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Data:
97.7 88.9 96.5 89.5 85.4 84.3 83.7 86.2 90.7 95.7 95.6 97 97.2 86.6 88.4 81.4 86.9 84.9 83.7 86.8 88.3 92.5 94.7 94.5 98.7 88.6 95.2 91.3 91.7 89.3 88.7 91.2 88.6 94.6 96 94.3 102 93.4 96.7 93.7 91.6 89.6 92.9 94.1 92 97.5 92.7 100.7 105.9 95.3 99.8 91.3 90.8 87.1 91.4 86.1 87.1 92.6 96.6 105.3 102.4 98.2 98.6 92.6 87.9 84.1 86.7 84.4 86 90.4 92.9 105.8 106 99.1 99.9 88.1 87.8 87.1 85.9 86.5 84.1 92.1 93.3 98.9 103 98.4 100.7 92.3 89 88.9 85.5 90.1 87 97.1 101.5 103 106.1 96.1 94.2 89.1 85.2 86.5 88 88.4 87.9 95.7 94.8 105.2 108.7 96.1 98.3 88.6 90.8 88.1 91.9 98.5 98.6 100.3 98.7 110.7 115.4 105.4 108 94.5 96.5 91 94.1 96.4 93.1 97.5 102.5 105.7 109.1 97.2 100.3 91.3 94.3 89.5 89.3 93.4 91.9 92.9 93.7 100.1 105.5 110.5 89.5 90.4 89.9 84.6 86.2 83.4 82.9 81.8 87.6 94.6 99.6 96.7 99.8 83.8 82.4 86.8 91 85.3 83.6 94 100.3 107.1 100.7 95.5 92.9 79.2 82 79.3 81.5 76 73.1 80.4 82.1 90.5 98.1 89.5 86.5 77 74.7 73.4 72.5 69.3 75.2 83.5 90.5 92.2 110.5 101.8 107.4 95.5 84.5 81.1 86.2 91.5 84.7 92.2 99.2 104.5 113 100.4 101 84.8 86.5 91.7 94.8 95
Type of Seasonality
greygrey13FALSEFALSE11two.sided1additive
additive
multiplicative
Seasonal Period
nono2111220.99212
12
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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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0 seconds
R Server
Big Analytics Cloud Computing Center
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