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Data:
4356.5 4340.3 4321.5 4302.8 4285.7 4270.5 4253 4236.9 4231.6 4223.7 4209.2 4192.5 4178.5 4167 4159 4148.1 4142.7 4147.6 4146.1 4141 4142.4 4149 4145.3 4154.7 4157.9 4146.7 4140.1 4125.8 4102.7 4076.7 4060.5 4041.7 4016.2 3997.2 3993 3991.3 3971.8 3947.3 3929.8 3914.7 3902.8 3893.3 3887.1 3881.8 3883.4 3883.2 3876.4 3872.4 3872.6 3868.7 3858.7 3818.2 3810.3 3806.8 3811.4 3818.2 3826.8 3833.6 3833 3839.5 3855.1 3860.4 3855.8 3856.3 3861.6 3858.4 3854.1 3851.8 3851.3 3844.8 3833.3 3826.9 3813.1 3795.5 3779.7 3765.5 3747.7 3735.4 3735.8 3705.8 3674.3 3665.8 3652.5 3649.7 3649.5 3647.3 3646.8 3640.6 3629.5 3618.4 3611.9 3611.4 3607.7 3606.9 3603.9 3596.9 3604.2 3612.2 3623.5 3639 3647.5 3660.6 3679.4 3691.4 3697.4 3707.1 3722.7
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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