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Data:
35 36.1 40.1 35.4 37.4 39.9 32 32.6 44.9 36.3 43.7 39.8 42.6 48.6 49.1 46.9 45.7 56.1 38.3 40.6 46.5 51.4 47 44.6 51 51.1 54.9 52.1 48.7 50.5 47.5 44.6 50.3 54.3 50 44.8 57.6 47.2 59.1 53.9 45.7 54.5 52.8 52.9 66 63.7 54.4 74.4 50.1 62.5 77.2 65.6 58.2 72.6 68.6 63.1 76.9 70.6 71.4 90.6 71.9 60.9 72.9 69.2 64.8 70.2 63 62.2 82.8 77.6 71.2 70.6 71.1 74 87.9 68.3 68.1 75.7 62.7 66.2 81.3 84 80 80.8 67.3 61.9 77.2 65.6 68.7 82 81.4 70.9 71.2 71.9 71.6 76.4 75.6 73.2 80.2 74 69.5 82 82.8 64.5 92.6 82 78.4 103.8 66.6 73.3 92.3 73.6 74.9 83.6 83.3 70.9 82.5 81.7 83.1 92.4 86.9 110.1 112.1 81.5 84.3 113.5 100.3 93.2 100.4 94.4 110.2 113 94.6 111 160.1 110.1 102.8 112.4 105.4 130.4 117.2 103.9 92.2 95.8 93.1 93.9 147.6 89.6 83 99.2 118.3 110.9 124.4 115.8 112.7 111.9 108.6 102.5 141.9 137.7 121.3 142.8 143 121.1 130.2 146.3 143.7 139.3 109.3 141.3 152.7 152.2 151.8 180.5 129 126.1 187.9 170 168.4 157.1 133.9 103.1 166.3 148 131.4 136.3 135.8 151.8 172.2 154.4 158 146.2 128 124.7 160.3 148.1 139.7 194 188.7 172.2 184.8 160.5 139.7 219.8 143.9 166.2 182.7 152.7 146.8 177.1 186 189.2
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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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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