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Data:
8.82000 8.80000 8.82000 8.58000 8.54000 8.42000 8.43000 8.44000 8.09000 7.69000 7.56000 7.54000 7.40000 7.39000 7.37000 7.31000 7.35000 7.26000 7.37000 7.35000 7.33000 7.32000 7.31000 7.33000 7.32000 7.27000 7.48000 7.70000 7.77000 7.80000 7.84000 7.81000 7.78000 7.82000 7.80000 7.81000 7.80000 7.66000 7.41000 7.35000 7.39000 7.32000 7.32000 7.30000 7.29000 7.26000 7.22000 7.21000 7.21000 7.21000 7.20000 7.19000 7.18000 7.12000 7.12000 7.07000 7.08000 7.05000 7.06000 7.07000 7.08000 7.08000 7.09000 7.07000 7.06000 6.99000 6.99000 6.99000 6.98000 6.96000 6.95000 6.91000 6.91000 6.87000 6.91000 6.89000 6.88000 6.90000 6.91000 6.85000 6.86000 6.82000 6.80000 6.83000 6.84000 6.89000 7.14000 7.21000 7.25000 7.31000 7.30000 7.48000 7.49000 7.40000 7.44000 7.42000 7.14000 7.24000 7.33000 7.61000 7.66000 7.69000 7.70000 7.68000 7.71000 7.71000 7.72000 7.68000 7.72000 7.74000 7.76000 7.90000 7.97000 7.96000 7.95000 7.97000 7.93000 7.99000 7.96000 7.92000 7.97000 7.98000 8.00000 8.04000 8.17000 8.29000 8.26000 8.30000 8.32000 8.28000 8.27000 8.32000
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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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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