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Data:
46.8 52.8 58.3 54.5 64.7 58.3 57.5 56.7 56 66.2 58.2 53.9 53.1 54.4 59.2 57.8 61.5 60.1 60.1 58.4 56.8 63.8 53.9 63.1 55.7 54.9 64.6 60.2 63.9 69.9 58.5 52 66.7 72 68.4 70.8 56.5 62.6 66.5 69.2 63.7 73.6 64.1 53.8 72.2 80.2 69.1 72 66.3 72.5 88.9 88.6 73.7 86 70 71.6 90.5 85.7 84.8 81.1 70.8 65.7 86.2 76.1 79.8 85.2 75.8 69.4 85 75 77.7 68.5 68.4 65 73.2 67.9 76.5 85.5 71.7 57.9 75.5 78.2 75.7 67.1 74.6 66.2 74.9 69.5 76.1 82.3 82.1 60.5 71.2 81.4 74.5 61.4 83.8 85.4 91.6 91.9 86.3 96.8 81 70.8 98.8 94.5 84.5 92.8 81.2 75.7 86.7 87.5 87.8 103.1 96.4 77.1 106.5 95.7 95.3 86.6 89.6 81.9 98.4 92.9 83.9 121.8 103.9 87.5 118.9 109 112.2 100.1 111.3 102.7 122.6 124.8 120.3 118.3 108.7 100.7 124 103.1 115 112.7 101.7 111.5 114.4 112.5 107.2 136.7 107.8 94.6 110.7 126.6 127.9 109.2 87.1 90.8 94.5 103.3 103.2 105.4 103.9 79.8 105.6 113 87.7 110 90.3 108.9 105.1 113 100.4 110.1 114.7 88.6 117.2 127.7 107.8 102.8 100.2 108.4 114.2 94.4 92.2 115.3 102 86.3 112 112.5 109.5 105.9 115.3 126.2 112.2 112.5 106.9 90.6 75.6 78.8 101.8 93.9 100 89.2 97.7 121.1 108.8 92.9 113.6 112.6 98.8 78
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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