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Task 9: Time series plot

*The author of this computation has been verified*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 03 Oct 2010 16:44:08 +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/Oct/03/t1286124219243l1ww6628atj3.htm/, Retrieved Sun, 03 Oct 2010 18:43:45 +0200
 
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/Oct/03/t1286124219243l1ww6628atj3.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 «
13328 12873 14000 13477 14237 13674 13529 14058 12975 14326 14008 16193 14483 14011 15057 14884 15414 14440 14900 15074 14442 15307 14938 17193 15528 14765 15838 15723 16150 15486 15986 15983 15692 16490 15686 18897 16316 15636 17163 16534 16518 16375 16290 16352 15943 16362 16393 19051 16747 16320 17910 16961 17480 17049 16879 17473 16998 17307 17418 20169 17871 17226 19062 17804 19100 18522 18060 18869 18127 18871 18890 21263 19547 18450 20254 19240 20216 19420 19415 20018 18652 19978 19509 21971 16441,5
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
113328NANA-15.2427662037039NA
212873NANA-778.173321759259NA
314000NANA620.347511574073NA
413477NANA-147.992766203704NA
514237NANA396.555844907408NA
613674NANA-279.437210648149NA
71352913644.462094907413937.9583333333-293.496238425926-115.462094907407
81405813908.201678240714033.5-125.298321759259149.798321759261
91297513580.153067129614124.9583333333-544.805266203704-605.153067129628
101432613943.236400463014227.625-284.388599537037382.76359953704
111400814026.694733796314335.2916666667-308.59693287037-18.6947337962938
121619316176.778067129614416.251760.5280671296316.2219328703704
131448314490.048900463014505.2916666667-15.2427662037039-7.0489004629635
141401113826.576678240714604.75-778.173321759259184.423321759259
151505715328.555844907414708.2083333333620.347511574073-271.555844907405
161488414662.215567129614810.2083333333-147.992766203704221.784432870372
171541415286.389178240714889.8333333333396.555844907408127.610821759263
181444014690.812789351914970.25-279.437210648149-250.812789351850
191490014761.962094907415055.4583333333-293.496238425926138.037905092591
201507415005.118344907415130.4166666667-125.29832175925968.881655092593
211444214649.569733796315194.375-544.805266203704-207.569733796296
221530714977.486400463015261.875-284.388599537037329.513599537038
231493815018.903067129615327.5-308.59693287037-80.9030671296296
241719317162.278067129615401.751760.5280671296330.7219328703723
251552815475.340567129615490.5833333333-15.242766203703952.6594328703704
261476514795.535011574115573.7083333333-778.173321759259-30.5350115740748
271583816284.014178240715663.6666666667620.347511574073-446.014178240739
281572315617.048900463015765.0416666667-147.992766203704105.951099537040
291615016242.055844907415845.5396.555844907408-92.0558449074051
301548615668.229456018515947.6666666667-279.437210648149-182.229456018516
311598615758.003761574116051.5-293.496238425926227.996238425927
321598315995.326678240716120.625-125.298321759259-12.3266782407409
331569215667.319733796316212.125-544.80526620370424.680266203708
341649016016.736400463016301.125-284.388599537037473.263599537038
351568616041.653067129616350.25-308.59693287037-355.653067129626
361889718163.153067129616402.6251760.52806712963733.846932870372
371631616437.090567129616452.3333333333-15.2427662037039-121.090567129624
381563615702.201678240716480.375-778.173321759259-66.2016782407372
391716317126.555844907416506.2083333333620.34751157407336.4441550925949
401653416363.340567129616511.3333333333-147.992766203704170.659432870372
411651816932.014178240716535.4583333333396.555844907408-414.014178240739
421637516291.896122685216571.3333333333-279.43721064814983.1038773148139
431629016302.212094907416595.7083333333-293.496238425926-12.2120949074051
441635216516.868344907416642.1666666667-125.298321759259-164.868344907405
451594316156.986400463016701.7916666667-544.805266203704-213.98640046296
461636216466.319733796316750.7083333333-284.388599537037-104.319733796296
471639316499.986400463016808.5833333333-308.59693287037-106.986400462967
481905118637.278067129616876.751760.52806712963413.721932870369
491674716914.132233796316929.375-15.2427662037039-167.132233796296
501632016222.451678240717000.625-778.17332175925997.548321759259
511791017711.639178240717091.2916666667620.347511574073198.360821759259
521696117026.632233796317174.625-147.992766203704-65.6322337962956
531748017653.264178240717256.7083333333396.555844907408-173.264178240741
541704917066.562789351917346-279.437210648149-17.5627893518504
551687917145.920428240717439.4166666667-293.496238425926-266.920428240741
561747317398.701678240717524-125.29832175925974.2983217592591
571699817064.944733796317609.75-544.805266203704-66.9447337962956
581730717408.486400463017692.875-284.388599537037-101.486400462964
591741817486.903067129617795.5-308.59693287037-68.9030671296314
602016919684.903067129617924.3751760.52806712963484.096932870369
611787118019.715567129618034.9583333333-15.2427662037039-148.715567129628
621722617364.160011574118142.3333333333-778.173321759259-138.160011574073
631906218867.889178240718247.5416666667620.347511574073194.110821759263
641780418211.757233796318359.75-147.992766203704-407.757233796296
651910018882.805844907418486.25396.555844907408217.194155092591
661852218313.729456018518593.1666666667-279.437210648149208.270543981485
671806018415.087094907418708.5833333333-293.496238425926-355.087094907401
681886918704.118344907418829.4166666667-125.298321759259164.881655092595
691812718385.278067129618930.0833333333-544.805266203704-258.278067129631
701887118755.194733796319039.5833333333-284.388599537037115.805266203704
711889018837.319733796319145.9166666667-308.5969328703752.6802662037044
722126320990.361400463019229.83333333331760.52806712963272.638599537036
731954719308.465567129619323.7083333333-15.2427662037039238.534432870372
741845018649.868344907419428.0416666667-778.173321759259-199.868344907401
752025420118.139178240719497.7916666667620.347511574073135.860821759259
761924019417.798900463019565.7916666667-147.992766203704-177.798900462964
772021620034.264178240719637.7083333333396.555844907408181.735821759259
781942019413.562789351919693-279.4372106481496.43721064814963
791941519299.607928240719593.1041666667-293.496238425926115.392071759263
8020018NANA-125.298321759259NA
8118652NANA-544.805266203704NA
8219978NANA-284.388599537037NA
8319509NANA-308.59693287037NA
8421971NANA1760.52806712963NA
8516441.5NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Oct/03/t1286124219243l1ww6628atj3/1gfs21286124246.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Oct/03/t1286124219243l1ww6628atj3/1gfs21286124246.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Oct/03/t1286124219243l1ww6628atj3/2gfs21286124246.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Oct/03/t1286124219243l1ww6628atj3/2gfs21286124246.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Oct/03/t1286124219243l1ww6628atj3/39psn1286124246.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Oct/03/t1286124219243l1ww6628atj3/39psn1286124246.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Oct/03/t1286124219243l1ww6628atj3/49psn1286124246.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Oct/03/t1286124219243l1ww6628atj3/49psn1286124246.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 12 ;
 
Parameters (R input):
par1 = additive ; par2 = 12 ;
 
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$trend[i]+m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
 





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