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Data:
95320.00 94965.00 94605.00 93860.00 101230.00 100840.00 95320.00 91650.00 92005.00 92005.00 92400.00 93110.00 94215.00 94215.00 93505.00 91650.00 101230.00 102690.00 100485.00 95320.00 97530.00 94215.00 95710.00 96425.00 97170.00 95320.00 95710.00 93110.00 101230.00 103795.00 101590.00 97530.00 101945.00 97170.00 101590.00 101230.00 102335.00 98275.00 102690.00 102335.00 108960.00 107465.00 101590.00 98630.00 102690.00 97170.00 101230.00 101945.00 103440.00 100130.00 101945.00 103050.00 107110.00 103795.00 99380.00 94605.00 99025.00 86875.00 92755.00 96065.00 99380.00 94605.00 94605.00 94605.00 97170.00 93505.00 88695.00 84670.00 87590.00 76190.00 83175.00 87235.00 87980.00 83920.00 84275.00 83175.00 86875.00 84275.00 79150.00 75445.00 81710.00 68105.00 76940.00 80965.00 80965.00 76190.00 71775.00 71420.00 75445.00 71775.00 64795.00 59985.00 65150.00 53005.00 64045.00 69920.00 71775.00 67715.00 62585.00 66255.00 67715.00 66610.00 55565.00 50440.00 54105.00 43065.00 54465.00 58525.00 61835.00 56315.00 51150.00 54105.00 55565.00 52645.00 41605.00 36795.00 41210.00 29065.00 42315.00 50440.00
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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