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Seasonal Decomposition

*The author of this computation has been verified*
R Software Module: /rwasp_decomposeloess.wasp (opens new window with default values)
Title produced by software: Decomposition by Loess
Date of computation: Tue, 14 Dec 2010 14:11:28 +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/14/t1292335796q7mox9497uedjag.htm/, Retrieved Tue, 14 Dec 2010 15:10:02 +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/14/t1292335796q7mox9497uedjag.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 «
1,3866 1,3582 1,3332 1,3595 1,3617 1,3684 1,3394 1,3262 1,3173 1,3085 1,327 1,3182 1,293 1,291 1,2984 1,2795 1,299 1,3174 1,326 1,3111 1,2816 1,276 1,2849 1,2818 1,2829 1,2796 1,3008 1,2967 1,2938 1,2833 1,2823 1,2765 1,2634 1,2596 1,2705 1,2591 1,2798 1,2763 1,2795 1,2782 1,2644 1,2596 1,2615 1,2555 1,2555 1,2658 1,2565 1,2783 1,2786 1,2782 1,2905 1,3042 1,2942 1,313 1,3671 1,3549 1,3558 1,3507 1,3494 1,3607 1,3295 1,3193 1,3308 1,3246 1,3392 1,3425 1,3496 1,3255 1,3231 1,3273 1,3276 1,3173 1,3196 1,3058 1,2966 1,2932 1,2947 1,305 1,3232 1,3125 1,2992 1,3266 1,3275 1,3223 1,3403 1,3322 1,3363 1,3425 1,3574 1,3683 1,3623 1,3563 1,3518 1,3494 1,3612 1,369 1,3771 1,3972 1,401 1,3908 1,3901 1,3856 1,4098 1,422 1,4238 1,4207 1,4095 1,4177 1,3866 1,3959 1,4102 1,3969 1,4004 1,385 1,389 1,384 1,392 1,3932 1,3858 1,3978 1,4029 1,394 1,4096 1,4058 1,4134 1,4096 1,4049 1,4009 1,3897 1,4019 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 seconds
R Server'George Udny Yule' @ 72.249.76.132


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal49110492
Trend911
Low-pass511


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
11.38661.40540095921826-0.001133703370409551.368932744152150.0188009592182583
21.35821.35229328128065-0.0006619510692042971.36476866978856-0.00590671871935422
31.33321.305285685054440.0002066171914247131.36090769775414-0.0279143149455607
41.35951.361873177900290.000499896635563841.356626925464150.00237317790028979
51.36171.368674528352290.001089140563050921.353636331084650.00697452835229484
61.36841.38784283892511-0.001133703370409551.350090864445300.0194428389251073
71.33941.33719343514027-0.0006619510692042971.34226851592893-0.00220656485972559
81.32621.320055710947470.0002066171914247131.33213767186111-0.00614428905253273
91.31731.310054162159210.000499896635563841.32404594120523-0.00724583784078892
101.30851.297207978769270.001089140563050921.31870288066768-0.0112920212307261
111.3271.34157998621299-0.001133703370409551.313553717157420.0145799862129898
121.31821.3283976410882-0.0006619510692042971.308664309981010.0101976410881983
131.2931.282241552658740.0002066171914247131.30355183014983-0.0107584473412563
141.2911.284500970250810.000499896635563841.29699913311363-0.00649902974918937
151.29841.301820217221630.001089140563050921.293890642215320.00342021722163088
161.27951.26306097697913-0.001133703370409551.29707272639128-0.0164390230208746
171.2991.29586316105715-0.0006619510692042971.30279879001206-0.00313683894285210
181.31741.327888382568780.0002066171914247131.306705000239790.0104883825687805
191.3261.344723271684390.000499896635563841.306776831680040.0187232716843924
201.31111.318796380381090.001089140563050921.302314479055850.00769638038109455
211.28161.26939212793940-0.001133703370409551.29494157543101-0.0122078720605960
221.2761.26535425109004-0.0006619510692042971.28730769997916-0.0106457489099565
231.28491.287061093381960.0002066171914247131.282532289426610.00216109338196313
241.28181.28084832984020.000499896635563841.28225177352424-0.000951670159799134
251.28291.279571718861770.001089140563050921.28513914057518-0.00332828113822980
261.27961.27206232280572-0.001133703370409551.28827138056469-0.00753767719428078
271.30081.31175361886303-0.0006619510692042971.290508332206170.0109536188630328
281.29671.301834452380490.0002066171914247131.291358930428080.00513445238049037
