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STSM 2

*The author of this computation has been verified*
R Software Module: /rwasp_structuraltimeseries.wasp (opens new window with default values)
Title produced by software: Structural Time Series Models
Date of computation: Wed, 29 Dec 2010 17:30:29 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy.htm/, Retrieved Wed, 29 Dec 2010 18:28:14 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
34420 34070 33978 34484 34645 34410 34534 35019 35508 35488 35709 36534 36066 35724 35727 36281 36017 35780 36003 36285 36419 36413 36663 37451 37063 36613 36564 37064 36817 36621 36798 36967 36926 36975 37324 38159 37776 37465 37451 37944 37737 37603 37813 37960 37934 37975 38241 39176 39137 38797 38730 39031 38851 38727 39291 39407 39326 39326 39617 40458 40425 40092 39939 40174 40065 39922 40107 40163 40116 40118 40416 41215 40852 40497 40430 40766 40626 40442 40590 40673 40660 40736 41082 41873 41507 41104 40963 41227 41063 40873 41016 41433 41345 41375 41597 42225 41937 41642 41551 41866 41744 41615 41764 41828 41786 41852 42183 42937 42635 42292 42202 42577 42600 42433 42584 42660 42659 42712 43039 43664 43397 43110 43005 43159 43066 42977 43139 43226 43242 43348 43605 44225 43983 43754 43632 43868 43864 43808 43954 44032 44090 44210 44435 44919 44785 44616 44 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ www.yougetit.org


Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
13442034420000
23407034131.2029122181-16.2888113060742-61.202912218095-1.88800432393156
33397834012.0899979814-22.7976946850222-34.0899979814116-0.917329963637011
43448434319.5953916254-2.09937048246635164.4046083746123.20020830598413
53464534588.814764816617.185059359740856.18523518338432.59786577490897
63441034514.79097280539.61909515314709-104.790972805307-0.862225100920138
73453434528.03531765219.959058072590645.964682347863330.0339359467101304
83501934875.450028790444.5099952911185143.549971209563.1340704388758
93550835370.724273979993.654482959847137.2757260200654.15981682081687
103548835540.6875348876102.364006445423-52.6875348875990.700623564548726
113570935703.7694894736109.5326769155135.230510526385740.555237164321971
123653436322.7993015351171.218177073246211.2006984648644.64448193610906
133606636216.5478023686140.605056986917-150.547802368595-2.7456658080555
143572435937.356132819588.1124580791673-213.356132819474-3.67153251008472
153572735872.225055702668.6236682525287-145.225055702628-1.30651482009274
163628136077.892290594586.0165539375395203.107709405521.22385960352315
173601736002.180385538465.494800085855314.8196144616266-1.45260028297814
183578035918.766908996146.5678518609296-138.766908996124-1.33160491925248
193600336035.665700620855.532524935723-32.66570062081570.628073804909721
203628536212.547001573671.045775807984672.45299842638891.08381144357146
213641936300.164050755173.1694830893095118.8359492449060.148049995896155
223641336463.060012437684.6875425629037-50.06001243758920.801639043707977
233666336739.1210184931109.250489680871-76.12101849313321.70733523552676
243745137053.2715066666135.453530761829397.7284933333621.82822694561328
253706337117.9930021992126.439442182036-54.9930021991532-0.64725331221341
263661336939.089124524587.1930355077995-326.08912452448-2.7214450158111
273656436816.830766336160.3332204247485-252.830766336086-1.82289327797739
283706436799.869087036650.4361750381991264.130912963359-0.684029498753077
293681736774.307842073140.687996225678842.6921579269463-0.678323300032229
303662136787.799400126537.1957008757122-166.799400126509-0.242208622700129
313679836848.551074054540.220681894897-50.55107405451430.209350876175983
323696736897.521733465341.344228655103769.47826653472760.0777512453986365
333692636885.571735045534.500759654085540.4282649544692-0.473724373677127
343697537046.691504268850.7565898483707-71.69150426877971.1251102076417
353732437360.089150217484.4412932695776-36.08915021743152.33155081091601
363815937646.3895483717110.291099566603512.610451628251.79817079906046
373777637738.8386789252108.0049086371737.1613210748054-0.160464922583948
383746537755.154348352196.2355141312736-290.154348352133-0.816038336142529
393745137726.546650708180.2462098491299-275.54665070807-1.09632685403033
403794437695.325788147965.9936062311606248.674211852087-0.98454732971312
413773737702.008034950558.40158016452934.9919650494879-0.527651788089461
423760337762.289801759858.642573075303-159.2898017597960.0167277695902708
433781337841.094545811861.2278736664804-28.09454581180460.179017761584549
