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Data:
57.8 65.2 74.5 64.5 73.1 71.3 56.7 62.5 75.3 73.9 74.6 67.1 72.5 73.5 80.7 71.1 75.8 80.5 62 65.6 73.5 77.6 72.1 63.1 68.8 69.4 76.7 75.2 72.9 75.5 65.8 62.8 76.2 80.8 72.2 65.9 72 71.1 77.8 75.4 70.4 77 67.5 61.3 81.2 81.5 72 76.9 69.2 74.7 88.2 81 74.7 90.3 73.1 73.3 90.6 88.3 84.9 87.5 80.8 80 90.8 86.9 81.2 94.3 70.8 74.2 93.2 87.3 90 88.8 84.3 86.6 101.5 85.2 93.5 99.4 76.6 80.1 98.2 101.4 97.2 91.7 92.2 91.3 104.9 93.2 97.8 107.4 90.3 89.4 102 112.9 101.6 94.3 100.9 104.2 106.5 110.6 102.1 112 98.3 87.4 114 110.3 91.9 93 78.7 82.6 93.4 83.8 87.9 93.6 78.7 77.2 95.9 94.5 90.9 87.3 85.1 94.2 108.8 96.2 94.8 113.2 90.6 90.3 110.4 107.3 105.9 103.2 97.3 104.6 128.4 104.7 113.6 110 91.7 100.6 116.3 107 104.4 101.2 99.1 102.6 124.3 98.9 101.7 111.8 97.2 96.5 109.3 111.7 104.3 92.9 97.1 96.5 110.3 108.4 106 112.5 102.9 92.3 110.4 117.4 106.7 99.1 101.9 105.3 113.6 111.5 108 115.8 100.6 91.4 122.3 119.1 106.5 103.8 102 100.6 121.6 111.6 103.5 118.7 100.2 95.8 121.5 117.4 110.6 103.8 100.4 107.9 119.3 112.7 107.2 124.7 100.3 101.7 120.2 112.5 110.8 115.4 104.5 111.3 130.1 108.8 116.6 125.1 103.3 107.1
Type of Seasonality
additive
multiplicative
Seasonal Period
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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