Free Statistics

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Author's title

Author*Unverified author*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationThu, 06 Mar 2008 09:52:55 -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/2008/Mar/06/t1204824677qpuklllbvmmq7ks.htm/, Retrieved Fri, 26 Apr 2024 08:31:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=10033, Retrieved Fri, 26 Apr 2024 08:31:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact474
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] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R  D      [Structural Time Series Models] [HPC Retail Sales] [2008-03-08 11:12:03] [74be16979710d4c4e7c6647856088456]
- R  D      [Structural Time Series Models] [HPC Retail Sales] [2008-03-08 11:33:35] [74be16979710d4c4e7c6647856088456]
- RM D        [Structural Time Series Models] [] [2010-11-26 10:41:09] [d39e5c40c631ed6c22677d2e41dbfc7d]
- R             [Structural Time Series Models] [WS8] [2011-12-01 14:36:00] [bab4e1d4a779bb46523d87231e2a2e96]
- RMP           [Exponential Smoothing] [Exponential smoot...] [2011-12-01 14:49:47] [bab4e1d4a779bb46523d87231e2a2e96]
- RMP           [Exponential Smoothing] [Exponential smoot...] [2011-12-01 14:53:25] [bab4e1d4a779bb46523d87231e2a2e96]
-  M D        [Structural Time Series Models] [WS8 residu's] [2010-11-28 15:36:37] [65eb19f81eab2b6e672eafaed2a27190]
-  M D        [Structural Time Series Models] [WS8 Structural Ti...] [2010-11-29 10:27:42] [f4dc4aa51d65be851b8508203d9f6001]
-    D          [Structural Time Series Models] [Structural Time S...] [2010-12-17 16:42:30] [f4dc4aa51d65be851b8508203d9f6001]
- RM D        [Multiple Regression] [Basic Structural ...] [2010-11-29 14:58:28] [2843717cd92615903379c14ebee3c5df]
-  M D        [Structural Time Series Models] [Basis Structual T...] [2010-11-29 15:22:20] [2843717cd92615903379c14ebee3c5df]
-  M D        [Structural Time Series Models] [] [2010-11-29 19:38:40] [de55ccbf69577500a5f46ed42a101114]
-  M D        [Structural Time Series Models] [model 2 ws 8] [2010-11-29 22:32:28] [f74ab32c8351972dfcced0581a68b6bb]
-               [Structural Time Series Models] [] [2010-11-30 16:45:24] [fc9068db680cd880760a7c0fccd81a61]
-  M D        [Structural Time Series Models] [] [2010-11-29 23:28:06] [2db311435ed525bc1ed0ddec922afb8f]
-  M D        [Structural Time Series Models] [Structural decomp...] [2010-11-30 15:46:51] [cbb1f7583f1ea41fcafd5f9709bd0e0a]
-  M D        [Structural Time Series Models] [ws8 structural ti...] [2010-11-30 17:53:54] [74be16979710d4c4e7c6647856088456]
- RMPD        [Multiple Regression] [ws8 multiple regr...] [2010-11-30 18:01:16] [74be16979710d4c4e7c6647856088456]
-    D          [Multiple Regression] [PaperTimDamen] [2011-02-03 09:33:21] [74be16979710d4c4e7c6647856088456]
- RMPD        [Multiple Regression] [ws8 multiple regr...] [2010-11-30 18:39:59] [74be16979710d4c4e7c6647856088456]
- R PD          [Multiple Regression] [PaperTimDamen] [2011-02-03 09:54:47] [74be16979710d4c4e7c6647856088456]
-   PD            [Multiple Regression] [PaperTimDamen] [2011-02-03 11:54:00] [74be16979710d4c4e7c6647856088456]
- R PD          [Multiple Regression] [PaperTimDamen] [2011-02-03 10:03:20] [74be16979710d4c4e7c6647856088456]
- RMPD        [Multiple Regression] [ws8 multiple regr...] [2010-11-30 20:45:11] [74be16979710d4c4e7c6647856088456]
-  M D        [Structural Time Series Models] [] [2010-12-07 12:42:20] [b2f924a86c4fbfa8afa1027f3839f526]
-  M D        [Structural Time Series Models] [] [2010-12-07 12:42:20] [b2f924a86c4fbfa8afa1027f3839f526]
- RMP           [Exponential Smoothing] [] [2010-12-07 12:46:53] [b2f924a86c4fbfa8afa1027f3839f526]
- RMPD            [Multiple Regression] [] [2010-12-07 12:51:30] [b2f924a86c4fbfa8afa1027f3839f526]
-    D              [Multiple Regression] [] [2010-12-09 16:17:21] [b2f924a86c4fbfa8afa1027f3839f526]
-    D            [Exponential Smoothing] [] [2010-12-09 16:12:25] [b2f924a86c4fbfa8afa1027f3839f526]
-    D          [Structural Time Series Models] [] [2010-12-09 16:08:50] [b2f924a86c4fbfa8afa1027f3839f526]
- R               [Structural Time Series Models] [] [2010-12-10 12:32:01] [b2f924a86c4fbfa8afa1027f3839f526]
- RM D        [Structural Time Series Models] [] [2010-12-07 13:10:14] [253127ae8da904b75450fbd69fe4eb21]
-  M D        [Structural Time Series Models] [Workshop 8 - STSM] [2010-12-07 13:12:32] [ed447cc2ebcc70947ad11d93fa385845]
-  M D        [Structural Time Series Models] [] [2010-12-07 13:18:37] [253127ae8da904b75450fbd69fe4eb21]
-  M D        [Structural Time Series Models] [WS5] [2010-12-07 13:51:52] [fa854ea294f510d944d2dbf77761bfce]
-  M D        [Structural Time Series Models] [WS5] [2010-12-07 13:51:52] [fa854ea294f510d944d2dbf77761bfce]
-               [Structural Time Series Models] [ws5q5] [2010-12-08 19:32:28] [df61ce38492c371f14c407a12b3bb2eb]
- R               [Structural Time Series Models] [ws5q5] [2010-12-09 18:54:41] [a2638725f7f7c6bd63902ba17eba666b]
-                 [Structural Time Series Models] [ws5q5] [2010-12-09 19:20:09] [c4f608d390ad7371b1365a9b84541edb]
-                 [Structural Time Series Models] [Workshop 5 assign...] [2010-12-10 00:01:25] [17d39bb3ec485d4ce196f61215d11ba1]
-                 [Structural Time Series Models] [WSvraag5] [2010-12-10 13:55:58] [9c3137400ced3280b419f1e434c29e1d]
-                 [Structural Time Series Models] [] [2010-12-10 19:01:52] [c420bdd199bcbe079f7d532ca3855317]
-  M D        [Structural Time Series Models] [] [2010-12-07 19:00:52] [1ec36cc0fd92fd0f07d0b885ce2c369b]
-  M D        [Structural Time Series Models] [WS5 - monthly bir...] [2010-12-08 15:29:30] [8ed0bd3560b9ca2814a2ed0a29182575]
-    D          [Structural Time Series Models] [STSM Dollar] [2010-12-26 13:32:25] [8ed0bd3560b9ca2814a2ed0a29182575]
