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Data:
3469648.00 3456726.00 3443622.00 3416504.00 3684772.00 3670576.00 3469648.00 3336060.00 3348982.00 3348982.00 3363360.00 3389204.00 3429426.00 3429426.00 3403582.00 3336060.00 3684772.00 3737916.00 3657654.00 3469648.00 3550092.00 3429426.00 3483844.00 3509870.00 3536988.00 3469648.00 3483844.00 3389204.00 3684772.00 3778138.00 3697876.00 3550092.00 3710798.00 3536988.00 3697876.00 3684772.00 3724994.00 3577210.00 3737916.00 3724994.00 3966144.00 3911726.00 3697876.00 3590132.00 3737916.00 3536988.00 3684772.00 3710798.00 3765216.00 3644732.00 3710798.00 3751020.00 3898804.00 3778138.00 3617432.00 3443622.00 3604510.00 3162250.00 3376282.00 3496766.00 3617432.00 3443622.00 3443622.00 3443622.00 3536988.00 3403582.00 3228498.00 3081988.00 3188276.00 2773316.00 3027570.00 3175354.00 3202472.00 3054688.00 3067610.00 3027570.00 3162250.00 3067610.00 2881060.00 2746198.00 2974244.00 2479022.00 2800616.00 2947126.00 2947126.00 2773316.00 2612610.00 2599688.00 2746198.00 2612610.00 2358538.00 2183454.00 2371460.00 1929382.00 2331238.00 2545088.00 2612610.00 2464826.00 2278094.00 2411682.00 2464826.00 2424604.00 2022566.00 1836016.00 1969422.00 1567566.00 1982526.00 2130310.00 2250794.00 2049866.00 1861860.00 1969422.00 2022566.00 1916278.00 1514422.00 1339338.00 1500044.00 1057966.00 1540266.00 1836016.00
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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