443796037885.609926903859.085160034098374.3900730962309-0.148234802969255
453793437954.526294598960.3453371916343-20.52629459886880.0871645910047532
463797538113.49644691772.9816417909671-138.4964469170480.873935001137353
473824138305.091273817488.1667256182532-64.09127381743831.05128301823958
483917638570.7192135446110.873858347615605.2807864553711.57869951057998
493913738935.8756037656143.43772653864201.1243962344072.2713561662892
503879739067.6315000288141.940641404888-270.63150002879-0.103740445725224
513873039047.188966111121.15942069362-317.188966111003-1.43088234710005
523903138893.541377369286.038229203487137.458622630765-2.42643714414879
533885138854.762736241770.0799315409846-3.7627362416833-1.10784596037374
543872738891.676259500365.8350654880423-164.676259500271-0.294750143275104
553929139180.348161349994.3702060611516110.6518386501131.97747710834885
563940739332.8697930287101.81649475377974.13020697135270.515252206508365
573932639411.525665752898.8516945414672-85.5256657528166-0.205011936829367
583932639521.6316562361100.291620165802-195.6316562360920.0995724281435571
593961739723.1601424277113.236750779661-106.1601424277420.896538209870205
604045839918.4255659305123.72476866552539.5744340695280.728690183554872
614042540152.7870975305137.88067534153272.2129024695310.984703431569813
624009240288.1112857941137.553442172771-196.111285794068-0.022667369762562
633993940238.7644315257113.651366351705-299.764431525749-1.64911922029903
644017440100.965566764281.527240702242273.0344332357872-2.22018919784979
654006540093.810813266370.1969591426799-28.8108132663502-0.785900033760094
663992240162.839416152370.0475772944461-240.83941615232-0.0103709622960517
674010740090.331606763951.811559740318616.6683932360689-1.26426788611005
684016340090.428112330945.195873297768172.5718876691195-0.457886467153475
694011640183.373365579451.3022517351742-67.37336557941180.422238170921185
704011840321.479699140562.3974852442972-203.4796991405130.767324115322881
714041640509.665509501778.4689992189358-93.66550950173751.11327496861512
724121540683.038430968690.5955188758592531.961569031420.841987979140863
734085240645.393977095174.2000138136784206.606022904899-1.1388350535164
744049740634.230355004963.2834563771645-137.230355004876-0.756058167706319
754043040647.298151341156.865617537618-217.298151341107-0.443307131953921
764076640670.642457395152.584701822240695.3575426049336-0.295982602381215
774062640666.169703804845.2985944314456-40.1697038048127-0.50510980473868
784044240649.42078488837.3712766374168-207.420784887995-0.550164516326476
794059040598.235909043826.0520410405689-8.23590904383484-0.784822586228876
804067340619.803230398525.47877277592253.1967696014998-0.0396847670149636
814066040728.755817472436.1457356652604-68.75581747234980.737688029708712
824073640921.38934795856.133951388556-185.3893479580221.38260045933073
834108241134.209675305676.1416250668271-52.2096753055861.3860118287833
844187341271.353942557883.9322915472476601.6460574421880.540635271391659
854150741301.547093453577.06614669674205.452906546546-0.476505867655466
864110441272.10819398263.4558129672064-168.10819398196-0.94255775084998
874096341215.34144591848.0990858060784-252.341445917973-1.0615238117824
884122741149.86558192133.599861310116377.134418078983-1.00282314122877
894106341100.606406421323.0231350553575-37.6064064213009-0.732979628906789
904087341066.014822552815.666205686952-193.014822552841-0.510379093714212
914101641043.810242913410.8286007678054-27.8102429133806-0.335394119601366
924143341293.123561642241.2945863168515139.8764383578082.10931584854636
934134541453.367155518356.4861218837122-108.367155518311.05080418795391
944137541595.128114993667.3727795251998-220.1281149935510.753189938875272
954159741674.101926091468.8535135710904-77.10192609141030.102575022801006
964222541648.679250485856.8185280009269576.320749514215-0.834793675598532
974193741682.604154465253.8949734258079254.395845534759-0.202776628215955
984164241745.441923953355.0371281037619-103.4419239533070.0790959858628096
994155141767.833247141750.8689684794288-216.833247141726-0.288263379204167
1004186641770.143924128544.671823316110395.8560758714878-0.428740329420021
1014174441773.055140634739.3428562861463-29.0551406347176-0.369221966192459
1024161541811.16873239139.1859543667649-196.168732391025-0.0108809998533519
1034176441878.332896685142.758137224221-114.3328966850590.24762610115794
1044182841807.330320170428.23253169615820.6696798296316-1.00577843764999
1054178641886.74631749534.7665172511351-100.746317494990.452055781934125
