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Data:
235.1 280.7 264.6 240.7 201.4 240.8 241.1 223.8 206.1 174.7 203.3 220.5 299.5 347.4 338.3 327.7 351.6 396.6 438.8 395.6 363.5 378.8 357.00 369.00 464.8 479.1 431.3 366.5 326.3 355.1 331.6 261.3 249.00 205.5 235.6 240.9 264.9 253.8 232.3 193.8 177.00 213.2 207.2 180.6 188.6 175.4 199.00 179.6 225.8 234.00 200.2 183.6 178.2 203.2 208.5 191.8 172.8 148.00 159.4 154.5 213.2 196.4 182.8 176.4 153.6 173.2 171.00 151.2 161.9 157.2 201.7 236.4 356.1 398.3 403.7 384.6 365.8 368.1 367.9 347.00 343.3 292.9 311.5 300.9 366.9 356.9 329.7 316.2 269.00 289.3 266.2 253.6 233.8 228.4 253.6 260.1 306.6 309.2 309.5 271.00 279.9 317.9 298.4 246.7 227.3 209.1 259.9 266.00 320.6 308.5 282.2 262.7 263.5 313.1 284.3 252.6 250.3 246.5 312.7 333.2 446.4 511.6 515.5 506.4 483.2 522.3 509.8 460.7 405.8 375.00 378.5 406.8 467.8 469.8 429.8 355.8 332.7 378.00 360.5 334.7 319.5 323.1 363.6 352.1 411.9 388.6 416.4 360.7 338.00 417.2 388.4 371.1 331.5 353.7 396.7 447.00 533.5 565.4 542.3 488.7 467.1 531.3 496.1 444.00 403.4 386.3 394.1 404.1 462.1 448.1 432.3 386.3 395.2 421.9 382.9 384.2 345.5 323.4 372.6 376.00 462.7 487.00 444.2 399.3 394.9 455.4 414.00 375.5 347.00 339.4 385.8 378.8 451.8 446.1 422.5 383.1 352.8 445.3 367.5 355.1 326.2 319.8 331.8 340.9 394.1 417.2 369.9 349.2 321.4 405.7 342.9 316.5 284.2 270.9 288.8 278.8 324.4 310.9 299.00 273.00 279.3 359.2 305.00 282.1 250.3 246.5 257.9 266.5 315.9 318.4 295.4 266.4 245.8 362.8 324.9 294.2 289.5 295.2 290.3 272.00 307.4 328.7 292.9 249.1 230.4 361.5 321.7 277.2 260.7 251.00 257.6 241.8 287.5 292.3 274.7 254.2 230.00 339.00 318.2 287.00 295.8 284.00 271.00 262.7 340.6 379.4 373.3 355.2 338.4 466.9 451.00 422.00 429.2 425.9 460.7 463.6 541.4 544.2 517.5 469.4 439.4 549.00 533.00 506.1 484.00 457.00 481.5 469.5 544.7 541.2 521.5 469.7 434.4 542.6 517.3 485.7 465.8 447.00 426.6 411.6 467.5 484.5 451.2 417.4 379.9 484.7 455.00 420.8 416.5 376.3 405.6 405.8 500.8 514.00 475.5 430.1 414.4 538.00 526.00 488.5 520.2 504.4 568.5 610.6 818.00 830.9 835.9 782.00 762.3 856.9 820.9 769.6 752.2 724.4 723.1 719.5 817.4 803.3 752.5 689.00 630.4 765.5 757.7 732.2 702.6 683.3 709.5 702.2 784.8 810.9 755.6 656.8 615.1 745.3 694.1 675.7 643.7 622.1 634.6 588.00 689.7 673.9 647.9 568.8 545.7 632.6 643.8 593.1 579.7 546.00 562.9 572.5
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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Big Analytics Cloud Computing Center
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