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Data:
20.98 20.1 20.61 20.27 20.08 23.58 22.31 22.89 21.78 22.19 22.58 22.78 25.06 25.16 25.47 25.34 24.2 25.32 25.57 25.76 24.79 23.14 22.66 22.06 24.26 23.15 22.92 21.43 21.56 23.48 24.35 24.83 24.19 23.58 23.58 24.35 27.18 25.69 24.81 23.26 23.49 26.86 27.12 27.66 26.26 25.51 24.63 23.57 27.63 25.85 26.09 24.47 24.19 25.09 25.26 25.58 24.76 25.02 24.24 24.14 28.69 26.74 26.48 24.45 23.88 26.58 26.23 28.63 26.81 26.56 26.64 26.8 28.37 27.13 28.44 28.62 27.28 31.32 31.26 31.41 31.76 32.72 32.15 33.62 35.97 33.78 33.77 32.75 32.55 33.22 32.88 31.56 30.27 28.65 27.89 27.07 30.8 28.38 27.5 28 28.02 29.2 27.59 27.22 27.16 26.31 25.67 26.41 28.34 25.43 23.72 23.33 23.8 27.7 26.28 27.51 27.93 28.76 28.65 29.52 31.23 27.9 27.87 27.52 27.59 31.2 30.22 30.62 31.52 30.59 31.42 31.95
Type of Seasonality
additive
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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Big Analytics Cloud Computing Center
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