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Seasonal decomposition of time series

*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: Mon, 29 Nov 2010 20:21:39 +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/Nov/29/t1291062054abkcxhjjlsbxw2s.htm/, Retrieved Mon, 29 Nov 2010 21:20:59 +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/Nov/29/t1291062054abkcxhjjlsbxw2s.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 «
235.1 280.7 264.6 240.7 201.4 240.8 241.1 223.8 206.1 174.7 203.3 220.5 299.5 347.4 338.3 327.7 351.6 396.6 438.8 395.6 363.5 378.8 357 369 464.8 479.1 431.3 366.5 326.3 355.1 331.6 261.3 249 205.5 235.6 240.9 264.9 253.8 232.3 193.8 177 213.2 207.2 180.6 188.6 175.4 199 179.6 225.8 234 200.2 183.6 178.2 203.2 208.5 191.8 172.8 148 159.4 154.5 213.2 196.4 182.8 176.4 153.6 173.2 171 151.2 161.9 157.2 201.7 236.4 356.1 398.3 403.7 384.6 365.8 368.1 367.9 347 343.3 292.9 311.5 300.9 366.9 356.9 329.7 316.2 269 289.3 266.2 253.6 233.8 228.4 253.6 260.1 306.6 309.2 309.5 271 279.9 317.9 298.4 246.7 227.3 209.1 259.9 266 320.6 308.5 282.2 262.7 263.5 313.1 284.3 252.6 250.3 246.5 312.7 333.2 446.4 511.6 515.5 506.4 483.2 522.3 509.8 460.7 405.8 375 378.5 406.8 467.8 469.8 429.8 355.8 332.7 378 360.5 334.7 319.5 323.1 363.6 352.1 411.9 388.6 416.4 360.7 338 417.2 388. etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal37210373
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1235.1214.82909453061945.7272473679813209.643658101400-20.2709054693809
2280.7293.79906118565654.3836745318679213.21726428247613.0990611856561
3264.6281.53353477728030.8755947591673216.79087046355216.9335347772804
4240.7269.75280229795-9.14411997212568220.79131767417629.05280229795
5201.4208.788200694984-30.7799655797835224.7917648847997.38820069498442
6240.8211.24207795285141.0692991453643229.288622901785-29.5579220471488
7241.1230.99274984362117.4217692376093233.78548091877-10.1072501563793
8223.8223.647314722719-14.5248380372195238.477523314501-0.152685277281023
9206.1201.463179816058-32.4327455262893243.169565710231-4.63682018394167
10174.7147.954837313879-48.7799560450432250.225118731164-26.7451626861208
11203.3176.933596335576-27.6142680876733257.280671752097-26.3664036644237
12220.5196.773631118990-26.2016910964050270.428059977415-23.7263688810096
13299.5269.69730442928745.7272473679813283.575448202732-29.8026955707135
14347.4341.18966480895254.3836745318679299.22666065918-6.21033519104799
15338.3330.84653212520530.8755947591673314.877873115628-7.4534678747952
16327.7334.024311334212-9.14411997212568330.5198086379136.32431133421221
17351.6387.818221419585-30.7799655797835346.16174416019936.2182214195846
18396.6392.44168442109841.0692991453643359.689016433538-4.15831557890215
19438.8486.96194205551417.4217692376093373.21628870687748.1619420555141
20395.6423.056856557164-14.5248380372195382.66798148005627.4568565571638
21363.5367.313071273055-32.4327455262893392.1196742532353.81307127305456
22378.8411.165256850869-48.7799560450432395.21469919417532.3652568508687
23357343.304543952559-27.6142680876733398.309724135114-13.6954560474410
24369369.971265782356-26.2016910964050394.2304253140480.971265782356454
25464.8493.72162613903645.7272473679813390.15112649298328.9216261390361
26479.1523.19178163574954.3836745318679380.62454383238344.091781635749
27431.3460.62644406904930.8755947591673371.09796117178429.3264440690491
28366.5383.426685670162-9.14411997212568358.71743430196416.9266856701622
29326.3337.04305814764-30.7799655797835346.33690743214410.74305814764
30355.1337.28500342411341.0692991453643331.845697430523-17.8149965758873
31331.6328.42374333348817.4217692376093317.354487428903-3.17625666651179
32261.3235.82392099443-14.5248380372195301.300917042789-25.4760790055698
