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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 06:22:06 -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/t1259932996nu0sigrk436rk1m.htm/, Retrieved Sun, 28 Apr 2024 09:15:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63480, Retrieved Sun, 28 Apr 2024 09:15:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100
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   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
- RMPD      [Structural Time Series Models] [ws9] [2009-12-04 13:22:06] [b243db81ea3e1f02fb3382887fb0f701] [Current]
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Dataseries X:
5594
5585
5710
5511
5403
5826
5884
5965
5960
6064
6046
5954
5952
5960
5983
5996
6021
6094
6202
6276
6306
6342
6345
6328
6191
6261
6253
6198
6247
6293
6381
6448
6470
6516
6532
6526
6533
6498
6507
6464
6453
6468
6497
6808
6793
6907
6792
6757
6734
6654
6589
6469
6521
6448
6410
6528
6445
6458
6215
6167




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63480&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]3 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=63480&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63480&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
155945594000
255855585.49267698426-0.465753823415763-0.492676984255414-0.0537199697662753
357105707.596294475910.205542337353012.403705524089651.28964252188643
455115515.60454454272-0.535805249445372-4.60454454272467-2.02202742407307
554035404.783537737-0.985150683927748-1.78353773699529-1.16024585017360
658265817.443839220640.6992994870306118.556160779356924.35168984027478
758845884.298174414510.967538038309447-0.2981744145103280.695967805368592
859655962.755417204131.280458299667422.244582795872970.815204911765328
959605959.274880243671.261309677064680.725119756329473-0.0500860864667244
1060646060.566153645121.662016516054003.433846354881511.05231571753898
1160466045.28596631541.594419068935140.714033684595423-0.178230683152004
1259545954.288744605051.22645718464851-0.288744605053132-0.974049435437869
1359525961.236225049070.986194715512836-9.23622504906980.072383046271056
1459605962.639147097250.9960390732434-2.639147097248860.00374972867586038
1559835976.486732922231.044301846171966.513267077765880.135025993182668
1659965994.440743670921.073320975605261.559256329079150.177714473876296
1760216026.553171143211.13311776747348-5.553171143214820.326200354141215
1860946084.09966477751.243952188273969.90033522249520.592866472567908
1962026199.96524336781.468616840929472.034756632193711.20459447496761
2062766271.747315644811.606116973431564.252684355184780.738944706054103
2163066306.390346838381.67058042811459-0.3903468383751450.347193814450709
2263426338.0828831811.729234926775363.91711681899740.315510013757201
2363456342.718894852961.735193664451372.281105147040670.0305514167643221
2463286328.633423022431.71369943878817-0.633423022430824-0.166196050529395
2561916230.441766995573.59369480458091-39.4417669955685-1.14783473088277
2662616260.533669043893.980624974386070.4663309561100440.254986689834194
2762536244.577663510163.911073235999128.42233648983997-0.209267911254642
2861986196.116879116493.849621668905961.88312088350807-0.550264578482982
2962476247.537826257793.91135978448179-0.5378262577934440.499796953269810
3062936282.599393073383.9540690430620110.40060692661820.327271254796888
