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Data:
6.4 7.7 9.2 8.6 7.4 8.6 6.2 6 6.6 5.1 4.7 5 3.6 1.9 -0.1 -5.7 -5.6 -6.4 -7.7 -8 -11.9 -15.4 -15.5 -13.4 -10.9 -10.8 -7.3 -6.5 -5.1 -5.3 -6.8 -8.4 -8.4 -9.7 -8.8 -9.6 -11.5 -11 -14.9 -16.2 -14.4 -17.3 -15.7 -12.6 -9.4 -8.1 -5.4 -4.6 -4.9 -4 -3.1 -1.3 0 -0.4 3 0.4 1.2 0.6 -1.3 -3.2 -1.8 -3.6 -4.2 -6.9 -8 -7.5 -8.2 -7.6 -3.7 -1.7 -0.7 0.2 0.6 2.2 3.3 5.3 5.5 6.3 7.7 6.5 5.5 6.9 5.7 6.9 6.1 4.8 3.7 5.8 6.8 8.5 7.2 5 4.7 2.3 2.4 0.1 1.9 1.7 2 -1.9 0.5 -1.3 -3.3 -2.8 -8 -13.9 -21.9 -28.8 -27.6 -31.4 -31.8 -29.4 -27.6 -23.6 -22.8 -18.2 -17.8 -14.2 -8.8 -7.9 -7 -7 -3.6 -2.4 -4.9 -7.7 -6.5 -5.1 -3.4 -2.8 0.8
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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