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Data:
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 0.357 0.369 464.8 479.1 431.3 366.5 326.3 355.1 331.6 261.3 0.249 205.5 235.6 240.9 264.9 253.8 232.3 193.8 0.177 213.2 207.2 180.6 188.6 175.4 0.199 179.6 225.8 0.234 200.2 183.6 178.2 203.2 208.5 191.8 172.8 0.148 159.4 154.5 213.2 196.4 182.8 176.4 153.6 173.2 0.171 151.2 161.9 157.2 201.7 236.4 356.1 398.3 403.7 384.6 365.8 368.1 367.9 0.347 343.3 292.9 311.5 300.9 366.9 356.9 329.7 316.2 0.269 289.3 266.2 253.6 233.8 228.4 253.6 260.1 306.6 309.2 309.5 0.271 279.9 317.9 298.4 246.7 227.3 209.1 259.9 0.266 320.6 308.5 282.2 262.7 263.5 313.1 284.3 252.6 250.3 246.5 312.7 333.2 446.4 511.6 515.5 506.4 483.2 522.3 509.8 460.7 405.8 0.375 378.5 406.8 467.8 469.8 429.8 355.8 332.7 0.378 360.5 334.7 319.5 323.1 363.6 352.1 411.9 388.6 416.4 360.7 0.338 417.2 388.4 371.1 331.5 353.7 396.7 0.447 533.5 565.4 542.3 488.7 467.1 531.3 496.1 0.444 403.4 386.3 394.1 404.1 462.1 448.1 432.3 386.3 395.2 421.9 382.9 384.2 345.5 323.4 372.6 0.376 462.7 0.487 444.2 399.3 394.9 455.4 0.414 375.5 0.347 339.4 385.8 378.8 451.8 446.1 422.5 383.1 352.8 445.3 367.5 355.1 326.2 319.8 331.8 340.9 394.1 417.2 369.9 349.2 321.4 405.7 342.9 316.5 284.2 270.9 288.8 278.8 324.4 310.9 0.299 0.273 279.3 359.2 0.305 282.1 250.3 246.5 257.9 266.5 315.9 318.4 295.4 266.4 245.8 362.8 324.9 294.2 289.5 295.2 290.3 0.272 307.4 328.7 292.9 249.1 230.4 361.5 321.7 277.2 260.7 0.251 257.6 241.8 287.5 292.3 274.7 254.2 0.23 0.339 318.2 0.287 295.8 0.284 0.271 262.7 340.6 379.4 373.3 355.2 338.4 466.9 0.451 0.422 429.2 425.9 460.7 463.6 541.4 544.2 517.5 469.4 439.4 0.549 0.533 506.1 0.484 0.457 481.5 469.5 544.7 541.2 521.5 469.7 434.4 542.6 517.3 485.7 465.8 0.447 426.6 411.6 467.5 484.5 451.2 417.4 379.9 484.7 0.455 420.8 416.5 376.3 405.6 405.8 500.8 0.514 475.5 430.1 414.4 0.538 0.526 488.5 520.2 504.4 568.5 610.6 0.818 830.9 835.9 0.782 762.3 856.9 820.9 769.6 752.2 724.4 723.1 719.5 817.4 803.3 752.5 0.689 630.4 765.5 757.7 732.2 702.6 683.3 709.5 702.2 784.8 810.9 755.6 656.8 615.1 745.3 694.1 675.7 643.7 622.1 634.6 0.588 689.7 673.9 647.9 568.8 545.7 632.6 643.8 593.1 579.7 0.546 562.9 572.5
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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R Server
Big Analytics Cloud Computing Center
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