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Data:
5797.8 5784.3 5714.8 5748.8 5793.8 5783.2 5765 5846.1 5879.4 5922.7 5992.7 6032.5 6028.3 6096.3 6184.8 6206.1 6324 6380.6 6504.6 6591 6637.9 6653.8 6611.3 6603.1 6562.8 6554.9 6529.8 6543.4 6481.5 6489.6 6452.3 6444.5 6409.6 6427.5 6374.2 6400.5 6268.2 6239.5 6220.1 6226.6 6207.1 6217.4 6196.9 6132.9 6151.2 6115.2 6122.6 6140.9 6146.5 6126 6131.9 6190.8 6209.2 6230.8 6196.5 6168.2 6213.4 6243 6298.1 6361.4 6388.7 6416.3 6505.7 6538.7 6605.5 6668.9 6741.7 6813.2 6864.3 6870 6889.8 6938.8 7033.3 7104 7168.7 7156 7156.6 7171.8 7251.2 7258.8 7231.5 7261.7 7252.8 7194.2 7211.9 7177.8 7145.9 7170.6 7189.6 7161 7219.9 7155.3 7155.8 7232.1 7254.9 7278.8 7291.2 7298.6 7256.3 7187.7 7126.3 7034.6 7018.6 7024.4 7028.2 7042.2 7022.2 6998.7 6982.7 6936.6 6887.2 6881.1 6890.9 6947.7 6887.5 6937.1
Type of Seasonality
12
additive
multiplicative
Seasonal Period
Triple
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
1 seconds
R Server
Big Analytics Cloud Computing Center
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