-    D          [Structural Time Series Models] [STSM Yuan] [2010-12-26 13:34:39] [8ed0bd3560b9ca2814a2ed0a29182575]
- RM D        [Structural Time Series Models] [] [2010-12-08 18:48:54] [bcc4ad4a6c0f95d5b548de29638ac6c2]

[Truncated]
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Dataseries X:
13328
12873
14000
13477
14237
13674
13529
14058
12975
14326
14008
16193
14483
14011
15057
14884
15414
14440
14900
15074
14442
15307
14938
17193
15528
14765
15838
15723
16150
15486
15986
15983
15692
16490
15686
18897
16316
15636
17163
16534
16518
16375
16290
16352
15943
16362
16393
19051
16747
16320
17910
16961
17480
17049
16879
17473
16998
17307
17418
20169
17871
17226
19062
17804
19100
18522
18060
18869
18127
18871
18890
21263
19547
18450
20254
19240
20216
19420
19415
20018
18652
19978
19509
21971




Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=10033&T=0

[TABLE]
[ROW][C]Summary of compuational 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]4 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=10033&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10033&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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedFittedLevelSlopeSeasonalResiduals
1133281332813328000
21287312876.825789185113200.68194781453.82578918514634-327.68194781448-1.30937963159356
31400014022.003195416113460.239175026122.0031954161445539.7608249738651.31765525270691
41347713503.28510594313535.980420899126.285105942988-58.98042089910190.311977226123143
51423714279.187168178013789.367306105442.1871681780315447.6326938945811.58162610681454
61367413716.330053584513834.089406732442.3300535845046-160.0894067323570.0192376923718027
71352913565.776204579913756.221415002136.7762045798851-227.221415002069-0.937057013630214
81405814096.052962469613824.126311763838.052962469584233.8736882362300.243826417267901
91297513001.984105796413582.498775267426.9841057964398-607.498775267415-2.18590323595731
101432614357.822589832413729.720459629231.8225898323746596.2795403707720.935766933751675
111400814044.349976730413869.974239000436.3499767303937138.0257609995870.840035252978843
121619316261.960045098214657.836502122868.96004509823561535.163497877235.79802749363536
131448314556.653080951614941.154811152173.6530809516055-458.1548111520811.70324729921804
141401114083.566100021114973.533754067972.56610002109-962.533754067875-0.330836466619584
151505715123.942435425614910.422651392366.9424354256193146.577348607663-1.01555961592100
161488414950.396541018114966.957094087366.3965410181129-82.9570940873432-0.0738137548231807
171541415477.559413134314983.556371205163.5594131342866430.443628794902-0.353622835038707
181444014491.857643176214842.047094907351.8576431761596-402.04709490728-1.49507756240521
191490014950.965892265014877.713299010550.96589226503822.2867009895442-0.120639961851066
201507415121.10496519514855.421556101747.1049651949951218.578443898257-0.551266726939755
211444214493.860534817114996.315769437551.8605348170913-554.3157694374570.706960235998102
221530715360.771601029415087.067621877253.7716010293883219.9323781228000.292538866816767
231493814995.940245514515228.059675091957.9402455145203-290.0596750918540.654261787956202
241719317259.299388613715465.375070008966.29938861369761727.624929991061.34474028856297
251552815602.018587608815699.544232176374.0185876088331-171.5442321763311.26208555258566
261476514838.806527430415769.063338417073.806527430363-1004.06333841696-0.0337794511447212
271583815908.205950248915770.367127643670.205950248940967.6328723564073-0.537689018234094
281572315789.407903913915767.917838339166.4079039138715-44.9178383390633-0.531044660759293
291615016210.536106885315725.662845630060.5361068853177424.337154370023-0.790177339025636
301548615546.212498884715780.286200526860.2124988847452-294.286200526844-0.0432200309184316
311598616048.226208361715877.398174020462.22620836171108.6018259795550.271983421683458
321598316044.639751111915928.746878529861.639751111918954.2531214702492-0.0806101036648515
331569215759.479736029016100.431723783267.4797360289696-408.4317237832090.81632027217282
341649016563.446724025616282.106735345273.4467240256002207.8932646548390.845799882141688
351568615757.887372620116325.337478227571.8873726200522-639.337478227512-0.223472602865751
361889718982.409756833616661.113104020385.40975683355732235.886895979681.95194819402148
371631616400.205388308116723.023036009384.2053883081234-407.023036009263-0.174005251309701
381563615715.375971044316713.717226233479.3759710442796-1077.71722623338-0.692066109425585
391716317244.405387988416831.896475851781.4053879883748331.1035241482940.286209760715423
401653416609.196748735216796.136125745675.1967487352262-262.136125745639-0.86037097664727
411651816578.451407353816595.816779431360.4514073538403-77.8167794313284-2.01850104025196
421637516432.410115160716599.730785008857.4101151607113-224.730785008773-0.414637749542108
431629016339.684777258216513.587846713049.6847772582255-223.587846712959-1.05576587816445
441635216397.456173405016484.454074275145.4561734049545-132.454074275099-0.580951664466268
451594315986.384904787316491.109234458843.3849047873067-548.109234458764-0.286122147509981
461636216399.815399736716429.598346882137.815399736733-67.598346882089-0.772925383912537
471639316441.425955957916668.133255975648.4259559578775-275.1332559755951.47794698241834
481905119103.162497450916787.41597010552.16249745091092263.584029895000.521772960478365
491674716803.811104894116927.734024717156.8111048941409-180.7340247171030.649499979507535
501632016383.604249908217113.092879123763.6042499082173-793.0928791237350.947131566555392
511791017978.586144416617270.641819616168.5861444166098639.3581803838520.69150670359207
521696117024.724632660317247.892451613563.7246326602895-286.892451613550-0.6713115311164