3163816374.750842002784.075181825439716.249157997220210.926622703581974
3264486440.447333855674.159565487426507.552666144325240.64740753922439
3364706468.667069464734.192449586178881.332930535274240.25278118282265
3465166508.635708321264.24217561081557.364291678737490.375880638291721
3565326527.246371096634.263996060938624.75362890337300.150986058536011
3665266522.28370221544.260951237382983.71629778459344-0.096883414367614
3765336572.700539614933.72635669464867-39.70053961492980.512308066485386
3864986501.307529929042.95752963466136-3.30752992903744-0.744530542525842
3965076496.310605786672.9302866559312510.6893942133312-0.0834179412674633
4064646466.229853190752.89694415335629-2.22985319074863-0.346814813038366
4164536455.047990837962.88286459612969-2.04799083795864-0.147897197461411
4264686461.305924651912.886570308205526.694075348087490.0354545813052317
4364976492.718899642022.918272046776254.281100357983380.299665872298256
4468086786.51453578883.2403666217287421.48546421120573.05562332299539
4567936798.260415557723.24978847173702-5.260415557717930.0893492048566338
4669076896.636255383073.3598321146343810.36374461693120.99933593844963
4767926794.941374950073.22857920938627-2.94137495006802-1.10381319909111
4867576756.943928219253.239111566095290.05607178075444-0.43286267391238
4967346764.614148842453.20376185130339-30.6141488424510.0483050690153466
5066546662.828929106692.40088842459201-8.82892910668605-1.05808427627804
5165896581.283440980472.118003133684467.7165590195332-0.879588106515234
5264696476.260610292732.01146917010183-7.26061029273046-1.12560133546848
5365216518.343760902752.044925713647672.656239097255150.420925640267405
5464486445.216901805011.97432019780642.78309819499167-0.78961198318977
5564106421.840614078221.94981055578984-11.8406140782181-0.266285246180862
5665286501.978513967212.0252400039442326.02148603278530.821294854334814
5764456461.630308012421.984043332134-16.6303080124156-0.445097906525562
5864586444.848486386021.9644835155421413.1515136139800-0.197135550775057
5962156230.699746828621.73700106415493-15.6997468286192-2.27049373982922
6061676168.160618006341.77627109626637-1.16061800633696-0.674976602552073

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 5594 & 5594 & 0 & 0 & 0 \tabularnewline
2 & 5585 & 5585.49267698426 & -0.465753823415763 & -0.492676984255414 & -0.0537199697662753 \tabularnewline
3 & 5710 & 5707.59629447591 & 0.20554233735301 & 2.40370552408965 & 1.28964252188643 \tabularnewline
4 & 5511 & 5515.60454454272 & -0.535805249445372 & -4.60454454272467 & -2.02202742407307 \tabularnewline
5 & 5403 & 5404.783537737 & -0.985150683927748 & -1.78353773699529 & -1.16024585017360 \tabularnewline
6 & 5826 & 5817.44383922064 & 0.699299487030611 & 8.55616077935692 & 4.35168984027478 \tabularnewline
7 & 5884 & 5884.29817441451 & 0.967538038309447 & -0.298174414510328 & 0.695967805368592 \tabularnewline
8 & 5965 & 5962.75541720413 & 1.28045829966742 & 2.24458279587297 & 0.815204911765328 \tabularnewline
9 & 5960 & 5959.27488024367 & 1.26130967706468 & 0.725119756329473 & -0.0500860864667244 \tabularnewline
10 & 6064 & 6060.56615364512 & 1.66201651605400 & 3.43384635488151 & 1.05231571753898 \tabularnewline
11 & 6046 & 6045.2859663154 & 1.59441906893514 & 0.714033684595423 & -0.178230683152004 \tabularnewline
12 & 5954 & 5954.28874460505 & 1.22645718464851 & -0.288744605053132 & -0.974049435437869 \tabularnewline
