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Data:
58.4 64.8 73.8 65 73 71.1 58.2 64 75 74.9 75 68.3 72.5 72.4 79.6 70.7 76.4 79.7 64.2 67.9 74.1 78.5 73.4 65.4 69.9 69.6 76.8 75.6 74 76 68.1 65.5 76.9 81.7 73.6 68.7 73.3 71.5 78.3 76.5 71.8 77.6 70 64 81.3 82.5 73.1 78.1 70.7 74.9 88 81.3 75.7 89.8 74.6 74.9 90 88.1 84.9 87.7 80.5 79 89.9 86.3 81.1 92.4 71.8 76.1 92.5 87 89.5 88.7 83.8 84.9 99 84.6 92.7 97.6 78 81.9 96.5 99.9 96.2 90.5 91.4 89.7 102.7 91.5 96.2 104.5 90.3 90.3 100.4 111.3 101.3 94.4 100.4 102 104.3 108.8 101.3 108.9 98.5 88.8 111.8 109.6 92.5 94.5 80.8 83.7 94.2 86.2 89 94.7 81.9 80.2 96.5 95.6 91.9 89.9 86.3 94 108 96.3 94.6 111.7 92 91.9 109.2 106.8 105.8 103.6 97.6 102.8 124.8 103.9 112.2 108.5 92.4 101.1 114.9 106.4 104 101.6 99.4 102.3 121.3 99.3 102.9 111.4 98.5 98.5 108.5 112.1 105.3 95.2 98.2 96.6 109.6 108 106.7 111.5 104.5 94.3 109.6 116.4 106.5 100.5 101.7 104.1 112.3 111.2 108.2 115.1 102.3 93.6 120.6 118.4 106.6 105.3 101.5 100.1 119.5 111.2 103.7 117.8 101.7 97.4 120 117 110.6 105.3 100.9 108.1 119.3 113 108.6 123.3 101.4 103.5 119.4 113.1 112 115.8 105.4 110.9 128.5 109 117.2 124.4 104.7 108.6
Seasonal period
12
12
1
2
3
4
5
6
7
8
9
10
11
12
Seasonal window
(?)
Seasonal degree
(?)
0
0
1
Trend window
(?)
Trend degree
(?)
1
1
0
Low-pass window
(?)
Low-pass degree
(?)
1
1
0
Robust loess fitting
FALSE
FALSE
TRUE
Chart options
Title:
R Code
par1 <- as.numeric(par1) #seasonal period if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window par3 <- as.numeric(par3) #s.degree if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window par5 <- as.numeric(par5)#t.degree if (par6 != '') par6 <- as.numeric(par6)#l.window par7 <- as.numeric(par7)#l.degree if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust nx <- length(x) x <- ts(x,frequency=par1) if (par6 != '') { m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8) } else { m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8) } m$time.series m$win m$deg m$jump m$inner m$outer bitmap(file='test1.png') plot(m,main=main) dev.off() mylagmax <- nx/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Component',header=TRUE) a<-table.element(a,'Window',header=TRUE) a<-table.element(a,'Degree',header=TRUE) a<-table.element(a,'Jump',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,m$win['s']) a<-table.element(a,m$deg['s']) a<-table.element(a,m$jump['s']) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,m$win['t']) a<-table.element(a,m$deg['t']) a<-table.element(a,m$jump['t']) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Low-pass',header=TRUE) a<-table.element(a,m$win['l']) a<-table.element(a,m$deg['l']) a<-table.element(a,m$jump['l']) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observed',header=TRUE) a<-table.element(a,'Fitted',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Remainder',header=TRUE) a<-table.row.end(a) for (i in 1:nx) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]+m$time.series[i,'remainder']) a<-table.element(a,m$time.series[i,'seasonal']) a<-table.element(a,m$time.series[i,'trend']) a<-table.element(a,m$time.series[i,'remainder']) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')
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R Server
Big Analytics Cloud Computing Center
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