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Data:
235.10 280.70 264.60 240.70 201.40 240.80 241.10 223.80 206.10 174.70 203.30 220.50 299.50 347.40 338.30 327.70 351.60 396.60 438.80 395.60 363.50 378.80 357.00 369.00 464.80 479.10 431.30 366.50 326.30 355.10 331.60 261.30 249.00 205.50 235.60 240.90 264.90 253.80 232.30 193.80 177.00 213.20 207.20 180.60 188.60 175.40 199.00 179.60 225.80 234.00 200.20 183.60 178.20 203.20 208.50 191.80 172.80 148.00 159.40 154.50 213.20 196.40 182.80 176.40 153.60 173.20 171.00 151.20 161.90 157.20 201.70 236.40 356.10 398.30 403.70 384.60 365.80 368.10 367.90 347.00 343.30 292.90 311.50 300.90 366.90 356.90 329.70 316.20 269.00 289.30 266.20 253.60 233.80 228.40 253.60 260.10 306.60 309.20 309.50 271.00 279.90 317.90 298.40 246.70 227.30 209.10 259.90 266.00 320.60 308.50 282.20 262.70 263.50 313.10 284.30 252.60 250.30 246.50 312.70 333.20 446.40 511.60 515.50 506.40 483.20 522.30 509.80 460.70 405.80 375.00 378.50 406.80 467.80 469.80 429.80 355.80 332.70 378.00 360.50 334.70 319.50 323.10 363.60 352.10 411.90 388.60 416.40 360.70 338.00 417.20 388.40 371.10 331.50 353.70 396.70 447.00 533.50 565.40 542.30 488.70 467.10 531.30 496.10 444.00 403.40 386.30 394.10 404.10 462.10 448.10 432.30 386.30 395.20 421.90 382.90 384.20 345.50 323.40 372.60 376.00 462.70 487.00 444.20 399.30 394.90 455.40 414.00 375.50 347.00 339.40 385.80 378.80 451.80 446.10 422.50 383.10 352.80 445.30 367.50 355.10 326.20 319.80 331.80 340.90 394.10 417.20 369.90 349.20 321.40 405.70 342.90 316.50 284.20 270.90 288.80 278.80 324.40 310.90 299.00 273.00 279.30 359.20 305.00 282.10 250.30 246.50 257.90 266.50 315.90 318.40 295.40 266.40 245.80 362.80 324.90 294.20 289.50 295.20 290.30 272.00 307.40 328.70 292.90 249.10 230.40 361.50 321.70 277.20 260.70 251.00 257.60 241.80 287.50 292.30 274.70 254.20 230.00 339.00 318.20 287.00 295.80 284.00 271.00 262.70 340.60 379.40 373.30 355.20 338.40 466.90 451.00 422.00 429.20 425.90 460.70 463.60 541.40 544.20 517.50 469.40 439.40 549.00 533.00 506.10 484.00 457.00 481.50 469.50 544.70 541.20 521.50 469.70 434.40 542.60 517.30 485.70 465.80 447.00 426.60 411.60 467.50 484.50 451.20 417.40 379.90 484.70 455.00 420.80 416.50 376.30 405.60 405.80 500.80 514.00 475.50 430.10 414.40 538.00 526.00 488.50 520.20 504.40 568.50 610.60 818.00 830.90 835.90 782.00 762.30 856.90 820.90 769.60 752.20 724.40 723.10 719.50 817.40 803.30 752.50 689.00 630.40 765.50 757.70 732.20 702.60 683.30 709.50 702.20 784.80 810.90 755.60 656.80 615.10 745.30 694.10 675.70 643.70 622.10 634.60 588.00 689.70 673.90 647.90 568.80 545.70 632.60 643.80 593.10 579.70 546.00 562.90 572.50
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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