13 & 5952 & 5961.23622504907 & 0.986194715512836 & -9.2362250490698 & 0.072383046271056 \tabularnewline
14 & 5960 & 5962.63914709725 & 0.9960390732434 & -2.63914709724886 & 0.00374972867586038 \tabularnewline
15 & 5983 & 5976.48673292223 & 1.04430184617196 & 6.51326707776588 & 0.135025993182668 \tabularnewline
16 & 5996 & 5994.44074367092 & 1.07332097560526 & 1.55925632907915 & 0.177714473876296 \tabularnewline
17 & 6021 & 6026.55317114321 & 1.13311776747348 & -5.55317114321482 & 0.326200354141215 \tabularnewline
18 & 6094 & 6084.0996647775 & 1.24395218827396 & 9.9003352224952 & 0.592866472567908 \tabularnewline
19 & 6202 & 6199.9652433678 & 1.46861684092947 & 2.03475663219371 & 1.20459447496761 \tabularnewline
20 & 6276 & 6271.74731564481 & 1.60611697343156 & 4.25268435518478 & 0.738944706054103 \tabularnewline
21 & 6306 & 6306.39034683838 & 1.67058042811459 & -0.390346838375145 & 0.347193814450709 \tabularnewline
22 & 6342 & 6338.082883181 & 1.72923492677536 & 3.9171168189974 & 0.315510013757201 \tabularnewline
23 & 6345 & 6342.71889485296 & 1.73519366445137 & 2.28110514704067 & 0.0305514167643221 \tabularnewline
24 & 6328 & 6328.63342302243 & 1.71369943878817 & -0.633423022430824 & -0.166196050529395 \tabularnewline
25 & 6191 & 6230.44176699557 & 3.59369480458091 & -39.4417669955685 & -1.14783473088277 \tabularnewline
26 & 6261 & 6260.53366904389 & 3.98062497438607 & 0.466330956110044 & 0.254986689834194 \tabularnewline
27 & 6253 & 6244.57766351016 & 3.91107323599912 & 8.42233648983997 & -0.209267911254642 \tabularnewline
28 & 6198 & 6196.11687911649 & 3.84962166890596 & 1.88312088350807 & -0.550264578482982 \tabularnewline
29 & 6247 & 6247.53782625779 & 3.91135978448179 & -0.537826257793444 & 0.499796953269810 \tabularnewline
30 & 6293 & 6282.59939307338 & 3.95406904306201 & 10.4006069266182 & 0.327271254796888 \tabularnewline
31 & 6381 & 6374.75084200278 & 4.07518182543971 & 6.24915799722021 & 0.926622703581974 \tabularnewline
32 & 6448 & 6440.44733385567 & 4.15956548742650 & 7.55266614432524 & 0.64740753922439 \tabularnewline
33 & 6470 & 6468.66706946473 & 4.19244958617888 & 1.33293053527424 & 0.25278118282265 \tabularnewline
34 & 6516 & 6508.63570832126 & 4.2421756108155 & 7.36429167873749 & 0.375880638291721 \tabularnewline
35 & 6532 & 6527.24637109663 & 4.26399606093862 & 4.7536289033730 & 0.150986058536011 \tabularnewline
36 & 6526 & 6522.2837022154 & 4.26095123738298 & 3.71629778459344 & -0.096883414367614 \tabularnewline
37 & 6533 & 6572.70053961493 & 3.72635669464867 & -39.7005396149298 & 0.512308066485386 \tabularnewline
38 & 6498 & 6501.30752992904 & 2.95752963466136 & -3.30752992903744 & -0.744530542525842 \tabularnewline
39 & 6507 & 6496.31060578667 & 2.93028665593125 & 10.6893942133312 & -0.0834179412674633 \tabularnewline
40 & 6464 & 6466.22985319075 & 2.89694415335629 & -2.22985319074863 & -0.346814813038366 \tabularnewline
41 & 6453 & 6455.04799083796 & 2.88286459612969 & -2.04799083795864 & -0.147897197461411 \tabularnewline
42 & 6468 & 6461.30592465191 & 2.88657030820552 & 6.69407534808749 & 0.0354545813052317 \tabularnewline
43 & 6497 & 6492.71889964202 & 2.91827204677625 & 4.28110035798338 & 0.299665872298256 \tabularnewline
44 & 6808 & 6786.5145357888 & 3.24036662172874 & 21.4854642112057 & 3.05562332299539 \tabularnewline
45 & 6793 & 6798.26041555772 & 3.24978847173702 & -5.26041555771793 & 0.0893492048566338 \tabularnewline
