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Data:
178.6 224.7 206.7 149.7 160.1 154.7 155 233.6 211.9 186.7 156.5 142 207 232.3 230.4 159.4 158.4 164.2 179.2 242.9 211.7 188.6 151.9 134.8 218 233.4 218.5 163.7 150.8 145.6 190.3 235.9 203.7 185.3 150.9 136 213.9 234.1 194.8 154.2 138.5 133.7 186.8 221.3 211.7 171.4 124.5 129.2 173.3 190.9 175 113.8 98.4 116.4 153.9 199.7 168.8 132.8 118.8 112.7 150.5 203.5 184.3 113.5 102.4 119.3 152.4 218.5 154.6 124.9 124 113.8 162.5 184.8 177.3 91.4 85.2 120.9 159.8 200.1 171.8 139.5 115.7 96.8 169.9 212.3 182.3 95.2 96.9 100.3 131.3 172.3 130.6 129.5 96.3 91.4 140.7 160.2 158.8 193.6 80.8 102 119.5 129.6 113.8 102.5 78.4 95.7 143.7 149.3 121.7 81 68.1 92.3 107.7 114.4 98.6 106.7 73.9 85.9 118.4 144.2 118.4 82.6 68 99.8 93.4 107.9 101.1 100.4 76.7 89.1 105.3 124.8 111.9 89 88.6 84.5 91.1 118.1 103.6 92.6 70.2 70.2 114.3 125.3 98.9 65.4 66 71.2 84.6 102.6 91.8 97.4 64.1 62.3 96.2 104.9 90.3 65.2 57.8 70.5 93.2 74.2 91.1 85 58.9 68.3 98.1 110.5 77.6 55.1 49.8 58.5 86.5 88.8 94 65 52.2 70.9 88.4 107.8 75.2 58 58.3 71.6 72.4 119.8 83.4 60.6 47.1 65.5 76.1 115.2 73.5 50.7 53.5 66.7 84.5 96.4 63.6 40.4 56.3 58.4 103 104.5 84.9 50.8 57.9 56.9 82.8 96
Type of Seasonality
FALSEFALSEFALSEFALSEFALSEFALSEFALSEadditiveFALSEFALSEFALSEadditivemultiplicativeadditivemultiplicativemultiplicativeadditivemultiplicative12additiveFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE12additive12additivemultiplicativeadditive12additiveFALSEFALSEFALSEFALSEadditiveFALSEadditive1212121212additive
additive
multiplicative
Seasonal Period
0.20.20.20.20.20.20.2120.2121212121212121212120.20.10.20.20.20.20.20.20.20.212periodic12121212120.20.21212120.21212SingleDoubleTripleTriple12
12
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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
1 seconds
R Server
Big Analytics Cloud Computing Center
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