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Data:
4751.5 4649.2 4664.9 4691.3 4713.7 4772.8 4748.9 4801 4891.9 4891.9 4903.5 4976.4 5009.8 4946.4 4981.9 5013.8 5015.5 5070.7 5000.9 5059.1 5156.8 5002.6 5059.1 5164.1 5087.9 5140.8 5192.8 5177.6 5167.8 5248.4 5097.5 5187.3 5261.5 5179.7 5205.6 5353.3 5425.7 5215.2 5215.6 5216.4 5208.2 5237.5 5175 5300.2 5279.3 5262.6 5220.5 5372.1 5406 5317.2 5258.4 5204.2 5304.2 5300.2 5228.8 5303.3 5296 5341.1 5354.8 5447.8 5405.6 5333.4 5291.9 5414.4 5317.2 5380.5 5431.5 5363.5 5435.4 5499.8 5447.4 5633 5617.4 5567.8 5574 5710.4 5583.1 5610.8 5620.1 5759.4 5838.7 5843.3 5821 5895.1 5881.6 5827.7 5865.9 5918.4 5875.2 6078.4 5986.3 6019.7 6255.7 6128.4 6210 6301.8 6305.7 6261.2 6200.5 6185.5 6237.4 6399 6182.5 6292.3 6419.8 6273.7 6344.8 6490.4 6355.4 6383.1 6377.3 6324.9 6342.2 6364.1 6249.5 6439.2 6409.4
Type of Seasonality
12
additive
multiplicative
Seasonal Period
1
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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