531748017544.445436162217325.119147574564.44543616219154.8808524255410.0991472221876955
541704917109.302270900717312.085857678860.3022709007103-263.085857678771-0.569037817998455
551687916932.347046733617242.367161210853.3470467335768-363.367161210762-0.955814972472107
561747317529.330033064117351.520995702956.3300330641291121.4790042971490.410589954146277
571699817058.114324207217478.752768527160.1143242071860-480.7527685271210.521754101189441
581730717368.691898610717568.474110901461.691898610739-261.4741109013570.217805644509610
591741817483.052478450017693.326983682765.0524784499592-275.3269836826860.464495230746059
602016920239.467949264917860.249436108470.46794926488242308.750563891640.749166786818708
611787117946.726144721018029.640552299175.7261447209669-158.6405522990910.727666511577185
621722617302.000458905918110.524511571076.0004589058506-884.524511571040.037944235448267
631906219140.557540384718234.557270025478.5575403847418827.4427299745510.353264630573422
641780417878.77705266318242.178761141874.7770526629889-438.178761141842-0.521457348362567
651910019182.777791725918466.945732505982.7777917258623633.054267494051.10218502294869
661852218610.140360217418650.201525230588.1403602173717-128.2015252305060.73835603786959
671806018145.545466737918689.728781975285.5454667378949-629.728781975174-0.357337570795217
681886918954.627320804518776.808002847085.627320804457692.19199715304160.0112775352566685
691812718209.548084169818804.713585938182.5480841697832-677.713585938127-0.42445991711634
701887118958.551806136118981.106581688887.5518061361308-110.1065816888410.690013654175918
711889018982.495672548919161.423566353392.4956725488576-271.4235663532570.681978362131513
722126321353.076216896719208.507758233190.076216896682054.49224176685-0.333844069038362
731954719643.554899578219420.193348132696.5548995781493126.8066518674270.894098448192825
741845018545.949572015219505.387402129295.9495720151657-1055.38740212923-0.0835331354626137
752025420347.156264971819548.926510694993.156264971797705.073489305116-0.385335983346102
761924019335.179909819819680.040091267495.179909819775-440.0400912674120.279026604563751
772021620309.333498126019740.596376184593.3334981259738475.403623815547-0.254484208719289
781942019507.398485136719722.656344817787.3984851366954-302.656344817737-0.817847173612272
791941519504.781836279519854.736950351389.781836279546-439.7369503512840.328439734536982
802001820108.460129309919957.235716873590.460129309855960.76428312645370.0934873708840648
811865218732.262607332919856.486091141780.262607332886-1204.48609114171-1.40571677346299
821997820059.125926290819952.938898406981.12592629082225.06110159313020.119020635507625
831950919586.419511099119964.546753736877.4195110990556-455.54675373678-0.51102528352137
842197122046.531889816420006.558195266375.53188981643711964.44180473367-0.260279917278426

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Fitted & Level & Slope & Seasonal & Residuals \tabularnewline
1 & 13328 & 13328 & 13328 & 0 & 0 & 0 \tabularnewline
2 & 12873 & 12876.8257891851 & 13200.6819478145 & 3.82578918514634 & -327.68194781448 & -1.30937963159356 \tabularnewline
3 & 14000 & 14022.0031954161 & 13460.2391750261 & 22.0031954161445 & 539.760824973865 & 1.31765525270691 \tabularnewline
4 & 13477 & 13503.285105943 & 13535.9804208991 & 26.285105942988 & -58.9804208991019 & 0.311977226123143 \tabularnewline
5 & 14237 & 14279.1871681780 & 13789.3673061054 & 42.1871681780315 & 447.632693894581 & 1.58162610681454 \tabularnewline
6 & 13674 & 13716.3300535845 & 13834.0894067324 & 42.3300535845046 & -160.089406732357 & 0.0192376923718027 \tabularnewline
7 & 13529 & 13565.7762045799 & 13756.2214150021 & 36.7762045798851 & -227.221415002069 & -0.937057013630214 \tabularnewline
8 & 14058 & 14096.0529624696 & 13824.1263117638 & 38.052962469584 & 233.873688236230 & 0.243826417267901 \tabularnewline
9 & 12975 & 13001.9841057964 & 13582.4987752674 & 26.9841057964398 & -607.498775267415 & -2.18590323595731 \tabularnewline
10 & 14326 & 14357.8225898324 & 13729.7204596292 & 31.8225898323746 & 596.279540370772 & 0.935766933751675 \tabularnewline
11 & 14008 & 14044.3499767304 & 13869.9742390004 & 36.3499767303937 & 138.025760999587 & 0.840035252978843 \tabularnewline
12 & 16193 & 16261.9600450982 & 14657.8365021228 & 68.9600450982356 & 1535.16349787723 & 5.79802749363536 \tabularnewline
13 & 14483 & 14556.6530809516 & 14941.1548111521 & 73.6530809516055 & -458.154811152081 & 1.70324729921804 \tabularnewline
14 & 14011 & 14083.5661000211 & 14973.5337540679 & 72.56610002109 & -962.533754067875 & -0.330836466619584 \tabularnewline
15 & 15057 & 15123.9424354256 & 14910.4226513923 & 66.9424354256193 & 146.577348607663 & -1.01555961592100 \tabularnewline
16 & 14884 & 14950.3965410181 & 14966.9570940873 & 66.3965410181129 & -82.9570940873432 & -0.0738137548231807 \tabularnewline
17 & 15414 & 15477.5594131343 & 14983.5563712051 & 63.5594131342866 & 430.443628794902 & -0.353622835038707 \tabularnewline
18 & 14440 & 14491.8576431762 & 14842.0470949073 & 51.8576431761596 & -402.04709490728 & -1.49507756240521 \tabularnewline
19 & 14900 & 14950.9658922650 & 14877.7132990105 & 50.965892265038 & 22.2867009895442 & -0.120639961851066 \tabularnewline
20 & 15074 & 15121.104965195 & 14855.4215561017 & 47.1049651949951 & 218.578443898257 & -0.551266726939755 \tabularnewline
21 & 14442 & 14493.8605348171 & 14996.3157694375 & 51.8605348170913 & -554.315769437457 & 0.706960235998102 \tabularnewline
22 & 15307 & 15360.7716010294 & 15087.0676218772 & 53.7716010293883 & 219.932378122800 & 0.292538866816767 \tabularnewline
23 & 14938 & 14995.9402455145 & 15228.0596750919 & 57.9402455145203 & -290.059675091854 & 0.654261787956202 \tabularnewline
24 & 17193 & 17259.2993886137 & 15465.3750700089 & 66.2993886136976 & 1727.62492999106 & 1.34474028856297 \tabularnewline
