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Data:
97 100.7 101.4 101.5 101.8 101.5 102.2 101.8 98.5 98.4 97.5 97.7 98.3 99.6 99.4 96.7 96.9 96.1 97.9 99.2 97.8 94.9 93.3 91.5 89.1 92.3 91.8 92.1 94.4 92.8 92.6 92.3 92.1 89.8 87.4 87.7 86.3 89.1 90.4 87.1 86.7 84.4 88.4 88.9 88.5 87.2 86.2 83.4 87.5 85.7 87.4 86.8 87.9 85.9 87.7 87 86.8 86.2 86.1 87.5 85.7 88.9 89.8 91.4 95.2 94.1 96.8 96.1 96.6 94.2 93.9 96.5 93.4 95 95.2 94 97 96.9 96.3 96.3 97.3 95.7 96.4 95.1 94.6 95.9 96.2 94.3 98.3 95.9 92.1 94.6 94.7 96.7 97.5 96.2 97.1 95.9 94.5 99.4 101.3 101.4 100.9 101.4 103.1 102.4 101.1 102 103.9 101.7 101.2 101.9 101.1 103.1 103.3 101.4 102.8 103 102.6 102.2
Type of Seasonality
multiplicative
additive
multiplicative
Seasonal Period
12
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')
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0 seconds
R Server
Big Analytics Cloud Computing Center
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