291.29381.296868613864810.000499896635563841.290231489499620.0030686138648115
301.28331.279421092555520.001089140563050921.28608976688143-0.00387890744447827
311.28231.28581248978212-0.001133703370409551.279921213588290.00351248978211882
321.27651.27961581484468-0.0006619510692042971.274046136224530.00311581484467927
331.26341.256872807724020.0002066171914247131.26972057508455-0.00652719227597709
341.25961.251954699815040.000499896635563841.26674540354939-0.00764530018495546
351.27051.273210729145480.001089140563050921.266700130291470.00271072914547799
361.25911.25001243433068-0.001133703370409551.26932126903973-0.0090875656693199
371.27981.28770237679997-0.0006619510692042971.272559574269230.00790237679997063
381.27631.277667109184370.0002066171914247131.274726273624210.00136710918437011
391.27951.283786489088360.000499896635563841.274713614276080.00428648908835938
401.27821.283296838418460.001089140563050921.272014021018490.00509683841846398
411.26441.26176724059860-0.001133703370409551.26816646277181-0.0026327594013964
421.25961.25609010761273-0.0006619510692042971.26377184345647-0.00350989238726962
431.26151.262494941764860.0002066171914247131.260298441043720.00099494176485826
441.25551.251310637364490.000499896635563841.25918946599995-0.00418936263551339
451.25551.250176861713460.001089140563050921.25973399772349-0.00532313828654107
461.26581.27027053588837-0.001133703370409551.262463167482040.00447053588837143
471.25651.24680920503135-0.0006619510692042971.26685274603785-0.00969079496865044
481.27831.284785358146800.0002066171914247131.271608024661770.00648535814680429
491.27861.279225166830230.000499896635563841.277474936534200.000625166830232171
501.27821.270980964344890.001089140563050921.28432989509206-0.00721903565510895
511.29051.29207637592713-0.001133703370409551.290057327443280.00157637592713011
521.30421.31063384742287-0.0006619510692042971.298428103646330.00643384742286934
531.29421.276200297733030.0002066171914247131.31199308507555-0.017999702266972
541.3131.299642537725180.000499896635563841.32585756563926-0.0133574622748205
551.36711.395156783572630.001089140563050921.337954075864310.0280567835726342
561.35491.36273699594179-0.001133703370409551.348196707428620.00783699594178566
571.35581.35853123894674-0.0006619510692042971.353730712122460.00273123894673932
581.35071.347861429127450.0002066171914247131.35333195368112-0.00283857087254624
591.34941.349324074606280.000499896635563841.34897602875815-7.59253937161475e-05
601.36071.377260522597940.001089140563050921.343050336839010.0165605225979371
611.32951.32254151046868-0.001133703370409551.33759219290173-0.00695848953131994
621.31931.30662144371355-0.0006619510692042971.33264050735566-0.0126785562864549
631.33081.331478608385850.0002066171914247131.329914774422730.000678608385847657
641.32461.316790507715170.000499896635563841.33190959564927-0.00780949228482997
651.33921.341568486846660.001089140563050921.335742372590290.00236848684665514
661.34251.34916856445964-0.001133703370409551.336965138910770.0066685644596407
671.34961.36395319342362-0.0006619510692042971.335908757645590.0143531934236156
681.32551.317452957278810.0002066171914247131.33334042552977-0.00804704272119183
691.32311.316233049341180.000499896635563841.32946705402325-0.0068669506588177
701.32731.32813049002930.001089140563050921.325380369407650.000830490029299913
711.32761.33369411842097-0.001133703370409551.322639584949440.00609411842096597
721.31731.31616883893011-0.0006619510692042971.31909311213909-0.00113116106988720
731.31961.325658542136260.0002066171914247131.313334840672320.00605854213625934
741.30581.304106234641420.000499896635563841.30699386872302-0.00169376535858090
751.29661.290073009455270.001089140563050921.30203784998168-0.00652699054473094
761.29321.28742082255319-0.001133703370409551.30011288081722-0.00577917744681278
771.29471.28791300343929-0.0006619510692042971.30214894762991-0.00678699656070925
