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Data:
19064.00 18993.00 18921.00 18772.00 20246.00 20168.00 19064.00 18330.00 18401.00 18401.00 18480.00 18622.00 18843.00 18843.00 18701.00 18330.00 20246.00 20538.00 20097.00 19064.00 19506.00 18843.00 19142.00 19285.00 19434.00 19064.00 19142.00 18622.00 20246.00 20759.00 20318.00 19506.00 20389.00 19434.00 20318.00 20246.00 20467.00 19655.00 20538.00 20467.00 21792.00 21493.00 20318.00 19726.00 20538.00 19434.00 20246.00 20389.00 20688.00 20026.00 20389.00 20610.00 21422.00 20759.00 19876.00 18921.00 19805.00 17375.00 18551.00 19213.00 19876.00 18921.00 18921.00 18921.00 19434.00 18701.00 17739.00 16934.00 17518.00 15238.00 16635.00 17447.00 17596.00 16784.00 16855.00 16635.00 17375.00 16855.00 15830.00 15089.00 16342.00 13621.00 15388.00 16193.00 16193.00 15238.00 14355.00 14284.00 15089.00 14355.00 12959.00 11997.00 13030.00 10601.00 12809.00 13984.00 14355.00 13543.00 12517.00 13251.00 13543.00 13322.00 11113.00 10088.00 10821.00 8613.00 10893.00 11705.00 12367.00 11263.00 10230.00 10821.00 11113.00 10529.00 8321.00 7359.00 8242.00 5813.00 8463.00 10088.00
Type of Seasonality
multiplicative
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,signif(m$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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