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Data:
52 54.9 60.5 54.8 60.1 60.3 49.8 53.8 64.8 62 65.2 60.1 61.2 63.6 68.6 63.1 66.5 71.9 58.1 61.5 66.2 72.3 67 62.9 66.4 65.6 70.9 68.4 66.4 67.6 64.1 62.1 70 74.4 67 64.8 70.7 64 72.5 70.4 63.6 69.8 67.7 66.4 78.9 79.9 69.1 81.2 66 71.8 86.1 76.1 70.5 83.3 74.8 73.4 86.5 82 80.8 91.5 77 72.3 83.5 79 76.7 83.1 71.1 75.5 90.9 85.4 84.8 83.8 79.3 79.9 93 78.1 82.3 87.3 74.6 80 91.3 94.2 90.9 88 81.6 77.4 91 79.9 83.4 91.6 85.2 84.1 87 92.8 89.2 87.3 89.5 86.8 92 92.2 86.4 92.9 91.2 80.3 102 99 89.2 103 80.4 83.4 97.6 87 84.4 94.1 88.9 82.3 94.7 94.5 91.6 96.8 87.9 99.9 109.5 91.2 89.4 109.7 96.9 94.1 104.4 100.8 107.4 108.9 95.2 102.7 130.9 104 106.5 106.1 97.8 112.2 114.5 105.8 101 101.2 96.5 99.5 123.8 94.6 95.8 105.4 104.4 105.2 112.7 114.8 108.9 103.8 102.5 98.1 118.2 114.8 109.9 116.7 116.9 104.4 113.5 123.8 116.4 114.1 102.8 112.7 121.1 120.8 117.8 130.4 110.9 105.4 137.6 133.3 123.3 122.8 110.2 101.4 128.7 120.6 110.1 121.6 113 115.9 131.1 127.4 123.9 120.8 108.5 112.9 129.6 121.3 119.1 140.8 127.4 128.1 136.6 126.5 120.8 144.3 116 123.4 138.6 118.3 124.2 136 127.4 131.6
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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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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