25 & 15528 & 15602.0185876088 & 15699.5442321763 & 74.0185876088331 & -171.544232176331 & 1.26208555258566 \tabularnewline
26 & 14765 & 14838.8065274304 & 15769.0633384170 & 73.806527430363 & -1004.06333841696 & -0.0337794511447212 \tabularnewline
27 & 15838 & 15908.2059502489 & 15770.3671276436 & 70.2059502489409 & 67.6328723564073 & -0.537689018234094 \tabularnewline
28 & 15723 & 15789.4079039139 & 15767.9178383391 & 66.4079039138715 & -44.9178383390633 & -0.531044660759293 \tabularnewline
29 & 16150 & 16210.5361068853 & 15725.6628456300 & 60.5361068853177 & 424.337154370023 & -0.790177339025636 \tabularnewline
30 & 15486 & 15546.2124988847 & 15780.2862005268 & 60.2124988847452 & -294.286200526844 & -0.0432200309184316 \tabularnewline
31 & 15986 & 16048.2262083617 & 15877.3981740204 & 62.22620836171 & 108.601825979555 & 0.271983421683458 \tabularnewline
32 & 15983 & 16044.6397511119 & 15928.7468785298 & 61.6397511119189 & 54.2531214702492 & -0.0806101036648515 \tabularnewline
33 & 15692 & 15759.4797360290 & 16100.4317237832 & 67.4797360289696 & -408.431723783209 & 0.81632027217282 \tabularnewline
34 & 16490 & 16563.4467240256 & 16282.1067353452 & 73.4467240256002 & 207.893264654839 & 0.845799882141688 \tabularnewline
35 & 15686 & 15757.8873726201 & 16325.3374782275 & 71.8873726200522 & -639.337478227512 & -0.223472602865751 \tabularnewline
36 & 18897 & 18982.4097568336 & 16661.1131040203 & 85.4097568335573 & 2235.88689597968 & 1.95194819402148 \tabularnewline
37 & 16316 & 16400.2053883081 & 16723.0230360093 & 84.2053883081234 & -407.023036009263 & -0.174005251309701 \tabularnewline
38 & 15636 & 15715.3759710443 & 16713.7172262334 & 79.3759710442796 & -1077.71722623338 & -0.692066109425585 \tabularnewline
39 & 17163 & 17244.4053879884 & 16831.8964758517 & 81.4053879883748 & 331.103524148294 & 0.286209760715423 \tabularnewline
40 & 16534 & 16609.1967487352 & 16796.1361257456 & 75.1967487352262 & -262.136125745639 & -0.86037097664727 \tabularnewline
41 & 16518 & 16578.4514073538 & 16595.8167794313 & 60.4514073538403 & -77.8167794313284 & -2.01850104025196 \tabularnewline
42 & 16375 & 16432.4101151607 & 16599.7307850088 & 57.4101151607113 & -224.730785008773 & -0.414637749542108 \tabularnewline
43 & 16290 & 16339.6847772582 & 16513.5878467130 & 49.6847772582255 & -223.587846712959 & -1.05576587816445 \tabularnewline
44 & 16352 & 16397.4561734050 & 16484.4540742751 & 45.4561734049545 & -132.454074275099 & -0.580951664466268 \tabularnewline
45 & 15943 & 15986.3849047873 & 16491.1092344588 & 43.3849047873067 & -548.109234458764 & -0.286122147509981 \tabularnewline
46 & 16362 & 16399.8153997367 & 16429.5983468821 & 37.815399736733 & -67.598346882089 & -0.772925383912537 \tabularnewline
47 & 16393 & 16441.4259559579 & 16668.1332559756 & 48.4259559578775 & -275.133255975595 & 1.47794698241834 \tabularnewline
48 & 19051 & 19103.1624974509 & 16787.415970105 & 52.1624974509109 & 2263.58402989500 & 0.521772960478365 \tabularnewline
49 & 16747 & 16803.8111048941 & 16927.7340247171 & 56.8111048941409 & -180.734024717103 & 0.649499979507535 \tabularnewline
50 & 16320 & 16383.6042499082 & 17113.0928791237 & 63.6042499082173 & -793.092879123735 & 0.947131566555392 \tabularnewline
51 & 17910 & 17978.5861444166 & 17270.6418196161 & 68.5861444166098 & 639.358180383852 & 0.69150670359207 \tabularnewline
52 & 16961 & 17024.7246326603 & 17247.8924516135 & 63.7246326602895 & -286.892451613550 & -0.6713115311164 \tabularnewline
53 & 17480 & 17544.4454361622 & 17325.1191475745 & 64.44543616219 & 154.880852425541 & 0.0991472221876955 \tabularnewline
54 & 17049 & 17109.3022709007 & 17312.0858576788 & 60.3022709007103 & -263.085857678771 & -0.569037817998455 \tabularnewline
55 & 16879 & 16932.3470467336 & 17242.3671612108 & 53.3470467335768 & -363.367161210762 & -0.955814972472107 \tabularnewline
56 & 17473 & 17529.3300330641 & 17351.5209957029 & 56.3300330641291 & 121.479004297149 & 0.410589954146277 \tabularnewline
57 & 16998 & 17058.1143242072 & 17478.7527685271 & 60.1143242071860 & -480.752768527121 & 0.521754101189441 \tabularnewline
58 & 17307 & 17368.6918986107 & 17568.4741109014 & 61.691898610739 & -261.474110901357 & 0.217805644509610 \tabularnewline
59 & 17418 & 17483.0524784500 & 17693.3269836827 & 65.0524784499592 & -275.326983682686 & 0.464495230746059 \tabularnewline
60 & 20169 & 20239.4679492649 & 17860.2494361084 & 70.4679492648824 & 2308.75056389164 & 0.749166786818708 \tabularnewline
61 & 17871 & 17946.7261447210 & 18029.6405522991 & 75.7261447209669 & -158.640552299091 & 0.727666511577185 \tabularnewline
62 & 17226 & 17302.0004589059 & 18110.5245115710 & 76.0004589058506 & -884.52451157104 & 0.037944235448267 \tabularnewline
63 & 19062 & 19140.5575403847 & 18234.5572700254 & 78.5575403847418 & 827.442729974551 & 0.353264630573422 \tabularnewline
64 & 17804 & 17878.777052663 & 18242.1787611418 & 74.7770526629889 & -438.178761141842 & -0.521457348362567 \tabularnewline
65 & 19100 & 19182.7777917259 & 18466.9457325059 & 82.7777917258623 & 633.05426749405 & 1.10218502294869 \tabularnewline
66 & 18522 & 18610.1403602174 & 18650.2015252305 & 88.1403602173717 & -128.201525230506 & 0.73835603786959 \tabularnewline
67 & 18060 & 18145.5454667379 & 18689.7287819752 & 85.5454667378949 & -629.728781975174 & -0.357337570795217 \tabularnewline
68 & 18869 & 18954.6273208045 & 18776.8080028470 & 85.6273208044576 & 92.1919971530416 & 0.0112775352566685 \tabularnewline
69 & 18127 & 18209.5480841698 & 18804.7135859381 & 82.5480841697832 & -677.713585938127 & -0.42445991711634 \tabularnewline
70 & 18871 & 18958.5518061361 & 18981.1065816888 & 87.5518061361308 & -110.106581688841 & 0.690013654175918 \tabularnewline
71 & 18890 & 18982.4956725489 & 19161.4235663533 & 92.4956725488576 & -271.423566353257 & 0.681978362131513 \tabularnewline
