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Data:
315.42 316.32 316.49 317.56 318.13 318.00 316.39 314.66 313.68 313.18 314.66 315.43 316.27 316.81 317.42 318.87 319.87 319.43 318.01 315.75 314.00 313.68 314.84 316.03 316.73 317.54 318.38 319.31 320.42 319.61 318.42 316.64 314.83 315.15 315.95 316.85 317.78 318.40 319.53 320.41 320.85 320.45 319.44 317.25 316.12 315.27 316.53 317.53 318.58 318.92 319.70 321.22 322.08 321.31 319.58 317.61 316.05 315.83 316.91 318.20 319.41 320.07 320.74 321.40 322.06 321.73 320.27 318.54 316.54 316.71 317.53 318.55 319.27 320.28 320.73 321.97 322.00 321.71 321.05 318.71 317.65 317.14 318.71 319.25 320.46 321.43 322.22 323.54 323.91 323.59 322.26 320.21 318.48 317.94 319.63 320.87 322.17 322.34 322.88 324.25 324.83 323.93 322.39 320.76 319.10 319.23 320.56 321.80 322.40 322.99 323.73 324.86 325.41 325.19 323.97 321.92 320.10 319.96 320.97 322.48 323.52 323.89 325.04 326.01 326.67 325.96 325.13 322.90 321.61 321.01 322.08 323.37 324.34 325.30 326.29 327.54 327.54 327.21 325.98 324.42 322.91 322.90 323.85 324.96 326.01 326.51 327.01 327.62 328.76 328.40 327.20 325.28 323.20 323.40 324.64 325.85 326.60 327.47 327.58 329.56 329.90 328.92 327.89 326.17 324.68 325.04 326.34 327.39 328.37 329.40 330.14 331.33 332.31 331.90 330.70 329.15 327.34 327.02 327.99 328.48 329.18 330.55 331.32 332.48 332.92 332.08 331.02 329.24 327.28 327.21 328.29 329.41 330.23 331.24 331.87 333.14 333.80 333.42 331.73 329.90 328.40 328.17 329.32 330.59 331.58 332.39 333.33 334.41 334.71 334.17 332.88 330.77 329.14 328.77 330.14 331.52 332.75 333.25 334.53 335.90 336.57 336.10 334.76 332.59 331.41 330.98 332.24 333.68 334.80 335.22 336.47 337.59 337.84 337.72 336.37 334.51 332.60 332.37 333.75 334.79 336.05 336.59 337.79 338.71 339.30 339.12 337.56 335.92 333.74 333.70 335.13 336.56 337.84 338.19 339.90 340.60 341.29 341.00 339.39 337.43 335.72 335.84 336.93 338.04 339.06 340.30 341.21 342.33 342.74 342.07 340.32 338.27 336.52 336.68 338.19 339.44 340.57 341.44 342.53 343.39 343.96 343.18 341.88 339.65 337.80 337.69 339.09 340.32 341.20 342.35 342.93 344.77 345.58 345.14 343.81 342.22 339.69 339.82 340.98 342.82 343.52 344.33 345.11 346.88 347.25 346.61 345.22 343.11 340.90 341.17 342.80 344.04 344.79 345.82 347.25 348.17 348.75 348.07 346.38 344.52 342.92 342.63 344.06 345.38 346.12 346.79 347.69 349.38 350.04 349.38 347.78 345.75 344.70 344.01 345.50 346.75 347.86 348.32 349.26 350.84 351.70 351.11 349.37 347.97 346.31 346.22 347.68 348.82 350.29 351.58 352.08 353.45 354.08 353.66 352.25 350.30 348.58 348.74 349.93 351.21 352.62 352.93 353.54 355.27 355.52 354.97 353.74 351.51 349.63 349.82 351.12 352.35 353.47 354.51 355.18 355.98 356.94 355.99 354.58 352.68 350.72 350.92 352.55 353.91
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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Big Analytics Cloud Computing Center
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