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Data:
12.11 11.42 11.71 12.04 12.21 12 12.36 12.32 12.96 12.79 13.19 12.34 13.25 12.54 12.77 12.96 13 13.61 13.8 14.16 14.27 14.69 15.01 15.09 15.14 14.2 13.83 14.31 14.04 14.9 14.92 15.36 15.5 15.65 16.18 15.44 15.58 15.24 15.33 16.07 15.82 15.87 15.72 17.07 16.83 17.52 17.76 17.36 17.95 16.71 17.14 16.72 17.26 17.24 17.69 18.13 18.08 18.18 18.18 17.64 17.89 16.82 16.61 16.66 17.02 16.91 17.18 18.06 17.58 17.48 17.54 17.44 17.79 16.79 16.19 16.62 16.39 16.54 17.26 18 17.29 18.16 17.82 17.48 18.31 17.04 17.03 16.97 17.11 17.12 17.69 18.5 18.27 18.45 18.35 18.03 18.49 18.07 17.8 17.88 18.12 18.68 18.8 19.64 19.56 19.3 20.07 19.82 20.29 19.36 18.74 18.87 18.87 18.91 19.31 20.06 20.72 20.42 20.58 20.58 21.18 19.87 19.83 19.48 19.49 19.4 19.89 20.44 20.07 19.75 19.54 19.07
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$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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