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Data:
6506.9 6481 6558.5 6583.7 6702.5 6825.2 6797 6926.5 7005 6953.1 7024.1 7069.1 7084.9 7068.5 7224.7 7246 7165.2 7148.9 7199 7145.6 7081.8 7105.1 7112.3 7204.4 7086.1 7153 7227.6 7180.3 7216.9 7097.3 7092.5 7086.5 6978.8 7066.5 7202.2 7173.9 7218.3 6978.6 6721.2 6821 6813.9 6726.3 6644.3 6740.8 6800.8 6714.2 6794.2 6891.1 6926.2 7119.5 7124 7201.4 7223.5 7296.9 7399.7 7295.7 7463.9 7407.6 7473.2 7507.1 7513.3 7567.7 7702.1 7719.5 7739.3 7915 7903.4 7937.7 7904.2 7878.9 7952.1 8127.2 8229.9 8204.3 8238.6 8374.7 8340 8308.8 8155.6 8198.4 8116.7 8186.8 8173.4 8330.1 8456.3 8495.5 8597.8 8550.4 8548.3 8561.9 8575.7 9055 9089.6 9350.8 9236.5 8919.4 8878.2 8777.4 8584.1 8735.8 8834.9 8678.1 8706.3 8769.5 8730.1 8867.9 8865.3 8679.5 8656.3 8759.5 8846.3 8947.4 8975.9 9111.1 9146.2 8880.1
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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R Server
Big Analytics Cloud Computing Center
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