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Data:
6195800 6172725 6149325 6100900 6579950 6554600 6195800 5957250 5980325 5980325 6006000 6052150 6123975 6123975 6077825 5957250 6579950 6674850 6531525 6195800 6339450 6123975 6221150 6267625 6316050 6195800 6221150 6052150 6579950 6746675 6603350 6339450 6626425 6316050 6603350 6579950 6651775 6387875 6674850 6651775 7082400 6985225 6603350 6410950 6674850 6316050 6579950 6626425 6723600 6508450 6626425 6698250 6962150 6746675 6459700 6149325 6436625 5646875 6029075 6244225 6459700 6149325 6149325 6149325 6316050 6077825 5765175 5503550 5693350 4952350 5406375 5670275 5718700 5454800 5477875 5406375 5646875 5477875 5144750 4903925 5311150 4426825 5001100 5262725 5262725 4952350 4665375 4642300 4903925 4665375 4211675 3899025 4234750 3445325 4162925 4544800 4665375 4401475 4068025 4306575 4401475 4329650 3611725 3278600 3516825 2799225 3540225 3804125 4019275 3660475 3324750 3516825 3611725 3421925 2704325 2391675 2678650 1889225 2750475 3278600
Type of Seasonality
0.01
additive
multiplicative
Seasonal Period
0.99
12
1
2
3
4
5
6
7
8
9
10
11
12
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R Code
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6)) a<-table.element(a,signif(m$trend[i],6)) a<-table.element(a,signif(m$seasonal[i],6)) a<-table.element(a,signif(m$random[i],6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')
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Big Analytics Cloud Computing Center
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