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classical decomposition - verkocht aantal producten - mattias debbaut

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Tue, 17 Aug 2010 10:54:18 +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/Aug/17/t12820426814sfpt2z9szuw8je.htm/, Retrieved Tue, 17 Aug 2010 12:58:01 +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/Aug/17/t12820426814sfpt2z9szuw8je.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:
mattias debbaut
 
Dataseries X:
» Textbox « » Textfile « » CSV «
376 375 374 372 370 369 370 372 373 373 374 376 371 374 369 363 357 366 362 366 361 362 358 363 360 360 348 345 332 333 323 327 332 337 336 337 343 337 326 321 309 302 293 287 292 292 289 302 310 295 276 264 257 243 227 226 226 229 224 240 244 226 208 199 193 180 167 164 166 173 169 191 193 166 143 147 139 129 115 108 106 116 108 135
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1376NANA1.08320290538612NA
2375NANA1.04665285028439NA
3374NANA0.998455699548646NA
4372NANA0.99742330061833NA
5370NANA0.979660064012128NA
6369NANA0.964147561243234NA
7370353.061174669923372.6250.9474972819051931.04797702649099
8372355.898290034118372.3750.9557523733712471.04524244824087
9373362.002750939189372.1250.9727987932527751.03037891019413
10373370.495648524427371.5416666666670.9971846545459531.00675946258896
11374370.257687593047370.6250.999008937856451.01010731858474
12376391.495671535198369.9583333333331.058215577975530.960419303042524
13371400.243473540171369.51.083202905386120.926935789154757
14374386.127680684083368.9166666666671.046652850284390.968591527386493
15369367.59810671716368.1666666666670.9984556995486461.00381365751679
16363366.262147847889367.2083333333330.997423300618330.991093407093643
17357358.637221767107366.0833333333330.9796600640121280.995434880520657
18366351.793341408625364.8750.9641475612432341.04038353464704
19362344.770573453252363.8750.9474972819051931.04997359946986
20366346.778819471534362.8333333333330.9557523733712471.05542778119424
21361351.545163911721361.3750.9727987932527751.02689508222236
22362358.737179472906359.750.9971846545459531.00909529514584
23358357.603574380198357.9583333333330.999008937856451.00110856168171
24363376.239730286051355.5416666666671.058215577975530.964810387579257
25360381.874157602998352.5416666666671.083202905386120.942718937200932
26360365.587118497252349.2916666666671.046652850284390.984717408752754
27348345.923297572791346.4583333333330.9984556995486461.00600336098141
28345343.321411933668344.2083333333330.997423300618331.00488926122282
29332335.288656908151342.250.9796600640121280.990191565266546
30333328.051207713010340.250.9641475612432341.01508542620980
31323320.688350871495338.4583333333330.9474972819051931.00720839756799
32327321.889434748325336.7916666666670.9557523733712471.01587677227018
33332325.806529173575334.9166666666670.9727987932527751.0190096584072
34337332.0624899638023330.9971846545459531.01486921945546
35336330.713583802895331.0416666666670.999008937856451.01598487771901
36337347.932463575206328.7916666666671.058215577975530.968578776861267
37343353.394947882222326.251.083202905386120.97058546551241
38337338.417754925286323.3333333333331.046652850284390.995810636691922
39326319.5058238555673200.9984556995486461.02032568942270
40321315.642915341509316.4583333333330.997423300618331.01697197813768
41309306.266227511792312.6250.9796600640121281.00892613106714
42302298.122460499418309.2083333333330.9641475612432341.01300653259766
43293290.289479743703306.3750.9474972819051931.00933730102341
44287289.831907224831303.250.9557523733712470.990229139186411
45292291.272172013102299.4166666666670.9727987932527751.00249878998693
46292294.12792373045294.9583333333330.9971846545459530.99276531210141
47289290.128845702477290.4166666666670.999008937856450.996109157296152
48302302.429193722258285.7916666666671.058215577975530.998580845595708
49310303.928681869589280.5833333333331.083202905386121.01997612760028
50295288.134807576207275.2916666666671.046652850284391.02382632102502
51276269.5830388781352700.9984556995486461.02380328209286
52264263.943140926126264.6250.997423300618331.00021542167633
53257254.017690764478259.2916666666670.9796600640121281.01174055722870
54243244.8934805557812540.9641475612432340.992268146332502
55227235.610990767091248.6666666666670.9474972819051930.963452508140406
56226232.28764974477243.0416666666670.9557523733712470.972931622702805
57226230.877580265325237.3333333333330.9727987932527750.97887373793627
58229231.139093051631231.7916666666670.9971846545459530.990745429414864
59224226.192273679664226.4166666666670.999008937856450.990307919700347
60240233.997919679840221.1251.058215577975531.02565014393449
61244233.9718275634022161.083202905386121.04286059796614
62226220.756530339149210.9166666666671.046652850284391.02375227429420
63208205.515464823763205.8333333333330.9984556995486461.01208928573024
64199200.4820834242842010.997423300618330.992607402123073
65193192.380745070382196.3750.9796600640121281.00321890285533
66180185.156504573753192.0416666666670.9641475612432340.97215056211164
67167178.011051837938187.8750.9474972819051930.938143998789678
68164175.141622420281183.250.9557523733712470.936385067887832
69166173.198718482046178.0416666666670.9727987932527750.95843665273544
70173172.679142678874173.1666666666670.9971846545459531.0018581127758
71169168.582758263276168.750.999008937856451.00247499650037
72191173.944185629728164.3751.058215577975531.09805337446909
73193173.402731770561160.0833333333331.083202905386121.11301591404782
74166162.841739290080155.5833333333331.046652850284391.01939466333195
75143150.517196706958150.750.9984556995486460.950057555738341
76147145.499123977699145.8750.997423300618331.01031536122878
77139138.091249856376140.9583333333330.9796600640121281.00658079454396
78129131.204413959183136.0833333333330.9641475612432340.983198629583687
79115NANA0.947497281905193NA
80108NANA0.955752373371247NA
81106NANA0.972798793252775NA
82116NANA0.997184654545953NA
83108NANA0.99900893785645NA
84135NANA1.05821557797553NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Aug/17/t12820426814sfpt2z9szuw8je/120041282042453.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/17/t12820426814sfpt2z9szuw8je/120041282042453.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/17/t12820426814sfpt2z9szuw8je/220041282042453.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/17/t12820426814sfpt2z9szuw8je/220041282042453.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/17/t12820426814sfpt2z9szuw8je/320041282042453.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/17/t12820426814sfpt2z9szuw8je/320041282042453.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/17/t12820426814sfpt2z9szuw8je/4u9h71282042453.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/17/t12820426814sfpt2z9szuw8je/4u9h71282042453.ps (open in new window)


 
Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
 
Parameters (R input):
par1 = multiplicative ; 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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$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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