46 & 6907 & 6896.63625538307 & 3.35983211463438 & 10.3637446169312 & 0.99933593844963 \tabularnewline
47 & 6792 & 6794.94137495007 & 3.22857920938627 & -2.94137495006802 & -1.10381319909111 \tabularnewline
48 & 6757 & 6756.94392821925 & 3.23911156609529 & 0.05607178075444 & -0.43286267391238 \tabularnewline
49 & 6734 & 6764.61414884245 & 3.20376185130339 & -30.614148842451 & 0.0483050690153466 \tabularnewline
50 & 6654 & 6662.82892910669 & 2.40088842459201 & -8.82892910668605 & -1.05808427627804 \tabularnewline
51 & 6589 & 6581.28344098047 & 2.11800313368446 & 7.7165590195332 & -0.879588106515234 \tabularnewline
52 & 6469 & 6476.26061029273 & 2.01146917010183 & -7.26061029273046 & -1.12560133546848 \tabularnewline
53 & 6521 & 6518.34376090275 & 2.04492571364767 & 2.65623909725515 & 0.420925640267405 \tabularnewline
54 & 6448 & 6445.21690180501 & 1.9743201978064 & 2.78309819499167 & -0.78961198318977 \tabularnewline
55 & 6410 & 6421.84061407822 & 1.94981055578984 & -11.8406140782181 & -0.266285246180862 \tabularnewline
56 & 6528 & 6501.97851396721 & 2.02524000394423 & 26.0214860327853 & 0.821294854334814 \tabularnewline
57 & 6445 & 6461.63030801242 & 1.984043332134 & -16.6303080124156 & -0.445097906525562 \tabularnewline
58 & 6458 & 6444.84848638602 & 1.96448351554214 & 13.1515136139800 & -0.197135550775057 \tabularnewline
59 & 6215 & 6230.69974682862 & 1.73700106415493 & -15.6997468286192 & -2.27049373982922 \tabularnewline
60 & 6167 & 6168.16061800634 & 1.77627109626637 & -1.16061800633696 & -0.674976602552073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63480&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]5594[/C][C]5594[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]5585[/C][C]5585.49267698426[/C][C]-0.465753823415763[/C][C]-0.492676984255414[/C][C]-0.0537199697662753[/C][/ROW]
[ROW][C]3[/C][C]5710[/C][C]5707.59629447591[/C][C]0.20554233735301[/C][C]2.40370552408965[/C][C]1.28964252188643[/C][/ROW]
[ROW][C]4[/C][C]5511[/C][C]5515.60454454272[/C][C]-0.535805249445372[/C][C]-4.60454454272467[/C][C]-2.02202742407307[/C][/ROW]
[ROW][C]5[/C][C]5403[/C][C]5404.783537737[/C][C]-0.985150683927748[/C][C]-1.78353773699529[/C][C]-1.16024585017360[/C][/ROW]
[ROW][C]6[/C][C]5826[/C][C]5817.44383922064[/C][C]0.699299487030611[/C][C]8.55616077935692[/C][C]4.35168984027478[/C][/ROW]
[ROW][C]7[/C][C]5884[/C][C]5884.29817441451[/C][C]0.967538038309447[/C][C]-0.298174414510328[/C][C]0.695967805368592[/C][/ROW]
[ROW][C]8[/C][C]5965[/C][C]5962.75541720413[/C][C]1.28045829966742[/C][C]2.24458279587297[/C][C]0.815204911765328[/C][/ROW]
[ROW][C]9[/C][C]5960[/C][C]5959.27488024367[/C][C]1.26130967706468[/C][C]0.725119756329473[/C][C]-0.0500860864667244[/C][/ROW]
[ROW][C]10[/C][C]6064[/C][C]6060.56615364512[/C][C]1.66201651605400[/C][C]3.43384635488151[/C][C]1.05231571753898[/C][/ROW]
[ROW][C]11[/C][C]6046[/C][C]6045.2859663154[/C][C]1.59441906893514[/C][C]0.714033684595423[/C][C]-0.178230683152004[/C][/ROW]
[ROW][C]12[/C][C]5954[/C][C]5954.28874460505[/C][C]1.22645718464851[/C][C]-0.288744605053132[/C][C]-0.974049435437869[/C][/ROW]
[ROW][C]13[/C][C]5952[/C][C]5961.23622504907[/C][C]0.986194715512836[/C][C]-9.2362250490698[/C][C]0.072383046271056[/C][/ROW]
[ROW][C]14[/C][C]5960[/C][C]5962.63914709725[/C][C]0.9960390732434[/C][C]-2.63914709724886[/C][C]0.00374972867586038[/C][/ROW]
[ROW][C]15[/C][C]5983[/C][C]5976.48673292223[/C][C]1.04430184617196[/C][C]6.51326707776588[/C][C]0.135025993182668[/C][/ROW]