72 & 21263 & 21353.0762168967 & 19208.5077582331 & 90.07621689668 & 2054.49224176685 & -0.333844069038362 \tabularnewline
73 & 19547 & 19643.5548995782 & 19420.1933481326 & 96.5548995781493 & 126.806651867427 & 0.894098448192825 \tabularnewline
74 & 18450 & 18545.9495720152 & 19505.3874021292 & 95.9495720151657 & -1055.38740212923 & -0.0835331354626137 \tabularnewline
75 & 20254 & 20347.1562649718 & 19548.9265106949 & 93.156264971797 & 705.073489305116 & -0.385335983346102 \tabularnewline
76 & 19240 & 19335.1799098198 & 19680.0400912674 & 95.179909819775 & -440.040091267412 & 0.279026604563751 \tabularnewline
77 & 20216 & 20309.3334981260 & 19740.5963761845 & 93.3334981259738 & 475.403623815547 & -0.254484208719289 \tabularnewline
78 & 19420 & 19507.3984851367 & 19722.6563448177 & 87.3984851366954 & -302.656344817737 & -0.817847173612272 \tabularnewline
79 & 19415 & 19504.7818362795 & 19854.7369503513 & 89.781836279546 & -439.736950351284 & 0.328439734536982 \tabularnewline
80 & 20018 & 20108.4601293099 & 19957.2357168735 & 90.4601293098559 & 60.7642831264537 & 0.0934873708840648 \tabularnewline
81 & 18652 & 18732.2626073329 & 19856.4860911417 & 80.262607332886 & -1204.48609114171 & -1.40571677346299 \tabularnewline
82 & 19978 & 20059.1259262908 & 19952.9388984069 & 81.125926290822 & 25.0611015931302 & 0.119020635507625 \tabularnewline
83 & 19509 & 19586.4195110991 & 19964.5467537368 & 77.4195110990556 & -455.54675373678 & -0.51102528352137 \tabularnewline
84 & 21971 & 22046.5318898164 & 20006.5581952663 & 75.5318898164371 & 1964.44180473367 & -0.260279917278426 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=10033&T=1

[TABLE]
[ROW][C]Structural Time Series Model[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Level[/C][C]Slope[/C][C]Seasonal[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]13328[/C][C]13328[/C][C]13328[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]12873[/C][C]12876.8257891851[/C][C]13200.6819478145[/C][C]3.82578918514634[/C][C]-327.68194781448[/C][C]-1.30937963159356[/C][/ROW]
[ROW][C]3[/C][C]14000[/C][C]14022.0031954161[/C][C]13460.2391750261[/C][C]22.0031954161445[/C][C]539.760824973865[/C][C]1.31765525270691[/C][/ROW]
[ROW][C]4[/C][C]13477[/C][C]13503.285105943[/C][C]13535.9804208991[/C][C]26.285105942988[/C][C]-58.9804208991019[/C][C]0.311977226123143[/C][/ROW]
[ROW][C]5[/C][C]14237[/C][C]14279.1871681780[/C][C]13789.3673061054[/C][C]42.1871681780315[/C][C]447.632693894581[/C][C]1.58162610681454[/C][/ROW]
[ROW][C]6[/C][C]13674[/C][C]13716.3300535845[/C][C]13834.0894067324[/C][C]42.3300535845046[/C][C]-160.089406732357[/C][C]0.0192376923718027[/C][/ROW]
[ROW][C]7[/C][C]13529[/C][C]13565.7762045799[/C][C]13756.2214150021[/C][C]36.7762045798851[/C][C]-227.221415002069[/C][C]-0.937057013630214[/C][/ROW]
[ROW][C]8[/C][C]14058[/C][C]14096.0529624696[/C][C]13824.1263117638[/C][C]38.052962469584[/C][C]233.873688236230[/C][C]0.243826417267901[/C][/ROW]
[ROW][C]9[/C][C]12975[/C][C]13001.9841057964[/C][C]13582.4987752674[/C][C]26.9841057964398[/C][C]-607.498775267415[/C][C]-2.18590323595731[/C][/ROW]
[ROW][C]10[/C][C]14326[/C][C]14357.8225898324[/C][C]13729.7204596292[/C][C]31.8225898323746[/C][C]596.279540370772[/C][C]0.935766933751675[/C][/ROW]
[ROW][C]11[/C][C]14008[/C][C]14044.3499767304[/C][C]13869.9742390004[/C][C]36.3499767303937[/C][C]138.025760999587[/C][C]0.840035252978843[/C][/ROW]
[ROW][C]12[/C][C]16193[/C][C]16261.9600450982[/C][C]14657.8365021228[/C][C]68.9600450982356[/C][C]1535.16349787723[/C][C]5.79802749363536[/C][/ROW]
[ROW][C]13[/C][C]14483[/C][C]14556.6530809516[/C][C]14941.1548111521[/C][C]73.6530809516055[/C][C]-458.154811152081[/C][C]1.70324729921804[/C][/ROW]
[ROW][C]14[/C][C]14011[/C][C]14083.5661000211[/C][C]14973.5337540679[/C][C]72.56610002109[/C][C]-962.533754067875[/C][C]-0.330836466619584[/C][/ROW]
[ROW][C]15[/C][C]15057[/C][C]15123.9424354256[/C][C]14910.4226513923[/C][C]66.9424354256193[/C][C]146.577348607663[/C][C]-1.01555961592100[/C][/ROW]
[ROW][C]16[/C][C]14884[/C][C]14950.3965410181[/C][C]14966.9570940873[/C][C]66.3965410181129[/C][C]-82.9570940873432[/C][C]-0.0738137548231807[/C][/ROW]
[ROW][C]17[/C][C]15414[/C][C]15477.5594131343[/C][C]14983.5563712051[/C][C]63.5594131342866[/C][C]430.443628794902[/C][C]-0.353622835038707[/C][/ROW]
[ROW][C]18[/C][C]14440[/C][C]14491.8576431762[/C][C]14842.0470949073[/C][C]51.8576431761596[/C][C]-402.04709490728[/C][C]-1.49507756240521[/C][/ROW]
[ROW][C]19[/C][C]14900[/C][C]14950.9658922650[/C][C]14877.7132990105[/C][C]50.965892265038[/C][C]22.2867009895442[/C][C]-0.120639961851066[/C][/ROW]
[ROW][C]20[/C][C]15074[/C][C]15121.104965195[/C][C]14855.4215561017[/C][C]47.1049651949951[/C][C]218.578443898257[/C][C]-0.551266726939755[/C][/ROW]
[ROW][C]21[/C][C]14442[/C][C]14493.8605348171[/C][C]14996.3157694375[/C][C]51.8605348170913[/C][C]-554.315769437457[/C][C]0.706960235998102[/C][/ROW]
[ROW][C]22[/C][C]15307[/C][C]15360.7716010294[/C][C]15087.0676218772[/C][C]53.7716010293883[/C][C]219.932378122800[/C][C]0.292538866816767[/C][/ROW]
[ROW][C]23[/C][C]14938[/C][C]14995.9402455145[/C][C]15228.0596750919[/C][C]57.9402455145203[/C][C]-290.059675091854[/C][C]0.654261787956202[/C][/ROW]
[ROW][C]24[/C][C]17193[/C][C]17259.2993886137[/C][C]15465.3750700089[/C][C]66.2993886136976[/C][C]1727.62492999106[/C][C]1.34474028856297[/C][/ROW]
[ROW][C]25[/C][C]15528[/C][C]15602.0185876088[/C][C]15699.5442321763[/C][C]74.0185876088331[/C][C]-171.544232176331[/C][C]1.26208555258566[/C][/ROW]
[ROW][C]26[/C][C]14765[/C][C]14838.8065274304[/C][C]15769.0633384170[/C][C]73.806527430363[/C][C]-1004.06333841696[/C][C]-0.0337794511447212[/C][/ROW]
[ROW][C]27[/C][C]15838[/C][C]15908.2059502489[/C][C]15770.3671276436[/C][C]70.2059502489409[/C][C]67.6328723564073[/C][C]-0.537689018234094[/C][/ROW]
