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Data:
96.1 96.5 96.9 97.8 98.9 100.2 101.2 101 101.6 102.4 103.7 103.7 104.6 104.5 104.5 105.6 106.1 107.6 107.7 108.3 108.1 108.1 108 108.2 108.9 109.8 109.9 109.8 110.9 111.1 112.2 112.7 114.6 114.2 114.7 114.7 116 116.3 116.4 116.6 118.1 117.2 108.3 109.5 110.5 110.6 111.2 111.1 111 112.4 112.5 112.4 111.8 111.6 112.9 112.8 113.7 113.8 114 113.8 113.9 114.4 114.4 114.5 113.8 114.3 115 115.4 115.3 114.9 114.3 114.5 115.5 115.8 115.8 116 114.9 114.1 114.1 113.5 115 114.7 115.4 116.1 116.6 117.2 118.2 118 117.7 118.5 117.5 118 117.7 116.3 115 115.7 113.6 114.8 114.9 117.3 117.3 117.7 120 119.6 119.2 117.3 117.5 119 112.5 118.9 118.4 119.4 120.6 118.6 122 122.6 120.6 117.4 116.4 122.2 121 122.4 124.9 126.1 124.5 123.2 126.4 123.9 116 126.6 125.9 126.6 116.7 126.4 129 128.7 128.4 129.2 133.3 128.9 132.7 127.7 131.8 133.9
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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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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