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Data:
21 22 22 18 23 12 20 22 21 19 22 15 20 19 18 15 20 21 21 15 16 23 21 18 25 9 30 20 23 16 16 19 25 18 23 21 10 14 22 26 23 23 24 24 18 23 15 19 16 25 23 17 19 21 18 27 21 13 8 29 28 23 21 19 19 20 18 19 17 19 25 19 22 23 14 16 24 20 12 24 22 12 22 20 10 23 17 22 24 18 21 20 20 22 19 20 26 23 24 21 21 19 8 17 20 11 8 15 18 18 19 19 23 22 21 25 30 17 27 23 23 18 18 23 19 15 20 16 24 25 25 19 19 16 19 19 23 21 22 19 20 20 3 23 23 20 15 16 7 24 17 24 24 19 25 20 28 23 27 18 28 21 19 23 27 22 28 25 21 22 28 20 29 25 25 20 20 16 20 20 23 18 25 18 19 25 25 25 24 19 26 10 17 13 17 30 25 4 16 21 23 22 17 20 20 22 16 23 0 18 25 23 12 18 24 11 18 23 24 29 18 15 29 16 19 22 16 23 23 19 4 20 24 20 4 24 22 16 3 15 24 17 20 27 26 23 17 20 22 19 24 19 23 15 27 26 22 22 18 15 22 27 10 20 17 23 19 13 27 23 16 25 2 26 20 23 22 24
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6)) a<-table.element(a,signif(m$trend[i],6)) a<-table.element(a,signif(m$seasonal[i],6)) a<-table.element(a,signif(m$random[i],6)) 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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