[ROW][C]28[/C][C]15723[/C][C]15789.4079039139[/C][C]15767.9178383391[/C][C]66.4079039138715[/C][C]-44.9178383390633[/C][C]-0.531044660759293[/C][/ROW]
[ROW][C]29[/C][C]16150[/C][C]16210.5361068853[/C][C]15725.6628456300[/C][C]60.5361068853177[/C][C]424.337154370023[/C][C]-0.790177339025636[/C][/ROW]
[ROW][C]30[/C][C]15486[/C][C]15546.2124988847[/C][C]15780.2862005268[/C][C]60.2124988847452[/C][C]-294.286200526844[/C][C]-0.0432200309184316[/C][/ROW]
[ROW][C]31[/C][C]15986[/C][C]16048.2262083617[/C][C]15877.3981740204[/C][C]62.22620836171[/C][C]108.601825979555[/C][C]0.271983421683458[/C][/ROW]
[ROW][C]32[/C][C]15983[/C][C]16044.6397511119[/C][C]15928.7468785298[/C][C]61.6397511119189[/C][C]54.2531214702492[/C][C]-0.0806101036648515[/C][/ROW]
[ROW][C]33[/C][C]15692[/C][C]15759.4797360290[/C][C]16100.4317237832[/C][C]67.4797360289696[/C][C]-408.431723783209[/C][C]0.81632027217282[/C][/ROW]
[ROW][C]34[/C][C]16490[/C][C]16563.4467240256[/C][C]16282.1067353452[/C][C]73.4467240256002[/C][C]207.893264654839[/C][C]0.845799882141688[/C][/ROW]
[ROW][C]35[/C][C]15686[/C][C]15757.8873726201[/C][C]16325.3374782275[/C][C]71.8873726200522[/C][C]-639.337478227512[/C][C]-0.223472602865751[/C][/ROW]
[ROW][C]36[/C][C]18897[/C][C]18982.4097568336[/C][C]16661.1131040203[/C][C]85.4097568335573[/C][C]2235.88689597968[/C][C]1.95194819402148[/C][/ROW]
[ROW][C]37[/C][C]16316[/C][C]16400.2053883081[/C][C]16723.0230360093[/C][C]84.2053883081234[/C][C]-407.023036009263[/C][C]-0.174005251309701[/C][/ROW]
[ROW][C]38[/C][C]15636[/C][C]15715.3759710443[/C][C]16713.7172262334[/C][C]79.3759710442796[/C][C]-1077.71722623338[/C][C]-0.692066109425585[/C][/ROW]
[ROW][C]39[/C][C]17163[/C][C]17244.4053879884[/C][C]16831.8964758517[/C][C]81.4053879883748[/C][C]331.103524148294[/C][C]0.286209760715423[/C][/ROW]
[ROW][C]40[/C][C]16534[/C][C]16609.1967487352[/C][C]16796.1361257456[/C][C]75.1967487352262[/C][C]-262.136125745639[/C][C]-0.86037097664727[/C][/ROW]
[ROW][C]41[/C][C]16518[/C][C]16578.4514073538[/C][C]16595.8167794313[/C][C]60.4514073538403[/C][C]-77.8167794313284[/C][C]-2.01850104025196[/C][/ROW]
[ROW][C]42[/C][C]16375[/C][C]16432.4101151607[/C][C]16599.7307850088[/C][C]57.4101151607113[/C][C]-224.730785008773[/C][C]-0.414637749542108[/C][/ROW]
[ROW][C]43[/C][C]16290[/C][C]16339.6847772582[/C][C]16513.5878467130[/C][C]49.6847772582255[/C][C]-223.587846712959[/C][C]-1.05576587816445[/C][/ROW]
[ROW][C]44[/C][C]16352[/C][C]16397.4561734050[/C][C]16484.4540742751[/C][C]45.4561734049545[/C][C]-132.454074275099[/C][C]-0.580951664466268[/C][/ROW]
[ROW][C]45[/C][C]15943[/C][C]15986.3849047873[/C][C]16491.1092344588[/C][C]43.3849047873067[/C][C]-548.109234458764[/C][C]-0.286122147509981[/C][/ROW]
[ROW][C]46[/C][C]16362[/C][C]16399.8153997367[/C][C]16429.5983468821[/C][C]37.815399736733[/C][C]-67.598346882089[/C][C]-0.772925383912537[/C][/ROW]
[ROW][C]47[/C][C]16393[/C][C]16441.4259559579[/C][C]16668.1332559756[/C][C]48.4259559578775[/C][C]-275.133255975595[/C][C]1.47794698241834[/C][/ROW]
[ROW][C]48[/C][C]19051[/C][C]19103.1624974509[/C][C]16787.415970105[/C][C]52.1624974509109[/C][C]2263.58402989500[/C][C]0.521772960478365[/C][/ROW]
[ROW][C]49[/C][C]16747[/C][C]16803.8111048941[/C][C]16927.7340247171[/C][C]56.8111048941409[/C][C]-180.734024717103[/C][C]0.649499979507535[/C][/ROW]
[ROW][C]50[/C][C]16320[/C][C]16383.6042499082[/C][C]17113.0928791237[/C][C]63.6042499082173[/C][C]-793.092879123735[/C][C]0.947131566555392[/C][/ROW]
[ROW][C]51[/C][C]17910[/C][C]17978.5861444166[/C][C]17270.6418196161[/C][C]68.5861444166098[/C][C]639.358180383852[/C][C]0.69150670359207[/C][/ROW]
[ROW][C]52[/C][C]16961[/C][C]17024.7246326603[/C][C]17247.8924516135[/C][C]63.7246326602895[/C][C]-286.892451613550[/C][C]-0.6713115311164[/C][/ROW]
[ROW][C]53[/C][C]17480[/C][C]17544.4454361622[/C][C]17325.1191475745[/C][C]64.44543616219[/C][C]154.880852425541[/C][C]0.0991472221876955[/C][/ROW]
[ROW][C]54[/C][C]17049[/C][C]17109.3022709007[/C][C]17312.0858576788[/C][C]60.3022709007103[/C][C]-263.085857678771[/C][C]-0.569037817998455[/C][/ROW]
[ROW][C]55[/C][C]16879[/C][C]16932.3470467336[/C][C]17242.3671612108[/C][C]53.3470467335768[/C][C]-363.367161210762[/C][C]-0.955814972472107[/C][/ROW]
[ROW][C]56[/C][C]17473[/C][C]17529.3300330641[/C][C]17351.5209957029[/C][C]56.3300330641291[/C][C]121.479004297149[/C][C]0.410589954146277[/C][/ROW]
[ROW][C]57[/C][C]16998[/C][C]17058.1143242072[/C][C]17478.7527685271[/C][C]60.1143242071860[/C][C]-480.752768527121[/C][C]0.521754101189441[/C][/ROW]
[ROW][C]58[/C][C]17307[/C][C]17368.6918986107[/C][C]17568.4741109014[/C][C]61.691898610739[/C][C]-261.474110901357[/C][C]0.217805644509610[/C][/ROW]
[ROW][C]59[/C][C]17418[/C][C]17483.0524784500[/C][C]17693.3269836827[/C][C]65.0524784499592[/C][C]-275.326983682686[/C][C]0.464495230746059[/C][/ROW]
[ROW][C]60[/C][C]20169[/C][C]20239.4679492649[/C][C]17860.2494361084[/C][C]70.4679492648824[/C][C]2308.75056389164[/C][C]0.749166786818708[/C][/ROW]
[ROW][C]61[/C][C]17871[/C][C]17946.7261447210[/C][C]18029.6405522991[/C][C]75.7261447209669[/C][C]-158.640552299091[/C][C]0.727666511577185[/C][/ROW]
[ROW][C]62[/C][C]17226[/C][C]17302.0004589059[/C][C]18110.5245115710[/C][C]76.0004589058506[/C][C]-884.52451157104[/C][C]0.037944235448267[/C][/ROW]
[ROW][C]63[/C][C]19062[/C][C]19140.5575403847[/C][C]18234.5572700254[/C][C]78.5575403847418[/C][C]827.442729974551[/C][C]0.353264630573422[/C][/ROW]
[ROW][C]64[/C][C]17804[/C][C]17878.777052663[/C][C]18242.1787611418[/C][C]74.7770526629889[/C][C]-438.178761141842[/C][C]-0.521457348362567[/C][/ROW]
[ROW][C]65[/C][C]19100[/C][C]19182.7777917259[/C][C]18466.9457325059[/C][C]82.7777917258623[/C][C]633.05426749405[/C][C]1.10218502294869[/C][/ROW]
[ROW][C]66[/C][C]18522[/C][C]18610.1403602174[/C][C]18650.2015252305[/C][C]88.1403602173717[/C][C]-128.201525230506[/C][C]0.73835603786959[/C][/ROW]
