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Data:
5884.5 5879.1 5897.2 5920.7 5944.6 5982.4 6017.4 5980 6087.4 6114.5 6143.2 6173.1 6195.7 6236 6255.2 6282.5 6301.7 6330.9 6350.8 6363 6388.6 6411.5 6436.4 6449.2 6473.3 6479.5 6507.3 6516.1 6534.2 6540.6 6542.9 6562.6 6577 6596.6 6612.1 6626.3 6640.1 6642.4 6648.7 6660.8 6668.2 6657.7 6682.8 6696.8 6714.4 6728.2 6741.8 6758.4 6774 6792.3 6809.1 6832.2 6850.3 6861.1 6882.6 6900.7 6915.1 6947.8 6965.9 6991.7 6993.9 7031.7 7048.7 7067.4 7077.1 7107.4 7127.1 7137.3 7147.9 7170.6 7193 7220.1 7251 7268.1 7282.2 7290.2 7292.5 7299.6 7305.1 7306.9 7313.3 7325.6 7348.1 7354.7 7375.3 7396.3 7401.9 7390.4 7393.6 7398.5 7392.4 7390.8 7380.6 7365.8 7346.9 7334.1 7314.8 7287.8 7274.3 7252.7 7257.5 7256.5 7253.9 7262.6 7263.6 7261.3 7250.4 7249.3 7245.6 7244.4 7253.8 7271.6 7282.7 7283 7293.3 7291.2 7298.5
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 <- '12' par1 <- 'additive' 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
1 seconds
R Server
Big Analytics Cloud Computing Center
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