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Data:
NA 6 NA 1 1 5.5 NA 6.5 4.5 2 5 0.5 5 NA NA NA 5.5 NA 3 NA 0.5 6.5 NA 7.5 5.5 4 7.5 NA 4 NA NA NA 3.5 2.5 4.5 4.5 NA 6 2.5 NA 0 5 6.5 5 6 NA 5.5 1 NA 6 5 1 5 6.5 7 4.5 NA 8.5 NA 7.5 3.5 NA NA 9 NA 3.5 NA 6.5 7.5 NA NA NA NA 7.5 NA NA 6.5 NA NA 1.5 NA NA NA 0 NA 5.5 5 NA NA NA 7 0 4.5 NA 1.5 NA 2.5 5.5 8 1 5 NA 3 3 8 NA NA NA NA NA NA 5.5 0.5 7.5 9 9.5 NA 7 8 NA 7 NA NA 9.5 4 6 8 5.5 9.5 7.5 7 NA 8 7 7 6 10 2.5 NA 8 6 8.5 6 9 NA NA 5.5 NA NA 9 NA 8.5 9 NA 9 7.5 10 NA NA NA NA 8.5 NA 10 NA 6.5 NA 8.5 NA NA 8 NA 7 7.5 7.5 9.5 6 NA 7 NA NA NA 10 NA 3.5 NA NA NA NA 6.5 6.5 8.5 4 NA NA 8.5 NA NA NA NA 10 8 NA NA 5 NA 4.5 8.5 NA 8.5 7.5 7.5 NA NA NA 5.5 8.5 9.5 7 NA NA NA 6.5 6.5 NA NA NA 10 10 NA NA NA 7.5 4.5 4.5 0.5 NA 4.5 5.5 5 NA NA 8 NA 6.5 8 NA 5.5 NA 5 3.5 NA 9 NA 5 NA 3 NA NA 0.5 6.5 NA 4.5 8 NA 7.5 NA NA 9.5 6.5 NA 6 NA NA 8 NA NA
Type of Seasonality
additive
additive
multiplicative
Seasonal Period
1
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
x<-na.omit(x) par2 <- '2' par1 <- 'additive' 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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R Server
Big Analytics Cloud Computing Center
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