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Data:
84.97 85.57 85.74 85.88 85.88 85.96 85.96 85.99 86.02 86.14 86.3 86.32 86.32 86.77 87.47 87.39 87.3 87.31 87.31 87.38 87.4 87.32 87.37 87.4 87.4 87.89 87.7 87.89 88.02 88.08 88.08 88.15 88.21 88.41 88.39 88.41 88.41 89.1 90.35 90.61 91.18 91.22 91.22 91.4 91.52 91.68 91.71 91.77 91.77 92.16 93.64 93.78 93.96 93.82 93.82 93.89 94.05 94.46 94.62 94.72 94.72 95.76 96.14 97.11 97.19 97.43 97.43 97.56 97.66 97.75 97.82 97.82 97.82 98.35 98.19 98.19 98.21 98.22 98.26 98.23 98.26 98.5 98.51 98.51 98.51 98.89 99.55 99.9 100.12 100.09 100.09 100.09 100.46 100.71 100.79 100.79 100.93 101.15 101.53 101.91 102.18 102.24 102.2 102.32 102.43 102.45 102.84 102.96 102.96 103.1 103.4 103.74 103.97 104.29 104.33 104.46 104.9 105.31 105.63 105.68
Type of Seasonality
additive
additive
multiplicative
Seasonal Period
12
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
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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