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Data:
73.97 75.01 75.98 78.85 79.34 79.62 79.76 79.62 79.89 79.88 79.97 79.63 80.04 80.23 80.44 81.78 82.51 82.43 82.35 82.53 82.08 82.73 82.46 81.98 82.11 82.26 82.51 82.89 83.83 84.73 84.48 84.84 84.99 84.7 84.54 84.73 84.51 84.54 84.27 84.47 84.25 84.33 84.29 84.53 84.01 84.18 84.08 83.44 83.61 83.89 83.4 82.96 82.76 83.35 87.78 88.99 88.92 88.91 89.79 90.54 93.15 92.79 93.21 95.35 100.91 103.69 104.04 104.16 104.71 105.18 104.92 104.83 104.9 105.05 104.6 103.21 102.52 101.09 101.19 102.34 102.62 102.47 101.82 101.86 101.54 101.98 101.23 100.4 99.94 99.94 100 98.8 99.07 99.46 99.18 98.47 97.12 96.91 96.09 97.17 96.8 97.13 99.9 100.56 100.84 99.81 100.44 100.07
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,signif(m$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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