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Data:
76.7 82.7 95 81.8 91.8 88.2 72.2 75.9 91.2 94.2 90.4 73.1 84.3 82.8 92.8 83.5 90.4 91.8 75.5 75.1 89.1 95.4 85.2 68.4 81 78.8 85.7 88.1 82.7 90.1 79.1 71.5 91 99 84.2 68.1 81.3 84.1 88.1 87.8 83 91.5 81 67.1 90.8 94.7 79.3 75.5 80.2 81.1 96.9 88.4 81.6 98.9 78.5 74.5 97.3 93 86.4 80.6 83.7 86.7 95.8 94.8 88.8 103.3 77.8 81.2 102.8 97.2 94.7 85.2 92.7 91.1 103.5 92.7 102 105.9 84 86.4 105 108.6 103.5 85.2 101.8 99.1 112.8 100.7 107.2 113.8 94.4 90.9 105.5 118.2 108.5 83.9 108.6 109.5 106.5 116.5 104.6 112.3 101.2 86.9 112.8 110.4 91.2 80 86.9 85.1 95.6 92.6 87.7 98.7 85.6 77.4 102 101.9 91.9 81.6 91.6 92.9 109.3 103.8 96.8 117.4 93 89 110 106 100.4 90 97.4 101.6 117 97.7 110.8 103.1 86.1 92.9 112.9 102.4 103.3 89.8 96.5 102.2 112 100 103 112.4 94.5 92.6 104 108.4 100.1 79.1 100.9 97.4 101.7 104.9 103.1 108.2 99.9 85.6 106.8 113 99.3 84.2 104.3 99.1 107.3 111.7 99.9 107.5 101.3 86.1 113 114 96.4 85.5 99.5 102.4 117.9 110.2 99.2 121.8 103.1 89 112.5 114 103.7 89.5 102.1 107.3 118.3 112.3 107.9 121 95.8 96.1 114.9 109.8 106.7 89.5 104 106 124.6 106.8 115.4 120.5 95.5 97.8
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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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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