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Data:
5682.5 5286.5 6608 6826 6994.5 7509 6599.5 7312.5 7791.5 7302.5 6168.5 5303 5631.5 5515.5 6676.5 6592.5 7101 7793 7177 7843 7858.5 7576.5 6229 5739.5 6559 6126.5 7486.5 7627.5 7602.5 7710 8102 7533.5 7736.5 8695 6885 6079.5 6874 5194 6980.5 7280.5 7313.5 7670.5 7251 7909.5 8434 8333.5 6898.5 6637 6683 5584 6510.5 7263 7114.5 8114.5 7228.5 8126 8071 8602.5 7371.5 6678 6440.5 6207.5 7067.5 7512 7474.5 8992 8265.5 7841 8758.5 9183.5 7792 7670.5 6530.5 6621.5 7870 7250.5 7779.5 9027.5 7891.5 9021 8559 8738 8051.5 7354 7182 6319.5 7353.5 7519.5 7854.5 8996.5 7684.5 8708.5 8371 8385.5 7916 6872 7492 6515 7875 7487 8195.5 8533 8688 8892.5 8221.5 8652.5 7410.5 6457 6400.5 6260.5 7220 7617 7922 8934 8395 8945 9738 9224 6923 6572.5 6962.5 7038 8564 8534 8420.5 8831
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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Big Analytics Cloud Computing Center
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