33249245.185398869613-32.4327455262893285.247346656676-3.81460113038679
34205.5188.960995855916-48.7799560450432270.818960189127-16.5390041440842
35235.6242.423694366095-27.6142680876733256.3905737215796.82369436609463
36240.9262.947178934096-26.2016910964050245.05451216230922.0471789340964
37264.9250.3543020289845.7272473679813233.718450603038-14.5456979710197
38253.8227.35680015873454.3836745318679225.859525309398-26.4431998412663
39232.3215.72380522507430.8755947591673218.000600015758-16.5761947749257
40193.8183.23812197173-9.14411997212568213.505998000396-10.5618780282699
41177175.768569594751-30.7799655797835209.011395985033-1.23143040524931
42213.2178.85448915752541.0692991453643206.476211697111-34.3455108424754
43207.2193.03720335320117.4217692376093203.941027409189-14.1627966467986
44180.6173.434705223465-14.5248380372195202.290132813754-7.1652947765348
45188.6208.99350730797-32.4327455262893200.63923821831920.3935073079700
46175.4200.008141107515-48.7799560450432199.57181493752824.6081411075147
47199227.109876430936-27.6142680876733198.50439165673828.1098764309356
48179.6187.797222560777-26.2016910964050197.6044685356288.19722256077651
49225.8209.16820721750045.7272473679813196.704545414519-16.6317927825005
50234218.11136778348354.3836745318679195.504957684649-15.8886322165167
51200.2175.21903528605430.8755947591673194.305369954778-24.9809647139458
52183.6183.576379040694-9.14411997212568192.767740931431-0.0236209593056174
53178.2195.949853671699-30.7799655797835191.23011190808417.7498536716994
54203.2175.39237057170241.0692991453643189.938330282934-27.8076294282983
55208.5210.93168210460717.4217692376093188.6465486577842.43168210460686
56191.8211.120421708298-14.5248380372195187.00441632892219.3204217082977
57172.8192.670461526230-32.4327455262893185.36228400006019.8704615262295
58148161.531109431451-48.7799560450432183.24884661359213.5311094314512
59159.4165.278858860549-27.6142680876733181.1354092271245.87885886054909
60154.5156.979150209213-26.2016910964050178.2225408871922.47915020921343
61213.2205.3630800847645.7272473679813175.309672547259-7.83691991524009
62196.4165.58847329643954.3836745318679172.827852171693-30.8115267035610
63182.8164.37837344470530.8755947591673170.346031796127-18.4216265552946
64176.4190.713879133249-9.14411997212568171.23024083887714.3138791332486
65153.6165.865515698157-30.7799655797835172.11444988162712.2655156981566
66173.2125.90256144793941.0692991453643179.428139406697-47.297438552061
67171137.83640183062417.4217692376093186.741828931767-33.1635981693758
68151.2115.949008974742-14.5248380372195200.975829062477-35.2509910252575
69161.9141.022916333102-32.4327455262893215.209829193187-20.8770836668981
70157.2129.322647644385-48.7799560450432233.857308400658-27.8773523556147
71201.7178.509480479545-27.6142680876733252.504787608128-23.1905195204551
72236.4227.525189593787-26.2016910964050271.476501502618-8.87481040621276
73356.1376.02453723491245.7272473679813290.44821539710719.9245372349117
74398.3435.31159205189654.3836745318679306.90473341623637.0115920518963
75403.7453.16315380546830.8755947591673323.36125143536549.4631538054681
76384.6443.862755685216-9.14411997212568334.48136428691059.2627556852162
77365.8416.778488441329-30.7799655797835345.60147713845550.978488441329
78368.1345.65740234397441.0692991453643349.473298510662-22.4425976560265
79367.9365.03311087952117.4217692376093353.34511988287-2.86688912047924
80347358.175673701868-14.5248380372195350.34916433535111.1756737018680
81343.3371.679536738456-32.4327455262893347.35320878783328.3795367384564
82292.9293.325431908523-48.7799560450432341.2545241365200.425431908522739
83311.5315.458428602465-27.6142680876733335.1558394852083.95842860246535
84300.9300.305102354594-26.2016910964050327.696588741811-0.594897645405865