[ROW][C]67[/C][C]18060[/C][C]18145.5454667379[/C][C]18689.7287819752[/C][C]85.5454667378949[/C][C]-629.728781975174[/C][C]-0.357337570795217[/C][/ROW]
[ROW][C]68[/C][C]18869[/C][C]18954.6273208045[/C][C]18776.8080028470[/C][C]85.6273208044576[/C][C]92.1919971530416[/C][C]0.0112775352566685[/C][/ROW]
[ROW][C]69[/C][C]18127[/C][C]18209.5480841698[/C][C]18804.7135859381[/C][C]82.5480841697832[/C][C]-677.713585938127[/C][C]-0.42445991711634[/C][/ROW]
[ROW][C]70[/C][C]18871[/C][C]18958.5518061361[/C][C]18981.1065816888[/C][C]87.5518061361308[/C][C]-110.106581688841[/C][C]0.690013654175918[/C][/ROW]
[ROW][C]71[/C][C]18890[/C][C]18982.4956725489[/C][C]19161.4235663533[/C][C]92.4956725488576[/C][C]-271.423566353257[/C][C]0.681978362131513[/C][/ROW]
[ROW][C]72[/C][C]21263[/C][C]21353.0762168967[/C][C]19208.5077582331[/C][C]90.07621689668[/C][C]2054.49224176685[/C][C]-0.333844069038362[/C][/ROW]
[ROW][C]73[/C][C]19547[/C][C]19643.5548995782[/C][C]19420.1933481326[/C][C]96.5548995781493[/C][C]126.806651867427[/C][C]0.894098448192825[/C][/ROW]
[ROW][C]74[/C][C]18450[/C][C]18545.9495720152[/C][C]19505.3874021292[/C][C]95.9495720151657[/C][C]-1055.38740212923[/C][C]-0.0835331354626137[/C][/ROW]
[ROW][C]75[/C][C]20254[/C][C]20347.1562649718[/C][C]19548.9265106949[/C][C]93.156264971797[/C][C]705.073489305116[/C][C]-0.385335983346102[/C][/ROW]
[ROW][C]76[/C][C]19240[/C][C]19335.1799098198[/C][C]19680.0400912674[/C][C]95.179909819775[/C][C]-440.040091267412[/C][C]0.279026604563751[/C][/ROW]
[ROW][C]77[/C][C]20216[/C][C]20309.3334981260[/C][C]19740.5963761845[/C][C]93.3334981259738[/C][C]475.403623815547[/C][C]-0.254484208719289[/C][/ROW]
[ROW][C]78[/C][C]19420[/C][C]19507.3984851367[/C][C]19722.6563448177[/C][C]87.3984851366954[/C][C]-302.656344817737[/C][C]-0.817847173612272[/C][/ROW]
[ROW][C]79[/C][C]19415[/C][C]19504.7818362795[/C][C]19854.7369503513[/C][C]89.781836279546[/C][C]-439.736950351284[/C][C]0.328439734536982[/C][/ROW]
[ROW][C]80[/C][C]20018[/C][C]20108.4601293099[/C][C]19957.2357168735[/C][C]90.4601293098559[/C][C]60.7642831264537[/C][C]0.0934873708840648[/C][/ROW]
[ROW][C]81[/C][C]18652[/C][C]18732.2626073329[/C][C]19856.4860911417[/C][C]80.262607332886[/C][C]-1204.48609114171[/C][C]-1.40571677346299[/C][/ROW]
[ROW][C]82[/C][C]19978[/C][C]20059.1259262908[/C][C]19952.9388984069[/C][C]81.125926290822[/C][C]25.0611015931302[/C][C]0.119020635507625[/C][/ROW]
[ROW][C]83[/C][C]19509[/C][C]19586.4195110991[/C][C]19964.5467537368[/C][C]77.4195110990556[/C][C]-455.54675373678[/C][C]-0.51102528352137[/C][/ROW]
[ROW][C]84[/C][C]21971[/C][C]22046.5318898164[/C][C]20006.5581952663[/C][C]75.5318898164371[/C][C]1964.44180473367[/C][C]-0.260279917278426[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=10033&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10033&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
tObservedFittedLevelSlopeSeasonalResiduals
1133281332813328000
21287312876.825789185113200.68194781453.82578918514634-327.68194781448-1.30937963159356
31400014022.003195416113460.239175026122.0031954161445539.7608249738651.31765525270691
41347713503.28510594313535.980420899126.285105942988-58.98042089910190.311977226123143
51423714279.187168178013789.367306105442.1871681780315447.6326938945811.58162610681454
61367413716.330053584513834.089406732442.3300535845046-160.0894067323570.0192376923718027
71352913565.776204579913756.221415002136.7762045798851-227.221415002069-0.937057013630214
81405814096.052962469613824.126311763838.052962469584233.8736882362300.243826417267901
91297513001.984105796413582.498775267426.9841057964398-607.498775267415-2.18590323595731
101432614357.822589832413729.720459629231.8225898323746596.2795403707720.935766933751675
111400814044.349976730413869.974239000436.3499767303937138.0257609995870.840035252978843
121619316261.960045098214657.836502122868.96004509823561535.163497877235.79802749363536
131448314556.653080951614941.154811152173.6530809516055-458.1548111520811.70324729921804
141401114083.566100021114973.533754067972.56610002109-962.533754067875-0.330836466619584
151505715123.942435425614910.422651392366.9424354256193146.577348607663-1.01555961592100
161488414950.396541018114966.957094087366.3965410181129-82.9570940873432-0.0738137548231807
171541415477.559413134314983.556371205163.5594131342866430.443628794902-0.353622835038707
181444014491.857643176214842.047094907351.8576431761596-402.04709490728-1.49507756240521
191490014950.965892265014877.713299010550.96589226503822.2867009895442-0.120639961851066
201507415121.10496519514855.421556101747.1049651949951218.578443898257-0.551266726939755
211444214493.860534817114996.315769437551.8605348170913-554.3157694374570.706960235998102
221530715360.771601029415087.067621877253.7716010293883219.9323781228000.292538866816767
231493814995.940245514515228.059675091957.9402455145203-290.0596750918540.654261787956202
241719317259.299388613715465.375070008966.29938861369761727.624929991061.34474028856297
251552815602.018587608815699.544232176374.0185876088331-171.5442321763311.26208555258566
261476514838.806527430415769.063338417073.806527430363-1004.06333841696-0.0337794511447212
271583815908.205950248915770.367127643670.205950248940967.6328723564073-0.537689018234094
281572315789.407903913915767.917838339166.4079039138715-44.9178383390633-0.531044660759293
291615016210.536106885315725.662845630060.5361068853177424.337154370023-0.790177339025636
301548615546.212498884715780.286200526860.2124988847452-294.286200526844-0.0432200309184316
311598616048.226208361715877.398174020462.22620836171108.6018259795550.271983421683458
321598316044.639751111915928.746878529861.639751111918954.2531214702492-0.0806101036648515
331569215759.479736029016100.431723783267.4797360289696-408.4317237832090.81632027217282
