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Data:
4393.9 4248 4346.2 4351.7 4424.4 4468.4 4519.1 4518.2 4574.5 4509.6 4337.9 4441.8 4414.1 4465.9 4426 4518.8 4606.3 4647.4 4650.8 4650.2 4720.1 4655 4520.8 4617.3 4488.1 4527.4 4618.3 4642.8 4667.3 4640.6 4716.9 4719.4 4817.3 4764.5 4514.1 4625 4617.7 4361.3 4474.9 4623.8 4692 4672.1 4721.5 4784.6 4858.7 4813.3 4628.2 4710.4 4698.4 4631 4727.4 4719.9 4890.6 4839.9 4867.5 4898.3 4675.7 4981.9 4771.1 4827.8 4685 4646.1 4815 4911.8 4958.4 5019.4 5024.3 5035.8 5082.4 5179.2 4963.2 4951.3 4876.4 4812.1 5004.1 5093.8 5063.1 5078.6 5251.5 5263.2 5280.5 5386.1 5227.3 5149.5 5128.6 5087.7 5188.5 5084 5258.6 5348.9 5280 5374.2 5458.4 5315 5294.5 5341.4 5068 5156.9 5184.7 5280.7 5339 5377.7 5388.6 5443.6 5528.7 5539 5292 5351.5 5163.7 5105 5248.1 5370.9 5484.9 5510.7 5484.9 5567.8 5275.6
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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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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