85366.9367.83541463360545.7272473679813320.2373379984140.935414633605092
86356.9347.26183819747054.3836745318679312.154487270662-9.6381618025303
87329.7324.45276869792230.8755947591673304.071636542911-5.24723130207838
88316.2344.230682837504-9.14411997212568297.31343713462228.0306828375038
89269278.224727853451-30.7799655797835290.5552377263339.22472785345082
90289.3251.62118366501341.0692991453643285.909517189622-37.6788163349867
91266.2233.71443410947917.4217692376093281.263796652912-32.4855658905214
92253.6243.823300807488-14.5248380372195277.901537229731-9.77669919251156
93233.8225.493467719739-32.4327455262893274.53927780655-8.30653228026068
94228.4232.083657756684-48.7799560450432273.4962982883593.68365775668394
95253.6262.360949317505-27.6142680876733272.4533187701698.76094931750464
96260.1272.779548006722-26.2016910964050273.62214308968312.6795480067221
97306.6292.68178522282245.7272473679813274.790967409197-13.9182147771783
98309.2288.56465004171854.3836745318679275.451675426414-20.6353499582817
99309.5312.01202179720230.8755947591673276.1123834436312.51202179720212
100271275.341180501594-9.14411997212568275.8029394705314.34118050159447
101279.9315.086470082352-30.7799655797835275.49349549743235.1864700823515
102317.9319.15552598858541.0692991453643275.5751748660511.25552598858462
103298.4303.72137652772117.4217692376093275.656854234675.32137652772053
104246.7232.761139025409-14.5248380372195275.163699011811-13.9388609745915
105227.3212.362201737338-32.4327455262893274.670543788952-14.9377982626625
106209.1193.492634656657-48.7799560450432273.487321388387-15.6073653433435
107259.9275.110169099852-27.6142680876733272.30409898782215.2101690998517
108266286.454346617531-26.2016910964050271.74734447887420.4543466175314
109320.6324.28216266209345.7272473679813271.1905899699263.68216266209316
110308.5290.81918269950854.3836745318679271.797142768624-17.6808173004924
111282.2261.12070967350930.8755947591673272.403695567323-21.0792903264906
112262.7259.697274628193-9.14411997212568274.846845343933-3.00272537180729
113263.5280.489970459241-30.7799655797835277.28999512054316.9899704592409
114313.1300.85078715063741.0692991453643284.279913703998-12.2492128493627
115284.3259.90839847493617.4217692376093291.269832287454-24.3916015250635
116252.6214.327883580445-14.5248380372195305.396954456774-38.2721164195545
117250.3213.508668900196-32.4327455262893319.524076626094-36.7913310998045
118246.5202.864031697315-48.7799560450432338.915924347728-43.6359683026847
119312.7294.706496018311-27.6142680876733358.307772069362-17.9935039816887
120333.2313.671619249712-26.2016910964050378.930071846692-19.5283807502876
121446.4447.52038100799645.7272473679813399.5523716240231.12038100799566
122511.6551.52514867951454.3836745318679417.29117678861939.9251486795135
123515.5565.09442328761930.8755947591673435.02998195321449.5944232876186
124506.4576.219814193543-9.14411997212568445.72430577858269.8198141935434
125483.2540.761335975833-30.7799655797835456.41862960395057.561335975833
126522.3544.46958918242441.0692991453643459.06111167221222.1695891824235
127509.8540.47463702191717.4217692376093461.70359374047430.674637021917
128460.7478.988211220689-14.5248380372195456.9366268165318.2882112206894
129405.8391.863085633703-32.4327455262893452.169659892586-13.9369143662972
130375356.707163207141-48.7799560450432442.072792837902-18.2928367928589
131378.5352.638342304456-27.6142680876733431.975925783218-25.8616576955444
132406.8419.400479886467-26.2016910964050420.40121120993812.6004798864665
133467.8481.04625599536045.7272473679813408.82649663665913.2462559953596
134469.8486.06529600581354.3836745318679399.15102946231916.2652960058128
135429.8439.24884295285330.8755947591673389.4755622879799.44884295285334
136355.8337.498881393317-9.14411997212568383.245238578809-18.3011186066833
