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Data:
2005/12 2005/11 2005/10 2005/09 2005/08 2005/07 2005/06 2005/05 2005/04 2005/03 2005/02 2005/01 2004/12 2004/11 2004/10 2004/09 2004/08 2004/07 2004/06 2004/05 2004/04 2004/03 2004/02 2004/01 2003/12 2003/11 2003/10 2003/09 2003/08 2003/07 2003/06 2003/05 2003/04 2003/03 2003/02 2003/01 2002/12 2002/11 2002/10 2002/09 2002/08 2002/07 2002/06 2002/05 2002/04 2002/03 2002/02 2002/01 2001/12 2001/11 2001/10 2001/09 2001/08 2001/07 2001/06 2001/05 2001/04 2001/03 2001/02 2001/01 2000/12 2000/11 2000/10 2000/09 2000/08 2000/07 2000/06 2000/05 2000/04 2000/03 2000/02 2000/01 1999/12 1999/11 1999/10 1999/09 1999/08 1999/07 1999/06 1999/05 1999/04 1999/03 1999/02 1999/01 1998/12 1998/11 1998/10 1998/09 1998/08 1998/07 1998/06 1998/05 1998/04 1998/03 1998/02 1998/01
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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