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Data:
4526.1 4616.8 4558 4736.8 4771.1 4611.3 4687.1 4718.3 4731.6 4755.4 4849.8 4697.8 4720.2 4741.1 4794.2 4807.4 4836.9 4853 4902.9 4938 4910.4 4954.6 4937.3 5003.8 5005.6 4984.4 5050 5017.7 4984.8 5036.3 5093.6 5111.2 5090.7 5063.7 5007.5 5122.5 5172.3 5232.8 5183.3 5204.6 5255.4 5294.5 5308.9 5281.3 5413.9 5462.4 5568.7 5579.1 5590.3 5703.2 5717.7 5772.3 5876.6 6134.6 6155.6 6259.5 6180.7 6120.3 6097 6167.5 6207.1 6181.7 6196.2 6183.9 6184 6271.1 6204.9 6284.5 6293.9 6377.9 6400.2 6456.2 6372.8 6368.8 6497.8 6599.4 6696.9 6676.3 6731.7 6732.3 6760.2 6841.4 6917.5 6899.3 6972.9 6969.2 6941.6 6905.5 6971.3 6968.4 7012.2 7049.5 7095.6 7237.5 7230.5 7253.5 7289.4 7364.6 7428.1 7390.2 7279.9 7426.5 7480.1
Type of Seasonality
12
additive
multiplicative
Seasonal Period
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,signif(m$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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Big Analytics Cloud Computing Center
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