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Data:
1674.15 1676.16 1665.27 1726.97 1769.37 1800.15 1808.78 1740.94 1716.27 1703.65 1669.41 1623.3 1662.22 1684.55 1669.95 1706.39 1702.15 1721.8 1723.59 1724.03 1769.91 1776.18 1832.94 1834.98 1872.84 1939.26 2003.65 2100.73 2153.45 2205.94 2227.82 2201.25 2267.36 2305.11 2380.24 2317.6 2418.13 2462.32 2476.02 2559.13 2592.53 2595.72 2658.63 2718.57 2783.86 2834.64 2920.23 2939.09 2977.04 2974.23 2988.85 3003.09 3103.64 3145.98 3139.76 3226.48 3269.67 3299.55 3265.36 3259.48 3339.16 3374.52 3371.33 3497.63 3554.45 3512.53 3474.21 3479.17 3601.73 3611.24 3540.12 3735.86 3783.03 3882.02 3876.33 4086.62 4154.92 4151.25 4190.03 4220.9 4251.04 4295.49 4423.96 4517.62 4686.9 4870.07 4867.83 4986.06 5015.89 5043.71 5030.95 5137.86 5118.81 5124.46 5164.26 5218.59 5317.28 5411.99 5418.55 5533.68 5482.58 5438.63 5448.32 5473.64 5617.73 5583.75 5559.94 5578.11 5708.65 5682.25 5650.27 5563.39 5444.99 5422.65 5435.63 5340.52 5321.07 5256.24 5296.99 5281.24 5355.44 5347.89 5384.93 5475.77 5377.91 5459.4
Type of Seasonality
additive
multiplicative
Seasonal Period
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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