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Data:
9.26 9.27 9.29 9.27 9.29 9.31 9.33 9.35 9.34 9.35 9.38 9.43 9.47 9.5 9.55 9.58 9.61 9.57 9.61 9.65 9.62 9.63 9.62 9.63 9.65 9.72 9.75 9.77 9.78 9.82 9.84 9.9 9.94 9.96 10.03 10.03 10.12 10.12 10.05 10.14 10.17 10.2 10.2 10.35 10.43 10.52 10.57 10.57 10.57 10.65 10.57 10.61 10.63 10.71 10.72 10.77 10.79 10.82 10.9 10.83 10.92 10.91 10.88 10.87 11 10.99 11.03 11.04 10.99 10.9 11 10.99 10.92 10.98 11.15 11.19 11.33 11.38 11.4 11.45 11.56 11.61 11.82 11.77 11.85 11.82 11.92 11.86 11.87 11.94 11.86 11.92 11.83 11.91 11.93 11.99 11.96 12.12 11.85 12.01 12.1 12.21 12.31 12.31 12.39 12.35 12.41 12.51 12.27 12.51 12.44 12.47 12.51 12.58 12.5 12.52 12.59 12.51 12.67 12.64 12.54 12.6 12.67 12.62 12.72 12.85 12.85 12.82 12.79 12.94 12.71 12.56
Type of Seasonality
multiplicative
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$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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Big Analytics Cloud Computing Center
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