[ROW][C]16[/C][C]5996[/C][C]5994.44074367092[/C][C]1.07332097560526[/C][C]1.55925632907915[/C][C]0.177714473876296[/C][/ROW]
[ROW][C]17[/C][C]6021[/C][C]6026.55317114321[/C][C]1.13311776747348[/C][C]-5.55317114321482[/C][C]0.326200354141215[/C][/ROW]
[ROW][C]18[/C][C]6094[/C][C]6084.0996647775[/C][C]1.24395218827396[/C][C]9.9003352224952[/C][C]0.592866472567908[/C][/ROW]
[ROW][C]19[/C][C]6202[/C][C]6199.9652433678[/C][C]1.46861684092947[/C][C]2.03475663219371[/C][C]1.20459447496761[/C][/ROW]
[ROW][C]20[/C][C]6276[/C][C]6271.74731564481[/C][C]1.60611697343156[/C][C]4.25268435518478[/C][C]0.738944706054103[/C][/ROW]
[ROW][C]21[/C][C]6306[/C][C]6306.39034683838[/C][C]1.67058042811459[/C][C]-0.390346838375145[/C][C]0.347193814450709[/C][/ROW]
[ROW][C]22[/C][C]6342[/C][C]6338.082883181[/C][C]1.72923492677536[/C][C]3.9171168189974[/C][C]0.315510013757201[/C][/ROW]
[ROW][C]23[/C][C]6345[/C][C]6342.71889485296[/C][C]1.73519366445137[/C][C]2.28110514704067[/C][C]0.0305514167643221[/C][/ROW]
[ROW][C]24[/C][C]6328[/C][C]6328.63342302243[/C][C]1.71369943878817[/C][C]-0.633423022430824[/C][C]-0.166196050529395[/C][/ROW]
[ROW][C]25[/C][C]6191[/C][C]6230.44176699557[/C][C]3.59369480458091[/C][C]-39.4417669955685[/C][C]-1.14783473088277[/C][/ROW]
[ROW][C]26[/C][C]6261[/C][C]6260.53366904389[/C][C]3.98062497438607[/C][C]0.466330956110044[/C][C]0.254986689834194[/C][/ROW]
[ROW][C]27[/C][C]6253[/C][C]6244.57766351016[/C][C]3.91107323599912[/C][C]8.42233648983997[/C][C]-0.209267911254642[/C][/ROW]
[ROW][C]28[/C][C]6198[/C][C]6196.11687911649[/C][C]3.84962166890596[/C][C]1.88312088350807[/C][C]-0.550264578482982[/C][/ROW]
[ROW][C]29[/C][C]6247[/C][C]6247.53782625779[/C][C]3.91135978448179[/C][C]-0.537826257793444[/C][C]0.499796953269810[/C][/ROW]
[ROW][C]30[/C][C]6293[/C][C]6282.59939307338[/C][C]3.95406904306201[/C][C]10.4006069266182[/C][C]0.327271254796888[/C][/ROW]
[ROW][C]31[/C][C]6381[/C][C]6374.75084200278[/C][C]4.07518182543971[/C][C]6.24915799722021[/C][C]0.926622703581974[/C][/ROW]
[ROW][C]32[/C][C]6448[/C][C]6440.44733385567[/C][C]4.15956548742650[/C][C]7.55266614432524[/C][C]0.64740753922439[/C][/ROW]
[ROW][C]33[/C][C]6470[/C][C]6468.66706946473[/C][C]4.19244958617888[/C][C]1.33293053527424[/C][C]0.25278118282265[/C][/ROW]
[ROW][C]34[/C][C]6516[/C][C]6508.63570832126[/C][C]4.2421756108155[/C][C]7.36429167873749[/C][C]0.375880638291721[/C][/ROW]
[ROW][C]35[/C][C]6532[/C][C]6527.24637109663[/C][C]4.26399606093862[/C][C]4.7536289033730[/C][C]0.150986058536011[/C][/ROW]
[ROW][C]36[/C][C]6526[/C][C]6522.2837022154[/C][C]4.26095123738298[/C][C]3.71629778459344[/C][C]-0.096883414367614[/C][/ROW]
[ROW][C]37[/C][C]6533[/C][C]6572.70053961493[/C][C]3.72635669464867[/C][C]-39.7005396149298[/C][C]0.512308066485386[/C][/ROW]
[ROW][C]38[/C][C]6498[/C][C]6501.30752992904[/C][C]2.95752963466136[/C][C]-3.30752992903744[/C][C]-0.744530542525842[/C][/ROW]
[ROW][C]39[/C][C]6507[/C][C]6496.31060578667[/C][C]2.93028665593125[/C][C]10.6893942133312[/C][C]-0.0834179412674633[/C][/ROW]
[ROW][C]40[/C][C]6464[/C][C]6466.22985319075[/C][C]2.89694415335629[/C][C]-2.22985319074863[/C][C]-0.346814813038366[/C][/ROW]
[ROW][C]41[/C][C]6453[/C][C]6455.04799083796[/C][C]2.88286459612969[/C][C]-2.04799083795864[/C][C]-0.147897197461411[/C][/ROW]
[ROW][C]42[/C][C]6468[/C][C]6461.30592465191[/C][C]2.88657030820552[/C][C]6.69407534808749[/C][C]0.0354545813052317[/C][/ROW]