781.3051.304383930809390.0002066171914247131.30540945199918-0.000616069190606972
791.32321.337387840671070.000499896635563841.308512262693360.0141878406710740
801.31251.311128410515380.001089140563050921.31278244892157-0.00137158948461713
811.29921.28316509510955-0.001133703370409551.31636860826086-0.0160349048904542
821.32661.33490950327613-0.0006619510692042971.318952447793070.00830950327613356
831.32751.33122218786320.0002066171914247131.323571194945380.00372218786319944
841.32231.315391388853030.000499896635563841.32870871451141-0.0069086111469725
851.34031.347511227411830.001089140563050921.331999632025120.00721122741183167
861.33221.32988473975096-0.001133703370409551.33564896361945-0.00231526024903617
871.33631.33211282214793-0.0006619510692042971.34114912892128-0.00418717785207345
881.34251.337530865142630.0002066171914247131.34726251766595-0.00496913485737216
891.35741.361202200102410.000499896635563841.353097903262030.00380220010241028
901.36831.378196220018040.001089140563050921.357314639418910.00989622001804147
911.36231.36699841613157-0.001133703370409551.358735287238840.00469841613156663
921.35631.35562802510370-0.0006619510692042971.35763392596551-0.000671974896301775
931.35181.346881974607080.0002066171914247131.35651140820149-0.00491802539291575
941.34941.340493400905090.000499896635563841.35780670245934-0.00890659909490621
951.36121.358717498408630.001089140563050921.36259336102832-0.00248250159137431
961.3691.36818644054012-0.001133703370409551.37094726283029-0.000813559459881708
971.37711.37468143156366-0.0006619510692042971.38018051950555-0.00241856843634447
981.39721.407150631257200.0002066171914247131.387042751551380.00995063125719509
991.4011.41042933299980.000499896635563841.391070770364640.0094293329998012
1001.39081.387380681808860.001089140563050921.39313017762809-0.00341931819114172
1011.39011.38594083446865-0.001133703370409551.39539286890176-0.00415916553135265
1021.38561.3720677568498-0.0006619510692042971.39979419421940-0.0135322431502007
1031.40981.413228145260430.0002066171914247131.406165237548140.00342814526043078
1041.4221.430982467278290.000499896635563841.412517636086150.00898246727828544
1051.42381.429677653084390.001089140563050921.416833206352560.00587765308438648
1061.42071.4255980193644-0.001133703370409551.416935684006010.00489801936440082
1071.40951.40742013933497-0.0006619510692042971.41224181173424-0.00207986066503385
1081.41771.428361970797640.0002066171914247131.406831412010930.0106619707976450
1091.38661.369250665239290.000499896635563841.40344943812514-0.0173493347607065
1101.39591.389750587912040.001089140563050921.40096027152491-0.00614941208795727
1111.41021.42255628059028-0.001133703370409551.398977422780130.0123562805902753
1121.39691.39668997977123-0.0006619510692042971.39777197129798-0.000210020228771812
1131.40041.405189430963800.0002066171914247131.395403951844780.00478943096379525
1141.3851.377628540375350.000499896635563841.39187156298908-0.00737145962464858
1151.3891.387196862762880.001089140563050921.38971399667407-0.00180313723711989
1161.3841.38019922071991-0.001133703370409551.38893448265049-0.00380077928008538
1171.3921.39540400325029-0.0006619510692042971.389257947818910.00340400325029022
1181.39321.395202188501640.0002066171914247131.390991194306930.00200218850164147
1191.38581.377711707845140.000499896635563841.39338839551930-0.00808829215485929
1201.39781.399115074991470.001089140563050921.395395784445480.00131507499147232
1211.40291.40867735161724-0.001133703370409551.398256351753170.00577735161723503
1221.3941.38685897674812-0.0006619510692042971.40180297432108-0.00714102325187915
1231.40961.414300840229030.0002066171914247131.404692542579540.00470084022903361
1241.40581.404308658596790.000499896635563841.40679144476765-0.00149134140321427
1251.41341.417785486802540.001089140563050921.407925372634410.00438548680253970
1261.40961.41353070016785-0.001133703370409551.406803003202560.0039307001678508