1064185242021.052801338347.4695952771838-169.0528013382790.879023357440318
1074218342175.969268764361.1795507908997.030731235686420.949691398877024
1084293742319.595480710771.7009087407682617.4045192893180.72953889802394
1094263542390.085168463871.5462975819694244.914831536212-0.0107195343650527
1104229242407.253009780164.6042890085586-115.25300978006-0.48074402986246
1114220242419.019508545957.8604995145094-217.019508545937-0.466553469098015
1124257742465.36776923256.3916525663129111.632230767943-0.101642991367556
1134260042589.816022933665.0740643763610.18397706634870.601480559364218
1144243342642.355711805863.4746949287337-209.355711805748-0.110881092885968
1154258442674.742812858959.507018754312-90.7428128589422-0.274997024387872
1164266042689.40668452653.7834008014837-29.4066845260093-0.396338753463232
1174265942782.269281555258.7704751073686-123.2692815552430.345101004643511
1184271242901.509824010366.4853879702489-189.5098240103010.533941134500517
1194303943033.520690073274.84427623585175.479309926758410.578994047036827
1204366443069.029720580469.8258541907243594.970279419575-0.347872408646036
1214339743127.679264202568.3996707209969269.320735797541-0.0988524505801976
1224311043201.211235683669.0546386099199-91.21123568363750.045357728116717
1234300543240.557163092965.2639467078627-235.557163092921-0.262317506638654
1244315943151.781457890845.61409670538797.21854210916868-1.35999550327818
1254306643089.141991035531.8061335779409-23.1419910354723-0.9564631188469
1264297743145.896577197534.9887604286113-168.8965771974820.220590810050398
1274313943206.840767153238.3004559174269-67.84076715324470.229495816529076
1284322643267.881035823741.2020115810441-41.88103582371980.200930088203064
1294324243366.261703896648.4969240114826-124.2617038966520.504887457262343
1304334843512.974513843861.0249188376814-164.9745138437910.86716779431678
1314360543588.393723647462.860823823544716.60627635261070.127161738490862
1324422543636.422293664160.9688957507256588.577706335929-0.131117491168231
1334398343705.240761111761.9703101824697277.7592388883230.0693961579915333
1344375443800.510929193166.2187949953138-46.51092919313330.29421758545736
1354363243824.870669898760.8789743723333-192.870669898679-0.369588273236117
1364386843840.63831239855.125355235222427.3616876019754-0.398271987841503
1374386443893.984168157654.8984098814727-29.9841681575744-0.015719209377499
1384380843974.613056019658.1802013310288-166.6130560196340.227419647289529
1394395444037.11719644558.7317760995155-83.11719644504440.038218181051304
1404403244101.784657075659.4889816918927-69.78465707557210.0524372049328125
1414409044220.801129913267.0820298538154-130.8011299131720.525595174125809
1424421044351.369327363775.1787762626397-141.3693273637420.560506034634006
1434443544422.857446275574.7081193204312.1425537244497-0.0325982679800258
1444491944392.483611564561.3064736709994526.516388435553-0.928622298536876
1454478544485.80230835265.3896651155813299.197691647990.282913555930513
1464461644612.676015963773.23234985381443.323984036348190.543128094425829
1474449644680.786957092872.5791422172849-184.786957092825-0.045217310523113
1484460044636.413065083357.6645515251014-36.4130650832733-1.03251634023858
1494446344561.841934211740.8020148413936-98.8419342117337-1.16791711644152
1504423244463.779314153423.0929806653699-231.779314153356-1.22700385009862
1514424844383.02430789179.8480843541351-135.024307891725-0.91762527452009
1524428244378.74647036558.04638592748616-96.7464703655403-0.124771434422762
1534431144437.327506711514.4913545952613-126.3275067115470.446174118814475
1544436244483.22074268418.4957357533358-121.2207426839960.277232195557075
1554461744548.749004210524.493016268170668.25099578951850.415366599687544
1564502244539.361026585620.1725103474852482.638973414409-0.299335214437702
1574488244587.206607537523.7017160552771294.7933924625280.244500659638169
1584459444591.687723385421.25039099534462.31227661455001-0.169762087822549
1594450244622.005592392822.4067436062854-120.005592392780.0800553479985221
1604475244696.714355625129.075967522655155.28564437489110.461739697441392
1614469044729.665371481229.5700662718137-39.66537148121660.0342206816014456
1624453344736.633763927226.6880365162696-203.63376392722-0.199663510442271
1634460444745.484306337224.4133175420933-141.484306337185-0.157581476791777
1644464644765.61931448223.8677129609592-119.619314481998-0.03778466823413
1654465244792.139484272224.2059558530055-140.1394842721770.0234180123995728