[ROW][C]43[/C][C]6497[/C][C]6492.71889964202[/C][C]2.91827204677625[/C][C]4.28110035798338[/C][C]0.299665872298256[/C][/ROW]
[ROW][C]44[/C][C]6808[/C][C]6786.5145357888[/C][C]3.24036662172874[/C][C]21.4854642112057[/C][C]3.05562332299539[/C][/ROW]
[ROW][C]45[/C][C]6793[/C][C]6798.26041555772[/C][C]3.24978847173702[/C][C]-5.26041555771793[/C][C]0.0893492048566338[/C][/ROW]
[ROW][C]46[/C][C]6907[/C][C]6896.63625538307[/C][C]3.35983211463438[/C][C]10.3637446169312[/C][C]0.99933593844963[/C][/ROW]
[ROW][C]47[/C][C]6792[/C][C]6794.94137495007[/C][C]3.22857920938627[/C][C]-2.94137495006802[/C][C]-1.10381319909111[/C][/ROW]
[ROW][C]48[/C][C]6757[/C][C]6756.94392821925[/C][C]3.23911156609529[/C][C]0.05607178075444[/C][C]-0.43286267391238[/C][/ROW]
[ROW][C]49[/C][C]6734[/C][C]6764.61414884245[/C][C]3.20376185130339[/C][C]-30.614148842451[/C][C]0.0483050690153466[/C][/ROW]
[ROW][C]50[/C][C]6654[/C][C]6662.82892910669[/C][C]2.40088842459201[/C][C]-8.82892910668605[/C][C]-1.05808427627804[/C][/ROW]
[ROW][C]51[/C][C]6589[/C][C]6581.28344098047[/C][C]2.11800313368446[/C][C]7.7165590195332[/C][C]-0.879588106515234[/C][/ROW]
[ROW][C]52[/C][C]6469[/C][C]6476.26061029273[/C][C]2.01146917010183[/C][C]-7.26061029273046[/C][C]-1.12560133546848[/C][/ROW]
[ROW][C]53[/C][C]6521[/C][C]6518.34376090275[/C][C]2.04492571364767[/C][C]2.65623909725515[/C][C]0.420925640267405[/C][/ROW]
[ROW][C]54[/C][C]6448[/C][C]6445.21690180501[/C][C]1.9743201978064[/C][C]2.78309819499167[/C][C]-0.78961198318977[/C][/ROW]
[ROW][C]55[/C][C]6410[/C][C]6421.84061407822[/C][C]1.94981055578984[/C][C]-11.8406140782181[/C][C]-0.266285246180862[/C][/ROW]
[ROW][C]56[/C][C]6528[/C][C]6501.97851396721[/C][C]2.02524000394423[/C][C]26.0214860327853[/C][C]0.821294854334814[/C][/ROW]
[ROW][C]57[/C][C]6445[/C][C]6461.63030801242[/C][C]1.984043332134[/C][C]-16.6303080124156[/C][C]-0.445097906525562[/C][/ROW]
[ROW][C]58[/C][C]6458[/C][C]6444.84848638602[/C][C]1.96448351554214[/C][C]13.1515136139800[/C][C]-0.197135550775057[/C][/ROW]
[ROW][C]59[/C][C]6215[/C][C]6230.69974682862[/C][C]1.73700106415493[/C][C]-15.6997468286192[/C][C]-2.27049373982922[/C][/ROW]
[ROW][C]60[/C][C]6167[/C][C]6168.16061800634[/C][C]1.77627109626637[/C][C]-1.16061800633696[/C][C]-0.674976602552073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63480&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63480&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
155945594000
255855585.49267698426-0.465753823415763-0.492676984255414-0.0537199697662753
357105707.596294475910.205542337353012.403705524089651.28964252188643
455115515.60454454272-0.535805249445372-4.60454454272467-2.02202742407307
554035404.783537737-0.985150683927748-1.78353773699529-1.16024585017360
658265817.443839220640.6992994870306118.556160779356924.35168984027478
758845884.298174414510.967538038309447-0.2981744145103280.695967805368592
859655962.755417204131.280458299667422.244582795872970.815204911765328
959605959.274880243671.261309677064680.725119756329473-0.0500860864667244
1060646060.566153645121.662016516054003.433846354881511.05231571753898
1160466045.28596631541.594419068935140.714033684595423-0.178230683152004
1259545954.288744605051.22645718464851-0.288744605053132-0.974049435437869
1359525961.236225049070.986194715512836-9.23622504906980.072383046271056
1459605962.639147097250.9960390732434-2.639147097248860.00374972867586038