1271.40491.40643632984515-0.0006619510692042971.404025621224050.00153632984515339
1281.40091.400705163619350.0002066171914247131.40088821918923-0.000194836380649877
1291.38971.380950818012410.000499896635563841.39794928535202-0.00874918198758512
1301.40191.406701475822900.001089140563050921.396009383614050.00480147582290313
1311.39011.38624667400906-0.001133703370409551.39508702936135-0.00385332599094457
1321.3991.40346939420089-0.0006619510692042971.395192556868320.0044693942008871
1331.39011.383964113623960.0002066171914247131.39602926918462-0.00613588637604434
1341.39751.396031431019490.000499896635563841.39846867234495-0.00146856898051162
1351.39911.395225736409380.001089140563050921.40188512302757-0.00387426359061793
1361.40891.41306564848577-0.001133703370409551.405868054884640.00416564848576795
1371.4131.41630181325247-0.0006619510692042971.410360137816740.00330181325246603
1381.4091.403442199831450.0002066171914247131.41435118297713-0.00555780016855034
1391.42171.425848159903070.000499896635563841.417051943461370.00414815990306883
1401.42231.424219904781330.001089140563050921.419290954655620.00191990478132964
1411.41911.41796731112886-0.001133703370409551.42136639224155-0.00113268887114115
1421.42291.42390611814967-0.0006619510692042971.422555832919540.00100611814966611
1431.42271.422611708161210.0002066171914247131.42258167464736-8.82918387883347e-05
1441.42691.432317618096420.000499896635563841.420982485268020.00541761809641517
1451.42291.426532972110780.001089140563050921.418177887326170.0036329721107804
1461.41041.40576585360823-0.001133703370409551.41616784976218-0.00463414639176807
1471.40531.39468547606075-0.0006619510692042971.41657647500845-0.0106145239392452
1481.41381.407371181389850.0002066171914247131.42002220141872-0.00642881861014821
1491.43031.434186975118850.000499896635563841.425913128245590.00388697511884595
1501.43841.443714224685910.001089140563050921.431996634751040.00531422468590592
1511.4411.44778165614213-0.001133703370409551.435352047228280.00678165614213366
1521.4371.44042481803475-0.0006619510692042971.434237133034460.00342481803474892
1531.43571.441081387840090.0002066171914247131.430111994968480.00538138784009057
1541.42021.414077899574280.000499896635563841.42582220379015-0.00612210042571504
1551.41661.408725695836310.001089140563050921.42338516360064-0.00787430416369217
1561.4171.41303276839569-0.001133703370409551.42210093497472-0.00396723160431245
1571.42931.43864643180206-0.0006619510692042971.420615519267140.0093464318020613
1581.42941.439661877865240.0002066171914247131.418931504943330.0102618778652432
1591.40721.396678848758560.000499896635563841.41722125460587-0.0105211512414372
1601.41011.402802685897190.001089140563050921.41630817353976-0.00729731410280787
1611.41121.40562653715546-0.001133703370409551.41790716621495-0.00557346284453875
1621.42431.42710153207224-0.0006619510692042971.422160418996970.00280153207223521
1631.4331.439174534726090.0002066171914247131.426618848082480.00617453472609242
1641.43231.434707444685940.000499896635563841.429392658678490.00240744468594412
1651.43241.433356519096240.001089140563050921.430354340340710.000956519096235287
1661.4271.42465457544312-0.001133703370409551.43047912792729-0.00234542455688458
1671.42681.42411850070333-0.0006619510692042971.43014345036588-0.00268149929667461
1681.43641.44291505623030.0002066171914247131.429678326578280.00651505623029935
1691.42721.424537219019880.000499896635563841.42936288434456-0.00266278098012074
1701.43141.432512226138690.001089140563050921.429198633298260.00111222613868689
1711.4221.41638853235280-0.001133703370409551.42874517101761-0.00561146764719678
1721.43351.43805715176805-0.0006619510692042971.429604799301160.00455715176804916
1731.42621.419349863100700.0002066171914247131.43284351970788-0.00685013689930236
1741.4331.427710660169750.000499896635563841.43778944319468-0.0052893398302456