1664471844840.925025910727.3400727657524-122.9250259106890.216997045595783
1674492844857.557927297525.974870236447470.4420727024919-0.0945506351725812
1684528544835.753395535519.8825881760383449.246604464542-0.422045212113775
1694518344869.649935612721.6696157502714313.3500643873490.12379288395873
1704496444943.612029476828.338006300809320.38797052321170.461809048957379
1714487545003.532316015532.3652027049557-128.5323160154970.278829013574972
1724507145024.293599998730.885656686439646.7064000013146-0.10244208349664
1734498545027.716091983427.3841210158583-42.7160919834303-0.24250653395678
1744485445050.1404263626.7517287421911-196.14042636002-0.043807447685434
1754492545069.237121001425.7756202158215-144.237121001391-0.0676151999746186
1764497045092.958750233125.5137060477705-122.958750233149-0.0181384143023421
1774501145144.266998703828.8027461851554-133.2669987037910.227729706922444
1784510745206.149375838833.0204072807923-99.14937583884890.292034423564383
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1804577345307.198574798637.655964388549465.8014252013570.414527067220196
1814562545341.330437196237.206617341368283.669562803762-0.0311255430502569
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1854532145384.297256802524.1115183178202-63.2972568025375-0.502822542457189
1864523845419.330037219225.5039390973566-181.3300372192480.0964500258424146
1874524645411.949497320321.3110880460442-165.949497320297-0.29042318395452
1884528145420.282365944119.6563049467494-139.282365944098-0.114599563019904
1894529745441.059126220119.7991634899147-144.059126220150.00989188451365484
1904534945453.425107705218.8514798840143-104.42510770525-0.0656211992179337
1914553645474.709209006119.161615890619761.29079099387450.0214786400336717
1924590345457.364819969914.5073468540064445.635180030129-0.322382276014761
1934571645444.987691826311.0796153484793271.01230817373-0.237421184161036
1944541945423.56592005716.93567090618412-4.5659200570506-0.28698351277894
1954523745355.8595074139-2.58096260250892-118.859507413878-0.658968760580607
1964534945319.1808817413-6.9281051773222729.8191182586841-0.301017596601367
1974526045318.4296789939-6.14062352120595-58.42967899391190.0545372551670214
1984513945309.9387135976-6.44026999729752-170.938713597608-0.0207548044478823
1994515445311.47712137-5.42302673592944-157.4771213699660.0704575874020555
2004518645319.637339304-3.69120449380068-133.6373393039990.119935023549576
2014526245373.85623160963.69197488952913-111.8562316096280.51124985597532
2024522745350.5233134550.246606983102826-123.523313455033-0.238578130290882
2034531345274.0046213627-9.5399092468142138.9953786373288-0.677764440582749
2044556445155.6008842969-23.4187708581669408.399115703068-0.961289379753557
2054539245104.9760684322-26.8873318559778287.023931567835-0.240239801554801
2064511445082.9032473024-26.27351137741831.09675269759230.0425093695099643
2074499645087.2012206841-22.3759181985902-91.20122068412770.269893832269445
2084515945110.4932806949-16.553861858540748.50671930507430.403159657514745
2094510745143.8361239124-10.1928078133477-36.83612391236020.440532948547887
2104496345141.018596119-9.25256085983847-178.0185961189750.0651230149116661
2114500545158.9348275192-5.78881772820783-153.934827519230.239902105059984
2124502445169.966512071-3.64435293231317-145.9665120709560.148512157023041
2134502545135.9305204483-7.51894642475745-110.930520448292-0.268304995232234
2144505345131.1891839694-7.16484308671884-78.18918396941190.024520876881208
2154514345082.6235026574-12.442745790854160.3764973426127-0.3655183765284
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/1kf8q1293643822.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/1kf8q1293643822.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/2v78b1293643822.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/2v78b1293643822.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/35g7w1293643822.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/35g7w1293643822.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/45g7w1293643822.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/45g7w1293643822.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/5g76h1293643822.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293643689nxjlcaskq2er1uy/5g76h1293643822.ps (open in new window)


 
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')
 




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Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


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