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Data:
11.73 11.75 11.39 11.54 9.62 9.82 9.94 9.9 9.8 9.86 10.5 10.33 10.16 9.91 9.96 10.03 9.55 9.51 9.8 10.08 10.2 10.23 10.2 10.07 10.01 10.05 9.92 10.03 10.18 10.1 10.16 10.15 10.13 10.09 10.18 10.06 9.65 9.74 9.53 9.5 9 9.15 9.32 9.62 9.59 9.37 9.35 9.32 9.49 9.52 9.59 9.35 9.2 9.57 9.78 9.79 9.57 9.53 9.65 9.36 9.4 9.32 9.31 9.19 9.39 9.28 9.28 9.31 9.28 9.31 9.35 9.19 9.07 8.96 8.69 8.58 8.56 8.47 8.46 8.75 8.95 9.33 9.51 9.561 9.94 9.9 9.275 9.56 9.779 9.746 9.991 9.98 10.195 10.31 10.25 9.871 10.06 9.894 9.59 9.64 9.89 9.53 9.388 9.16 9.418 9.57 9.857 9.877 9.76 9.76 9.695 9.475 9.262 9.097 8.55 8.16 7.532 7.325 6.749 7.13 6.995 7.346 7.73 7.837 7.514 7.58 6.83 6.617 6.715 6.63 6.891 7.002 7.09 7.36 7.477 7.826 7.79 7.578 7.204 7.198 7.685 7.795 7.46 7.274 7.33 7.655 7.767 7.84 7.424 7.54 7.351 6.735 6.777 6.679 7.34 6.978 6.92 6.628 6.385 5.984 6.268 6.596 6.395 6.715 6.804 6.929 6.846 6.992 6.774 6.75 6.485 6.27 6.47 6.78 6.71 6.141 6.72 6.68 6.371 6.097 6.27 6.447 6.37 6.446 6.54 6.374 6.33 6.63 6.498 6.485 6.36
Type of Seasonality
multiplicative
additive
multiplicative
Seasonal Period
5
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$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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R Server
Big Analytics Cloud Computing Center
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