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Data:
7235.6 7268.3 7271.3 7327.4 7339.5 7303.2 7300.7 7311.8 7329 7330.8 7328.6 7346.5 7356.9 7385.7 7394.9 7422.8 7446.6 7441.2 7476.1 7461.6 7450.2 7483.8 7479.7 7509.3 7518.6 7495.4 7507.5 7533.8 7544.7 7564.7 7573.6 7604.6 7605.6 7619.9 7661 7664.1 7663.9 7652.1 7632.8 7677.7 7677.3 7727 7746.4 7771.2 7781.2 7819.4 7819.1 7849.1 7757.8 7823 7825.6 7827 7884.7 7912 7897 7881.1 7885.8 7891.3 7920.9 7946.3 7952.3 8001.9 8007.9 8028.1 8012.5 8069.6 8082.7 8110.6 8129 8149.4 8139.7 8162.4 8207.7 8215.5 8244.6 8269 8245.6 8244.6 8287.6 8284.3 8290.6 8325 8344.2 8353.6 8367.8 8334.6 8330.2 8368.2 8384.7 8351.4 8411.4 8442.8 8443.1 8462.6 8508.5 8522.7 8559.6 8556.7 8618.9 8613.2 8634 8653.4
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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