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Data:
5947968.00 5925816.00 5903352.00 5856864.00 6316752.00 6292416.00 5947968.00 5718960.00 5741112.00 5741112.00 5765760.00 5810064.00 5879016.00 5879016.00 5834712.00 5718960.00 6316752.00 6407856.00 6270264.00 5947968.00 6085872.00 5879016.00 5972304.00 6016920.00 6063408.00 5947968.00 5972304.00 5810064.00 6316752.00 6476808.00 6339216.00 6085872.00 6361368.00 6063408.00 6339216.00 6316752.00 6385704.00 6132360.00 6407856.00 6385704.00 6799104.00 6705816.00 6339216.00 6154512.00 6407856.00 6063408.00 6316752.00 6361368.00 6454656.00 6248112.00 6361368.00 6430320.00 6683664.00 6476808.00 6201312.00 5903352.00 6179160.00 5421000.00 5787912.00 5994456.00 6201312.00 5903352.00 5903352.00 5903352.00 6063408.00 5834712.00 5534568.00 5283408.00 5465616.00 4754256.00 5190120.00 5443464.00 5489952.00 5236608.00 5258760.00 5190120.00 5421000.00 5258760.00 4938960.00 4707768.00 5098704.00 4249752.00 4801056.00 5052216.00 5052216.00 4754256.00 4478760.00 4456608.00 4707768.00 4478760.00 4043208.00 3743064.00 4065360.00 3307512.00 3996408.00 4363008.00 4478760.00 4225416.00 3905304.00 4134312.00 4225416.00 4156464.00 3467256.00 3147456.00 3376152.00 2687256.00 3398616.00 3651960.00 3858504.00 3514056.00 3191760.00 3376152.00 3467256.00 3285048.00 2596152.00 2296008.00 2571504.00 1813656.00 2640456.00 3147456.00
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,signif(m$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
0 seconds
R Server
Big Analytics Cloud Computing Center
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