137332.7319.165050710145-30.7799655797835377.014914869639-13.5349492898552
138378342.20777041894141.0692991453643372.722930435695-35.792229581059
139360.5335.1472847606417.4217692376093368.430946001751-25.3527152393601
140334.7318.16864624025-14.5248380372195365.756191796969-16.5313537597499
141319.5308.351307934101-32.4327455262893363.081437592188-11.1486920658987
142323.1331.782040314158-48.7799560450432363.1979157308858.68204031415809
143363.6391.499874218091-27.6142680876733363.31439386958227.8998742180910
144352.1365.138959637451-26.2016910964050365.26273145895413.0389596374507
145411.9410.86168358369245.7272473679813367.211069048326-1.03831641630757
146388.6353.79846615534154.3836745318679369.017859312791-34.8015338446591
147416.4431.09975566357630.8755947591673370.82464957725614.6997556635765
148360.7357.110730835223-9.14411997212568373.433389136903-3.58926916477731
149338330.737836883234-30.7799655797835376.04212869655-7.2621631167662
150417.2410.61296505073641.0692991453643382.7177358039-6.58703494926425
151388.4369.98488785114017.4217692376093389.39334291125-18.4151121488596
152371.1356.231897386812-14.5248380372195400.492940650407-14.8681026131878
153331.5283.840207136725-32.4327455262893411.592538389564-47.6597928632751
154353.7332.406095296224-48.7799560450432423.773860748819-21.2939047037756
155396.7385.0590849796-27.6142680876733435.955183108073-11.6409150203999
156447473.189936518508-26.2016910964050447.01175457789726.1899365185077
157533.5563.20442658429745.7272473679813458.06832604772129.7044265842974
158565.4610.86466455143954.3836745318679465.55166091669345.4646645514388
159542.3580.68940945516730.8755947591673473.03499578566538.3894094551675
160488.7511.798167286059-9.14411997212568474.74595268606723.0981672860590
161467.1488.523055993315-30.7799655797835476.45690958646821.4230559933155
162531.3549.54177056845541.0692991453643471.98893028618118.2417705684549
163496.1507.25727977649717.4217692376093467.52095098589311.1572797764973
164444443.284468948841-14.5248380372195459.240369088379-0.715531051159303
165403.4388.272958335425-32.4327455262893450.959787190864-15.1270416645748
166386.3379.078691590792-48.7799560450432442.301264454251-7.22130840920767
167394.1382.171526370036-27.6142680876733433.642741717638-11.9284736299644
168404.1408.466851475278-26.2016910964050425.9348396211274.36685147527783
169462.1460.24581510740245.7272473679813418.226937524616-1.85418489259774
170448.1429.70306660530254.3836745318679412.11325886283-18.3969333946976
171432.3427.7248250397930.8755947591673405.999580201043-4.57517496021012
172386.3379.932992247982-9.14411997212568401.811127724143-6.36700775201768
173395.2423.557290332539-30.7799655797835397.62267524724428.3572903325395
174421.9406.76149385414141.0692991453643395.969207000495-15.1385061458589
175382.9354.06249200864617.4217692376093394.315738753745-28.8375079913543
176384.2387.891409338233-14.5248380372195395.0334286989863.69140933823337
177345.5327.681626882062-32.4327455262893395.751118644227-17.818373117938
178323.4297.945930911204-48.7799560450432397.63402513384-25.4540690887964
179372.6373.297336464221-27.6142680876733399.5169316234520.697336464221451
180376376.513021945884-26.2016910964050401.6886691505210.513021945883565
181462.7475.81234595442845.7272473679813403.86040667759113.1123459544278
182487514.37574970678554.3836745318679405.24057576134727.3757497067851
183444.2450.9036603957330.8755947591673406.6207448451036.7036603957298
184399.3401.029952789863-9.14411997212568406.7141671822621.72995278986338
185394.9413.772376060362-30.7799655797835406.80758951942218.8723760603617
186455.4463.85675736115141.0692991453643405.8739434934858.45675736115055
187414405.63793329484217.4217692376093404.940297467548-8.36206670515776
