802-6.8558937647209-222.945610987148-0.570707897539624
2092839097.55670913712-7.93365258961034186.560772068260-0.499253744665062
2188299105.02135657368-7.72594706235133-276.2802347638930.115351198940874
2299479330.49146897202-5.08649185514184612.5802749847941.74711149030179
2396289424.08534386672-4.23982836968727202.2503907528360.738833540386894
2493189518.27043512378-3.77030289426937-201.9348515495240.737829998929544
2596059459.6525333162-3.83436376185997146.280162888563-0.413336377929872
2686409243.96375879017-4.5673021732229-600.392639825714-1.58347703966637
2792149214.56358112121-4.81574726388101-0.157408460500335-0.181087518937939
2895679224.46897353754-4.57916825516746342.2964654638070.105542558958506
2985479155.45177696454-5.81034756066413-607.427922744883-0.463382439639212
3091858982.89970567253-8.99400504744033204.786549860404-1.21588207581558
3194709195.98429437877-5.18028867497022270.3794909051941.64089527434201
3291239183.67228549114-5.28384289359201-60.5541585213881-0.0531211458806797
3392789382.238653821-2.87829799995906-107.6368611160591.52376779855306
34101709524.17096509914-1.54558636518991643.4076573815851.08343245497791
3594349459.87903816316-1.97218082081931-24.8282625863602-0.469477382632173
3696559554.28603843822-1.4971418164170599.09844209023150.721210944559087
3794299426.43881059238-2.046589058599064.67679322160564-0.944253596916056
3887399353.99551485272-2.45262076854173-613.825140780271-0.522791335292416
3995529400.4538714729-2.0255327633396150.7437093160060.359400179017050
4096879364.41978943982-2.42987756385864323.131332353283-0.247868711532408
4190199439.20439028914-1.34601321892296-421.4508419876910.562529215139126
4296729526.26882372462-0.047847268485069144.2969670518200.648008784330493
4392069354.29953962991-2.45049008702385-145.485846509328-1.27002892558780
4490699291.74798991872-3.19410451169397-221.756334733109-0.446692690210052
4597889527.3417025869-0.719835326329942256.6965501139551.78077301638989
46103129624.511097137840.096027957588888685.8597870709150.731020408457668
47101059836.23184526941.49713127116114265.2409342760261.58093275393686
4898639824.504981512341.4237357828263838.7154690254109-0.0987486279718978
4996569747.284836308630.997551636934981-89.9762517528914-0.586130706721582
5092959801.800005113741.33567199133678-507.68614499150.397175228268162
5199469816.170475885931.43942942766346129.6152475589620.096192398012906
5297019676.922683202960.065689968782605126.3750423304587-1.03376292817842
5390499568.96478398853-1.13769998998178-518.205210398125-0.793200338741033
54101909686.239676118570.250703430598917501.8277898474430.871968273634657
5597069795.057315726631.49455614989545-90.83765730320480.803048404502364
5697659964.837820382483.26280650936327-202.6118802677061.24985211411604
5798939928.374504672822.89852693427271-34.7169593287432-0.295842154417687
5899949736.867826754551.38879256112751260.358382812888-1.44974137909044
59104339841.258171068342.06803443808084590.0302673792870.768443002439589
60100739925.00863817952.54992803660270146.6340136989280.609127917613946
611011210052.85943286913.2801345084146957.06154181480540.932957672188076
6292669964.61454470272.69702650226427-697.100793514692-0.679604846605334
6398209805.627217455231.5097542334838517.0356306578212-1.19665516212745
64100979863.865650548211.98797486111284232.2035348082970.418831124021567
6591159841.262161059161.75920451005729-725.859346237903-0.181460233955533
66104119901.61220556342.3291757362672508.4272679715910.432962988604558
6796789895.063257502942.24374033344239-216.917323822984-0.0657746447297351
681040810131.57375659304.36484884925401272.5629337803091.74014104808724
691015310180.03756834284.72613312181003-27.76688861343380.328215053005083
701036810200.94747659534.84359730176286166.7843357439430.120589854460093
711058110156.28394818084.52340528299437425.537312848946-0.369042073997497
721059710267.04953649915.15994571093786328.1879559706250.79171878968827
731068010388.66273884895.84920868267741289.4073553899860.86687876213114
74973810404.42575585705.91101679723834-666.589716397170.0736698387168638
75955610104.89350414813.82775190582163-543.854804388791-2.26531949085490



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
nx <- length(x)
x <- ts(x,frequency=par1)
m <- StructTS(x,type='BSM')
m$coef
m$fitted
m$resid
mylevel <- as.numeric(m$fitted[,'level'])
myslope <- as.numeric(m$fitted[,'slope'])
myseas <- as.numeric(m$fitted[,'sea'])
myresid <- as.numeric(m$resid)
myfit <- mylevel+myseas
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time')
grid()
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel,main='Level')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test5.png')
op <- par(mfrow = c(2,2))
hist(m$resid,main='Residual Histogram')
plot(density(m$resid),main='Residual Kernel Density')
qqnorm(m$resid,main='Residual Normal QQ Plot')
qqline(m$resid)
plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Structural Time Series Model',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Level',header=TRUE)
a<-table.element(a,'Slope',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Stand. Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,mylevel[i])
a<-table.element(a,myslope[i])
a<-table.element(a,myseas[i])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')