1751.44731.450245855777570.001089140563050921.443265003659380.00294585577756723
1761.45221.45650007517133-0.001133703370409551.449033628199080.00430007517133335
1771.45451.45601994111012-0.0006619510692042971.453642009959090.00151994111011788
1781.45941.461862767990810.0002066171914247131.456730614817760.00246276799081313
1791.45611.452105837343390.000499896635563841.45959426602105-0.00399416265661023
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4291.28341.285452239572300.000499896635563841.280847863792140.00205223957229728
4301.28741.294169812859570.001089140563050921.279541046577380.00676981285956524
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4391.3061.304648617169950.000499896635563841.30685148619449-0.00135138283005420
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4881.33211.333273241319590.0002066171914247131.330720141488990.00117324131958996
4891.32251.323331333991670.000499896635563841.321168769372770.00083133399166524
4901.31461.316386513325960.001089140563050921.311724346110990.00178651332595736
4911.29981.29839090449318-0.001133703370409551.30234279887723-0.00140909550681600
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335796q7mox9497uedjag/148d61292335871.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335796q7mox9497uedjag/148d61292335871.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335796q7mox9497uedjag/2ficr1292335871.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335796q7mox9497uedjag/2ficr1292335871.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335796q7mox9497uedjag/3ficr1292335871.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335796q7mox9497uedjag/3ficr1292335871.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335796q7mox9497uedjag/4qrtc1292335871.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335796q7mox9497uedjag/4qrtc1292335871.ps (open in new window)


 
Parameters (Session):
par1 = 5 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
Parameters (R input):
par1 = 5 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
R code (references can be found in the software module):
par1 <- 5
if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window
par3 <- as.numeric(par3) #s.degree
if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window
par5 <- as.numeric(par5)#t.degree
if (par6 != '') par6 <- as.numeric(par6)#l.window
par7 <- as.numeric(par7)#l.degree
if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust
nx <- length(x)
x <- ts(x,frequency=par1)
if (par6 != '') {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8)
} else {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8)
}
m$time.series
m$win
m$deg
m$jump
m$inner
m$outer
bitmap(file='test1.png')
plot(m,main=main)
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Component',header=TRUE)
a<-table.element(a,'Window',header=TRUE)
a<-table.element(a,'Degree',header=TRUE)
a<-table.element(a,'Jump',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,m$win['s'])
a<-table.element(a,m$deg['s'])
a<-table.element(a,m$jump['s'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,m$win['t'])
a<-table.element(a,m$deg['t'])
a<-table.element(a,m$jump['t'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Low-pass',header=TRUE)
a<-table.element(a,m$win['l'])
a<-table.element(a,m$deg['l'])
a<-table.element(a,m$jump['l'])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',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,'Fitted',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Remainder',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,x[i]+m$time.series[i,'remainder'])
a<-table.element(a,m$time.series[i,'seasonal'])
a<-table.element(a,m$time.series[i,'trend'])
a<-table.element(a,m$time.series[i,'remainder'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 





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Software written by Ed van Stee & Patrick Wessa


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