188375.5362.369098573574-14.5248380372195403.155739463645-13.1309014264255
189347325.061564066548-32.4327455262893401.371181459741-21.9384359334522
190339.4328.083789888226-48.7799560450432399.496166156817-11.3162101117741
191385.8401.593117233780-27.6142680876733397.62115085389315.7931172337804
192378.8387.993042812955-26.2016910964050395.808648283459.19304281295473
193451.8463.87660691901145.7272473679813393.99614571300812.0766069190112
194446.1445.94145747044754.3836745318679391.874867997685-0.158542529552903
195422.5424.3708149584730.8755947591673389.7535902823621.87081495847031
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198445.3469.69518597935741.0692991453643379.83551487527824.3951859793573
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294542.6557.8870815167441.0692991453643486.24361933789615.2870815167401
295517.3534.6212473885717.4217692376093482.5569833738217.3212473885702
296485.7508.482353032142-14.5248380372195477.44248500507722.7823530321421
297465.8491.704758889955-32.4327455262893472.32798663633425.9047588899550
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299426.6420.149049948906-27.6142680876733460.665218138767-6.45095005109386
300411.6394.754195906014-26.2016910964050454.647495190391-16.8458040939860
301467.5440.64298039000445.7272473679813448.629772242015-26.8570196099959
302484.5471.16993125320754.3836745318679443.446394214925-13.3300687467930
303451.2433.26138905299730.8755947591673438.263016187835-17.9386109470027
304417.4408.588920891701-9.14411997212568435.355199080424-8.81107910829854
305379.9358.132583606770-30.7799655797835432.447381973013-21.7674163932295
306484.7495.79391952252041.0692991453643432.53678133211611.0939195225195
307455459.95205007117117.4217692376093432.6261806912194.95205007117124
308420.8421.629786515983-14.5248380372195434.4950515212360.829786515983187
309416.5429.068823175036-32.4327455262893436.36392235125312.5688231750361
310376.3362.737391673690-48.7799560450432438.642564371353-13.5626083263096
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312405.8393.205936461850-26.2016910964050444.595754634555-12.5940635381496
313500.8507.60244975436245.7272473679813448.2703028776566.80244975436233
314514518.7520696954254.3836745318679454.8642557727124.75206969541961
315475.5458.66619657306430.8755947591673461.458208667769-16.8338034269358
316430.1396.985507890066-9.14411997212568472.35861208206-33.1144921099344
317414.4376.320950083432-30.7799655797835483.259015496352-38.0790499165681
318538533.60938039926141.0692991453643501.321320455375-4.39061960073889
319526515.19460534799317.4217692376093519.383625414398-10.8053946520070
320488.5445.929816598915-14.5248380372195545.595021438304-42.5701834010845
321520.2501.026328064079-32.4327455262893571.80641746221-19.173671935921
322504.4455.492843514319-48.7799560450432602.087112530724-48.9071564856807
323568.5532.246460488436-27.6142680876733632.367807599237-36.253539511564
324610.6586.173699944291-26.2016910964050661.227991152114-24.4263000557088
325818900.18457792702945.7272473679813690.0881747049982.1845779270286
326830.9893.21468141787254.3836745318679714.2016440502662.314681417872
327835.9902.60929184530330.8755947591673738.3151133955366.7092918453028
328782818.767234243614-9.14411997212568754.37688572851136.7672342436143
329762.3784.94130751829-30.7799655797835770.43865806149322.6413075182905
330856.9896.47021830389741.0692991453643776.26048255073939.5702183038967
331820.9842.29592372240617.4217692376093782.08230703998521.3959237224055
332769.6774.491826208536-14.5248380372195779.2330118286844.89182620853569
333752.2760.449028908907-32.4327455262893776.3837166173838.24902890890678
334724.4728.719956043672-48.7799560450432768.8600000013714.31995604367216
335723.1712.477984702314-27.6142680876733761.336283385359-10.6220152976858