341649016563.446724025616282.106735345273.4467240256002207.8932646548390.845799882141688
351568615757.887372620116325.337478227571.8873726200522-639.337478227512-0.223472602865751
361889718982.409756833616661.113104020385.40975683355732235.886895979681.95194819402148
371631616400.205388308116723.023036009384.2053883081234-407.023036009263-0.174005251309701
381563615715.375971044316713.717226233479.3759710442796-1077.71722623338-0.692066109425585
391716317244.405387988416831.896475851781.4053879883748331.1035241482940.286209760715423
401653416609.196748735216796.136125745675.1967487352262-262.136125745639-0.86037097664727
411651816578.451407353816595.816779431360.4514073538403-77.8167794313284-2.01850104025196
421637516432.410115160716599.730785008857.4101151607113-224.730785008773-0.414637749542108
431629016339.684777258216513.587846713049.6847772582255-223.587846712959-1.05576587816445
441635216397.456173405016484.454074275145.4561734049545-132.454074275099-0.580951664466268
451594315986.384904787316491.109234458843.3849047873067-548.109234458764-0.286122147509981
461636216399.815399736716429.598346882137.815399736733-67.598346882089-0.772925383912537
471639316441.425955957916668.133255975648.4259559578775-275.1332559755951.47794698241834
481905119103.162497450916787.41597010552.16249745091092263.584029895000.521772960478365
491674716803.811104894116927.734024717156.8111048941409-180.7340247171030.649499979507535
501632016383.604249908217113.092879123763.6042499082173-793.0928791237350.947131566555392
511791017978.586144416617270.641819616168.5861444166098639.3581803838520.69150670359207
521696117024.724632660317247.892451613563.7246326602895-286.892451613550-0.6713115311164
531748017544.445436162217325.119147574564.44543616219154.8808524255410.0991472221876955
541704917109.302270900717312.085857678860.3022709007103-263.085857678771-0.569037817998455
551687916932.347046733617242.367161210853.3470467335768-363.367161210762-0.955814972472107
561747317529.330033064117351.520995702956.3300330641291121.4790042971490.410589954146277
571699817058.114324207217478.752768527160.1143242071860-480.7527685271210.521754101189441
581730717368.691898610717568.474110901461.691898610739-261.4741109013570.217805644509610
591741817483.052478450017693.326983682765.0524784499592-275.3269836826860.464495230746059
602016920239.467949264917860.249436108470.46794926488242308.750563891640.749166786818708
611787117946.726144721018029.640552299175.7261447209669-158.6405522990910.727666511577185
621722617302.000458905918110.524511571076.0004589058506-884.524511571040.037944235448267
631906219140.557540384718234.557270025478.5575403847418827.4427299745510.353264630573422
641780417878.77705266318242.178761141874.7770526629889-438.178761141842-0.521457348362567
651910019182.777791725918466.945732505982.7777917258623633.054267494051.10218502294869
661852218610.140360217418650.201525230588.1403602173717-128.2015252305060.73835603786959
671806018145.545466737918689.728781975285.5454667378949-629.728781975174-0.357337570795217
681886918954.627320804518776.808002847085.627320804457692.19199715304160.0112775352566685
691812718209.548084169818804.713585938182.5480841697832-677.713585938127-0.42445991711634
701887118958.551806136118981.106581688887.5518061361308-110.1065816888410.690013654175918
711889018982.495672548919161.423566353392.4956725488576-271.4235663532570.681978362131513
722126321353.076216896719208.507758233190.076216896682054.49224176685-0.333844069038362
731954719643.554899578219420.193348132696.5548995781493126.8066518674270.894098448192825
741845018545.949572015219505.387402129295.9495720151657-1055.38740212923-0.0835331354626137
752025420347.156264971819548.926510694993.156264971797705.073489305116-0.385335983346102
761924019335.179909819819680.040091267495.179909819775-440.0400912674120.279026604563751
772021620309.333498126019740.596376184593.3334981259738475.403623815547-0.254484208719289
781942019507.398485136719722.656344817787.3984851366954-302.656344817737-0.817847173612272
791941519504.781836279519854.736950351389.781836279546-439.7369503512840.328439734536982
802001820108.460129309919957.235716873590.460129309855960.76428312645370.0934873708840648
811865218732.262607332919856.486091141780.262607332886-1204.48609114171-1.40571677346299
821997820059.125926290819952.938898406981.12592629082225.06110159313020.119020635507625
831950919586.419511099119964.546753736877.4195110990556-455.54675373678-0.51102528352137
842197122046.531889816420006.558195266375.53188981643711964.44180473367-0.260279917278426



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$resid)
myfit <- mylevel+myslope+myseas
bitmap(file='test1.png')
op <- par(mfrow = c(2,1))
plot(as.numeric(x),main='Interpolation Fit',ylab='Observed (black) / Fitted (red)',xlab='time',type='b')
lines(myfit, col = 'red')
grid()
plot(as.numeric(m$resid),main='Residuals',ylab='Residuals',xlab='time')
grid()
par(op)
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(3,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myslope,na.action=na.pass,lag.max = mylagmax,main='Slope')
acf(mylevel + myslope,na.action=na.pass,lag.max = mylagmax,main='Level+Slope')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(3,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myslope,main='Slope')
spectrum(mylevel + myslope,main='Level+Slope')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel + myslope,main='Level+Slope')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='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',7,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,'Fitted',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,'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,myfit[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')