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Data:
126.64 126.81 125.84 126.77 124.34 124.4 120.48 118.54 117.66 116.97 120.11 119.16 116.9 116.11 114.98 113.65 115.82 117.59 118.57 118.07 114.98 114.04 115.02 114.28 115.04 116.7 119.21 118.39 116.5 115.46 117.59 117.33 116.2 116.83 118.99 118.62 121.09 122.4 123.76 125.33 123.23 122.52 123.64 124.67 124.71 122.53 124.4 125.45 125.35 124.3 127.03 128.51 128.1 128.94 129.67 129.87 131.12 132.68 132.24 133.63 129.91 127.93 131.17 130.86 133.48 134.08 136.02 132.8 132.37 133.05 132.57 130.7 130.5 129.67 127.8 126.82 126.85 128.28 128.3 126.82 125.08 128.53 130.34 131.52 132.59 131.17 132.72 133.36 132.82 132.9 130.9 129.41 128.67 129.28 130.91 131.06 130.84 131.41 133.22 132.06 132.48 134.38 135.22 134.89 136.09 136.33 136.32 137.48 136.53 136.8 138.03 137.39 137.55 136.08 134.78 133.28 133.57 134.84 133.02 133.49 133.77 134.34 134.5 134.03 135.51 136.53 135.95 134.32 132.44 133.61 131.02 130.05 128.21 129.03 130.34 131.57 132.63 132.06 134.44 134.1 132.49 134.23 134.92 135.61 134.53 133.86 133.89 135.33 135.86 136.22 137.38 137.31 136.89 138.01 136.72 135.77 137.52 135.61 132.94 134.12 132.55 134.11 134.19 135.57 135.05 134.32 133.61 134.75 133.1 133.26 131.63 132.47 132.45 133.33 133.57 134.13 133.92 132.62 132.3 133.26 132.6 134.38 134.17 135.46 135.09 134.96 133.85 132.59 131.15 130.91 131.07 130.78 129.95 131.41 131.21 130.68 130.46 131.12 132.99 133.02 133.39 134.07 135.6 135.66 135.53 135.82 136.9 137.97 138.09 136.91 134.76 135.13 134.66 132.95 132.25 134.3 134.3 134.76 134.81 134.51 135.11 134.32 133.51 134.02 132.76 133.39 132.05 131.87 133.03 132.57 132.1 130.7 129.2 129.77 131.02 131.55 133.17 133.08 133.24 130.74 129.91 130.03 131.13 129.55 130.22 130.61 129.27 129.68 130.1 130.83 130.95 131.73 131.86 132.44 132.35 133.16 133.62 132.54 132.69 133.5 133.36 134.23 132.41 133.02 132.88 130.76 130.33 129.79 128.65 129.14 127.35 127.74 126.31 125.95 126.36 126.15 125.6 126.2 126.73 125.68 122.49 122.07 123.4 123.01 123.03 122.33 122.42 122.68 124.69 123.3 124.17 124.38 123.19 122.16 120.66 120.92 120.67 120.68 121.1 120.86 121.48 123.48 121.72 123.16 123.84 124.57 124.3 124.22 124.43 123.33 122.86 121.25 122.16 122.62 123.44 124 124.75 124.8 125.93 126.28 126.04 125.04 123.76 125.34 126.99 126.34 127.42 126.18 125.3 123.5 125.32 124.65 124.03 125.11 125.46 124.7 124.48 124.76 125.81 124.95 123.66 122.66 119.34 117.84 120.97 117.38 118.06 116.99 115.55 114.17 115.32 112.49 111.93 112.08 111.63 109.53 111.35 110.79 113.06 112.62 110.65 112.36 113.74 111.73 109.86 109.32 109.99 109.84 111.13 112.43 111.77 112.15 112.89 112.12 113.1 111.09 110.76 109.59 109.99 110.25 108.31 108.79 108.14 109.88 109.93 110.46 109.56 111.49 111.85 111.35 110.95 112.49 113.11 112.54 112.84 111.5 111.52 111.57 112.48 112.31 113.79 114.01 113.64 112.62 113.27 113.51 112.92 113.66 113.14 113.48 113.23 110.56 109.5 109.78 109.49 109.66 109.93 109.82 108.54 108.23 106.19 106.49 107.15 107.74 107.54 107.07 107.54 107.81 108.38 108.42 106.86 106.41 106.46 106.84 107.69 107.04 111.04 111.93 111.98 112.07 112.05 113.14 112.49 113.2 113.52 113.22 113.85 113.68 114.26 114.1 114.8 114.98 115.1 114.21 114.24 113.35 114.23 114.43 114.28 113 113.16 112.59 113.65 113.18 113.21 113.11 112.78 112.57 111.87 111.94 113.18 113.67 115.15 114.41 112.88 112.44 113.48 112.78 112.59 113.31 113.21 112.5 113.72 114.09 113.97 112.5 111.28 111.35 110.92 110.73 109
Seasonal period
additive
12
1
2
3
4
5
6
7
8
9
10
11
12
Seasonal window
(?)
Seasonal degree
(?)
0
1
Trend window
(?)
Trend degree
(?)
1
0
Low-pass window
(?)
Low-pass degree
(?)
1
0
Robust loess fitting
FALSE
TRUE
Chart options
Title:
R Code
par1 <- 5 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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Computing time
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R Server
Big Analytics Cloud Computing Center
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