336719.5711.792069242997-26.2016910964050753.409621853408-7.70793075700328
337817.4843.58979231056145.7272473679813745.48296032145726.1897923105614
338803.3812.05615568618554.3836745318679740.1601697819478.75615568618468
339752.5739.28702599839530.8755947591673734.837379242437-13.2129740016046
340689655.172295290788-9.14411997212568731.971824681337-33.8277047092117
341630.4562.473695459546-30.7799655797835729.106270120238-67.9263045404541
342765.5761.99356103171541.0692991453643727.93713982292-3.50643896828478
343757.7771.21022123678717.4217692376093726.76800952560313.5102212367873
344732.2751.82654245377-14.5248380372195727.0982955834519.6265424537694
345702.6710.204163884992-32.4327455262893727.4285816412977.60416388499243
346683.3688.558515066544-48.7799560450432726.82144097855.25851506654385
347709.5720.399967771972-27.6142680876733726.21430031570210.8999677719717
348702.2707.766749750501-26.2016910964050722.8349413459045.566749750501
349784.8804.41717025591245.7272473679813719.45558237610619.6171702559125
350810.9853.30055489880854.3836745318679714.11577056932442.4005548988077
351755.6771.54844647829130.8755947591673708.77595876254215.9484464782906
352656.8620.197589074523-9.14411997212568702.546530897603-36.6024109254773
353615.1564.66286254712-30.7799655797835696.317103032664-50.4371374528804
354745.3761.07575859312141.0692991453643688.45494226151415.7757585931213
355694.1690.18544927202617.4217692376093680.592781490365-3.91455072797407
356675.7693.6588622882-14.5248380372195672.2659757490217.9588622882000
357643.7655.893575518615-32.4327455262893663.93917000767412.1935755186150
358622.1637.039310150027-48.7799560450432655.94064589501614.9393101500268
359634.6648.872146305315-27.6142680876733647.94212178235814.2721463053151
360588561.989746209275-26.2016910964050640.21194488713-26.0102537907248
361689.7701.19098464011845.7272473679813632.48176799190111.4909846401175
362673.9667.74424540596354.3836745318679625.672080062169-6.15575459403692
363647.9646.06201310839630.8755947591673618.862392132437-1.83798689160380
364568.8531.505368588922-9.14411997212568615.238751383204-37.2946314110781
365545.7510.564854945813-30.7799655797835611.615110633971-35.1351450541874
366632.6616.09556261465341.0692991453643608.035138239983-16.5044373853473
367643.8665.72306491639617.4217692376093604.45516584599521.9230649163957
368593.1599.40580716076-14.5248380372195601.319030876466.30580716075997
369579.7593.649849619365-32.4327455262893598.18289590692413.9498496193652
370546545.275613718932-48.7799560450432595.504342326111-0.724386281068064
371562.9560.588479342375-27.6142680876733592.825788745298-2.31152065762501
372572.5580.789109242778-26.2016910964050590.4125818536278.28910924277773
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062054abkcxhjjlsbxw2s/1srj91291062089.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062054abkcxhjjlsbxw2s/1srj91291062089.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062054abkcxhjjlsbxw2s/231id1291062089.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062054abkcxhjjlsbxw2s/231id1291062089.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062054abkcxhjjlsbxw2s/331id1291062089.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062054abkcxhjjlsbxw2s/331id1291062089.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062054abkcxhjjlsbxw2s/4wazy1291062089.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062054abkcxhjjlsbxw2s/4wazy1291062089.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
Parameters (R input):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
R code (references can be found in the software module):
par1 <- as.numeric(par1) #seasonal period
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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