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Data:
3606.1 3102.8 3602.5 3247.3 3467.7 3330.2 3367.1 3579.2 3303.8 3513.1 3892.7 4698.2 3876.6 3937.9 4011.5 3881.2 4054.6 3609.9 3788 3603.2 4110.8 4398.5 4402 4249.8 4054.5 3868.7 4165.4 4043.8 4220.2 4078 4129.3 4129.3 4161.5 4193.3 3959.8 3962.8 4079.3 3824.5 4160 3906.2 3907.8 4076.7 4099.4 4213.7 4012.2 4088.4 3911.9 3992.5 4333 4159 4540.8 4515.4 4661.1 4394.3 4916.4 4999.7 4783.4 4889.5 4840.6 4979.2 5442.4 5229.9 5670.3 5129.1 5358 5363.5 5388.7 5409.2 5431.2 5591.9 5622.5 5528.7 4968.7 4812.5 5175.1 4943.2 5007.1 5028.5 5023 5158.3 5248.8 5494 5193.3 4318.2 5726.3 5378.7 5776.1 5626.3 5755.2 5540.9 5560.8 5742.6 5592.9 5782.6 5611.5 5653.5 5438.7 5084.7 5736.2 5497.2 5650.9 5645.8 5634 5747.2 5585.2 5952.5 5833.5 5778.4 6096.9 5797.6 6187.9 5849.6 6096.6 5757.8 6248.1 6110.5 5919.8 6082.2 5886.9 6167.4 6458.9 6282.3 6762.1 6698.1 6017.3 5790.5
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 <- '12' par1 <- 'additive' 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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R Server
Big Analytics Cloud Computing Center
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