1559835976.486732922231.044301846171966.513267077765880.135025993182668
1659965994.440743670921.073320975605261.559256329079150.177714473876296
1760216026.553171143211.13311776747348-5.553171143214820.326200354141215
1860946084.09966477751.243952188273969.90033522249520.592866472567908
1962026199.96524336781.468616840929472.034756632193711.20459447496761
2062766271.747315644811.606116973431564.252684355184780.738944706054103
2163066306.390346838381.67058042811459-0.3903468383751450.347193814450709
2263426338.0828831811.729234926775363.91711681899740.315510013757201
2363456342.718894852961.735193664451372.281105147040670.0305514167643221
2463286328.633423022431.71369943878817-0.633423022430824-0.166196050529395
2561916230.441766995573.59369480458091-39.4417669955685-1.14783473088277
2662616260.533669043893.980624974386070.4663309561100440.254986689834194
2762536244.577663510163.911073235999128.42233648983997-0.209267911254642
2861986196.116879116493.849621668905961.88312088350807-0.550264578482982
2962476247.537826257793.91135978448179-0.5378262577934440.499796953269810
3062936282.599393073383.9540690430620110.40060692661820.327271254796888
3163816374.750842002784.075181825439716.249157997220210.926622703581974
3264486440.447333855674.159565487426507.552666144325240.64740753922439
3364706468.667069464734.192449586178881.332930535274240.25278118282265
3465166508.635708321264.24217561081557.364291678737490.375880638291721
3565326527.246371096634.263996060938624.75362890337300.150986058536011
3665266522.28370221544.260951237382983.71629778459344-0.096883414367614
3765336572.700539614933.72635669464867-39.70053961492980.512308066485386
3864986501.307529929042.95752963466136-3.30752992903744-0.744530542525842
3965076496.310605786672.9302866559312510.6893942133312-0.0834179412674633
4064646466.229853190752.89694415335629-2.22985319074863-0.346814813038366
4164536455.047990837962.88286459612969-2.04799083795864-0.147897197461411
4264686461.305924651912.886570308205526.694075348087490.0354545813052317
4364976492.718899642022.918272046776254.281100357983380.299665872298256
4468086786.51453578883.2403666217287421.48546421120573.05562332299539
4567936798.260415557723.24978847173702-5.260415557717930.0893492048566338
4669076896.636255383073.3598321146343810.36374461693120.99933593844963
4767926794.941374950073.22857920938627-2.94137495006802-1.10381319909111
4867576756.943928219253.239111566095290.05607178075444-0.43286267391238
4967346764.614148842453.20376185130339-30.6141488424510.0483050690153466
5066546662.828929106692.40088842459201-8.82892910668605-1.05808427627804
5165896581.283440980472.118003133684467.7165590195332-0.879588106515234
5264696476.260610292732.01146917010183-7.26061029273046-1.12560133546848
5365216518.343760902752.044925713647672.656239097255150.420925640267405
5464486445.216901805011.97432019780642.78309819499167-0.78961198318977
5564106421.840614078221.94981055578984-11.8406140782181-0.266285246180862
5665286501.978513967212.0252400039442326.02148603278530.821294854334814
5764456461.630308012421.984043332134-16.6303080124156-0.445097906525562
5864586444.848486386021.9644835155421413.1515136139800-0.197135550775057
5962156230.699746828621.73700106415493-15.6997468286192-2.27049373982922
6061676168.160618006341.77627109626637-1.16061800633696-0.674976602552073



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')