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Data:
81.83 82.58 82.6 82.71 82.98 83.11 83.22 83.32 83.39 83.45 83.52 83.59 83.97 84.48 84.8 84.93 85.14 85.22 85.54 85.5 85.61 85.75 85.89 85.94 86.08 86.3 86.97 87.3 87.62 87.59 87.78 87.87 88.17 88.67 88.84 88.9 88.98 89.27 89.69 89.72 89.79 89.82 89.98 90.09 90.31 90.3 90.48 90.52 90.53 91.38 91.87 91.9 92.08 92.14 92.09 92.32 92.67 92.78 92.96 93.12 93.32 94.12 94.34 94.52 94.81 94.95 94.99 95.03 95.16 95.41 95.46 95.62 95.66 95.96 96.18 96.24 97.03 97.11 97.28 97.74 97.83 98.14 98.18 98.21 98.43 98.67 99.51 99.64 99.83 99.84 99.94 100.17 100.56 101.05 101.17 101.21 101.01 101.92 102.33 102.41 102.5 102.69 102.98 103.11 103.36 103.8 104.07 104.15 104.19 104.64 104.98 105.25 105.43 105.59 105.84 105.87 106 106.14 106.24 106.31
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 <- '12' par1 <- 'multiplicative' 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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R Server
Big Analytics Cloud Computing Center
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