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Data:
196.9 192.1 201.8 186.9 218 214.4 227.5 204.1 225.8 223.7 244.7 243.9 257.3 234.5 251.4 243.8 247.4 245.3 262.5 270 259.9 262.2 244.9 249.3 268.2 231.2 264.3 252.7 275.5 261.5 275.5 272.3 268.6 270.4 267.7 275 272.6 248.6 279.4 270.5 292.8 297.8 296.8 290.9 282.8 312.8 303.2 301.4 289.8 279.6 302.2 299.1 319.7 310.9 315.2 338.5 315.6 321.2 318.5 342.7 261.4 287 331.5 326.9 338.6 337 358.4 344.5 345.7 344.1 317.4 354.5 345.2 314.1 352.5 361.2 365.9 332.5 364 359.1 345.6 366.9 370.2 359.9 366.6 336.3 368.5 374.2 384.3 358.9 407.7 433.3 404.7 392.7 409.7 416.5 414.3 404.3 421.4 372.6 404.7 420.2 438.4 449.1 445.8 413.8 420.5 442.3 438.9 394.5 416.8 402.9 424.5 432.3 484.1 492.7 496.3 471.9 491.2 512.9 482.4 407.9 448.5 431.1 498.8 497.1 517.1 487.7 512.5 550.1 532.5 524.1 515.7 461 529.3 467.4 559.8 536.5 531.9 546.5 547.4 536.1 482.8 551 532.9 484.1 554.8 537 558 511.4 502.9 558.6 545.1 574.3 542.2 600 588.6 524.4 618.5 580.9 557.2 571.2 597.5 601.7 558.9 600.9 601 615.7 578.1 495.9 526.8 522.1 605.1 574.4 609.7 580.7 565.1 590.7 571.5 601.3 567.3 467.9 588.9 579.4 502.6 568.7 616 586.2 575.5 599.9 568.2 516 493.4 496.8 529.9 491.7 543.2 490.8 554.7 625.7 605 645.2 645.2 611.8 600.3 549.8 635.5 617.7 643.5 485.7 689.5 692 677.3 704.7 668.6 717.8 689.8 640.4 675.2 528.1 538 527.2 655.6 650.6 623.7 748.4 727.4 750.5 678.9 659.5 691.9 639.8 663.8 572.9 592.5 734.8 696.1 589.2 662.9 661.2 672.1 583.7 705.5 631 733.3 674.9 695.5 634.1 630.6 635.2 554.1 623.9 679.3 565.6 564.1 637.2 650.8 602.7 587.5 619.2 616.5 637.9 557.9 594 668.7 603.3 674.5 573.4 706 693.7 627.5 550.7 592.3 660.2 597.3 641 663.6 595.9 638.4 665.4 671.4 637 685.7 705.8 704.8 734.4 674.2 748.6 763.4 658 627.5 528.9 488.3 575.5 735.6 685.3 613.6 629.5 634.7 652.6 728.3 634.3 690.7 676.3 675.4 595.6 712.4 735.8 544.4 567 510 564 630.7 496.7 660.9 601.2 655.2 591.6 606.1 560.7 368.3 371.6 413.9 413.9 389 399.2 429.8 395.6 472 486 525 396 511 525 492 517 525 474 539 468 543 532 565 535 534 546 494 552 511 451 537 494 549 544 598 583 582 589 578 561 592 504 545 547 585 562 520 581 590 562 548 567 542 473 531 462 479 533 552 547 562 524 479 445 406 475 589 495 484 536 555 565 564 573 569 588 546 508 560 558 516 549 595 586 597 592 538 590 576 451 538 555 532 530 553 626 601 573 569 562 468 483 460 411 458 455 600 605 545 549 415 568 577 517 558 518 489 502 569 540 550 557 542 542 582 525 584 562 639 613 604 613 625 654 638
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
48
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
1
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree of non-seasonal differencing (d)
1
0
1
2
Degree of seasonal differencing (D)
0
0
1
2
Seasonality
12
12
1
2
3
4
6
12
CI type
White Noise
White Noise
MA
Confidence Interval
Use logarithms with this base
(overrules the Box-Cox lambda parameter)
(?)
Chart options
R Code
if (par1 == 'Default') { par1 = 10*log10(length(x)) } else { par1 <- as.numeric(par1) } par2 <- as.numeric(par2) par3 <- as.numeric(par3) par4 <- as.numeric(par4) par5 <- as.numeric(par5) if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma' par7 <- as.numeric(par7) if (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF') dev.off() (myacf <- c(racf$acf)) (mypacf <- c(rpacf$acf)) lengthx <- length(x) sqrtn <- sqrt(lengthx) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 2:(par1+1)) { a<-table.row.start(a) a<-table.element(a,i-1,header=TRUE) a<-table.element(a,round(myacf[i],6)) mytstat <- myacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) 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,'Partial Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 1:par1) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,round(mypacf[i],6)) mytstat <- mypacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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