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Data:
78 100.1 113.2 93.1 115.4 103.3 45.1 104.7 111.3 111.5 100.9 82.1 85.4 97.7 106.6 92.6 109.2 110 52.5 105.3 102.3 118.5 100 74.4 89.2 91.9 107 103.6 101.8 105.1 55.5 92.1 109.8 112.7 98.5 70.3 84.5 91.1 107.6 102.2 96 107.3 59.9 90.2 116.3 115.6 92 76.5 87.9 95.8 116.9 102.9 95.8 117.3 52.8 100.1 116.3 111.8 98.5 86.2 79.9 92.3 100.5 112.5 101.1 121.5 49.6 104.8 120.4 108.3 105.2 85.7 86.8 95.1 117 100.1 112.3 119.6 51.8 105.5 119.9 115.4 112.8 85.1 96.2 103.6 119.9 103.7 109 119.6 57 109.2 112.6 126 109.7 80.1 105.8 114.1 98.3 125.3 111.6 119.7 65 99 124.5 119 98.8 81.8 90.3 102 119.3 104.3 102.8 118.8 60.9 101 122.6 122.2 95 75.6 83.1 89.8 126.1 108.6 98.9 124.3 56.8 102.7 121.7 118.2 101 69 88.6 109.6 128.2 102 122.7 110.5 54 108.1 125 114.1 112.4 87.3 95.4 96.9 125.8 102 112.5 118.9 62.7 110 114.7 124.4 111.9 77 84.1 96.5 106.8 107.9 107.5 114.3 66.6 97.9 117.8 123.8 103.3 84.2 103.6 103.6 112.2 102.7 100.8 109.4 63.5 92.3 119.2 121.5 97.6 78.3 95.6 97.9 114.4 100.9 94.4 117.2 61 95.8 116.2 118.5 94.3 74.4 94.9 102 102.9 109.5 99.7 118.3 56.2 100.3 116.9 108.7 93.9 85.3 85.3 102.4 121.6 91.4 110.2 112.7 55.7 100.1
Type of Seasonality
0
additive
multiplicative
Seasonal Period
no
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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