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Data:
-45.6 16.1 23.9 39.3 -39.4 -0.3 17.3 17.7 31.4 -28.6 -17.2 -79 -47.9 9.1 10.6 -23.9 -45 -42.2 43.2 32.1 -15.3 21.8 -12 -95.8 -14.3 47.8 64.8 40.2 -28.8 23.5 70.3 12.3 43.5 -30.1 -5.3 -24 11.1 21.5 38.5 16.8 -36.2 6 26.6 -8 13.2 -23.6 19.4 -46.2 -8.2 33.8 16.6 5.4 -25 -5.3 16.7 19 24.8 -11.4 4.9 -58.7 16.8 13.6 6.4 22.8 -19.6 2.2 19.8 -10.7 4.7 -44.5 -34.7 -119.7 -42.2 -5.4 19.1 18.8 -2.3 0.2 20.9 3.7 50.4 -18.6 10.6 -66 10 27.2 13.5 47.2 -20.3 23.1 12.6 19.8 5.4 -25.2 -6.5 -46.5 -2.6 -0.3 38.5 -8.9 -38 19.5 51.7 19.4 18.2 -50.8 -6.1 -54.6 12.1 26.3 19.5 -0.8 -49.6 28.8 31.7 2.3 3.8 -66.2 -20.5 -113.2 -65.2 -3.9 9.1 23.2 -39.1 12.5 49.1 54.9 30.8 -3.5 -28.3 -61 -2 40 74 23.1 -45.3 17.5 25.8 15.2 -3.6 -40.5 11.5 -59.8 23.3 -27.8 55.7 22.7 -79.2 28.8 17.3 39.6 -22.2 -43 -50.3 -86.5 -31.9 23.1 53.6 21.6 -64.2 35.2 52.1 40.6 17.1 -7.8 -10 -58 14 15.8 46 -8.9 -26.7 39 -1.3 38.7 22.1 -49.2 -3.4 -86.7 -24.3 42.8 44.9 4.4 -60.5 41.4 38.5 28.5 7.6 -46.4 7 -73 5.7 23.6 39.4 30.3 -92.5 77.8 12.4 28.9 6.4 -12 -9.1 -53.2 -23.1 47.3 20.7 27.8 -84.3 62.8 26.4 32.3 13.3 -17.9 10 -45.6 13.5 11.9 26 -6.3 -79.9 54.2 22.9 31.8 3.8 -11.4 -8.6 -49.4 -2.5 23 29 20.6 -117 37.9 30.7 4.7 -5.7 4.9 18.3 -35.4 -21.3 35.8 43.8 18.7 -131.1 39.8 44.5 16.5 9.7 -6.6 15.8 -45.7 -4.8 17.6 20.5 24.2 -109 20.8 31.2 -8.8 11.8 13 8.3 -77.9 -38.8 6.1 18.1 16.8 -128.5 15.9 29 -7.2 3.3 -34.8 -2.9 -77.8 -2.8 26.7 48.1 30 -109.6 16 26.9 22.1 27 -24.5 12 -75.2 3.5 19.7 51.8 35.3 -108.2 25.3 31.6 19.9 18.8 20.4 15 -55.9 -17 33.3 33.8 37.5 -104.8 29.7 34.2 4.3 40.2 -29.3 -0.2 -95 -13.2 38.5 45.4 15.7 -123.6 12 37.5 -31.7 15.8 -64.1 -42.1 -207.4 -12.9 -5 53.9 19.7 -94.6 36 51.3 17.4 27.8 1.3 3.6 -97.9 14.1 50.8 63.5 58.6 -135.1 7.8 25.5 29.6 19.3 -26.2 7.3 -82.6 -26.1 55.3 98.8 41.7 -130.2 51.2 18.4 32 21.6 -12.5 46.6 -101.7 15.8 26 79.1 23.1 -86.9 -11.2 50.7 13.4 33.7 -16.9 -9.6
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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')
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0 seconds
R Server
Big Analytics Cloud Computing Center
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