R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(411
+ ,410
+ ,415
+ ,414
+ ,411
+ ,408
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+ ,411
+ ,416
+ ,479
+ ,498
+ ,502
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+ ,554
+ ,601
+ ,622
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+ ,600
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+ ,567
+ ,555
+ ,555
+ ,608
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+ ,629
+ ,624
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+ ,481
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+ ,457
+ ,442
+ ,439
+ ,488
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+ ,464
+ ,460
+ ,467
+ ,460
+ ,448
+ ,443
+ ,436
+ ,431
+ ,484
+ ,510
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+ ,503
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+ ,471
+ ,476
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+ ,470
+ ,461
+ ,455
+ ,456
+ ,517
+ ,525
+ ,523
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+ ,509
+ ,512
+ ,519
+ ,517
+ ,510
+ ,509
+ ,501
+ ,507
+ ,569
+ ,580
+ ,578
+ ,565
+ ,547
+ ,555
+ ,562
+ ,561
+ ,555
+ ,544
+ ,537
+ ,543
+ ,594
+ ,611
+ ,613
+ ,611
+ ,594
+ ,595
+ ,591
+ ,589
+ ,584
+ ,573
+ ,567
+ ,569
+ ,621
+ ,629
+ ,628
+ ,612
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+ ,590
+ ,580
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+ ,573
+ ,573
+ ,620
+ ,626
+ ,620
+ ,588
+ ,566
+ ,557
+ ,561
+ ,549
+ ,532
+ ,526
+ ,511
+ ,499
+ ,555
+ ,565
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+ ,527
+ ,510
+ ,514
+ ,517
+ ,508
+ ,493
+ ,490
+ ,469
+ ,478
+ ,528
+ ,534
+ ,518
+ ,506
+ ,502
+ ,516
+ ,528
+ ,533
+ ,536
+ ,537
+ ,524
+ ,536
+ ,587
+ ,597
+ ,581
+ ,564
+ ,558
+ ,575
+ ,580
+ ,575
+ ,563
+ ,552
+ ,537
+ ,545
+ ,601
+ ,604
+ ,586
+ ,564
+ ,549
+ ,551
+ ,556
+ ,548
+ ,540
+ ,531
+ ,521
+ ,519
+ ,572
+ ,581
+ ,563
+ ,548
+ ,411
+ ,410
+ ,415
+ ,414
+ ,411
+ ,408
+ ,410
+ ,411
+ ,416
+ ,479
+ ,498
+ ,502
+ ,498
+ ,499
+ ,506
+ ,510
+ ,509
+ ,502
+ ,495
+ ,490
+ ,490
+ ,553
+ ,570
+ ,573
+ ,572
+ ,575
+ ,580
+ ,580
+ ,574
+ ,563
+ ,556
+ ,546
+ ,545
+ ,605
+ ,628
+ ,631
+ ,626
+ ,614
+ ,606
+ ,602
+ ,589
+ ,574
+ ,558
+ ,552
+ ,546
+ ,607
+ ,636
+ ,631
+ ,623
+ ,618
+ ,605
+ ,619
+ ,596
+ ,570
+ ,546
+ ,528
+ ,506
+ ,555
+ ,568
+ ,564
+ ,553
+ ,541
+ ,542
+ ,540
+ ,521
+ ,505
+ ,491
+ ,482
+ ,478
+ ,523
+ ,531
+ ,532
+ ,540
+ ,525
+ ,533
+ ,531
+ ,508
+ ,495
+ ,482
+ ,470
+ ,466
+ ,515
+ ,518
+ ,516
+ ,511
+ ,500
+ ,498
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+ ,476
+ ,458
+ ,443
+ ,430
+ ,424
+ ,476
+ ,481
+ ,470
+ ,460
+ ,451
+ ,450
+ ,444
+ ,429
+ ,421
+ ,400
+ ,389
+ ,384
+ ,432
+ ,446
+ ,431
+ ,423
+ ,416
+ ,416
+ ,413
+ ,399
+ ,386
+ ,374
+ ,365
+ ,365
+ ,418
+ ,428
+ ,424
+ ,421
+ ,417
+ ,423
+ ,423
+ ,419
+ ,406
+ ,398
+ ,390
+ ,391
+ ,444
+ ,460
+ ,455
+ ,456
+ ,452
+ ,459
+ ,461
+ ,451
+ ,443
+ ,439
+ ,430
+ ,436
+ ,488
+ ,506
+ ,502
+ ,501
+ ,501
+ ,515
+ ,521
+ ,520
+ ,512
+ ,509
+ ,505
+ ,511
+ ,570
+ ,592
+ ,594
+ ,586
+ ,586
+ ,592
+ ,594
+ ,586
+ ,572
+ ,563
+ ,555
+ ,554
+ ,601
+ ,622
+ ,617
+ ,606
+ ,595
+ ,599
+ ,600
+ ,592
+ ,575
+ ,567
+ ,555
+ ,555
+ ,608
+ ,631
+ ,629
+ ,624
+ ,610
+ ,616
+ ,621
+ ,604
+ ,584
+ ,574
+ ,555
+ ,545
+ ,599
+ ,620
+ ,608
+ ,590
+ ,579
+ ,580
+ ,579
+ ,572
+ ,560
+ ,551
+ ,537
+ ,541
+ ,588
+ ,607
+ ,599
+ ,578
+ ,563
+ ,566
+ ,561
+ ,554
+ ,540
+ ,526
+ ,512
+ ,505
+ ,554
+ ,584
+ ,569
+ ,540
+ ,522
+ ,526
+ ,527
+ ,516
+ ,503
+ ,489
+ ,479
+ ,475
+ ,524
+ ,552
+ ,532
+ ,511
+ ,492
+ ,492
+ ,493
+ ,481
+ ,462
+ ,457
+ ,442
+ ,439
+ ,488
+ ,521
+ ,501
+ ,485
+ ,464
+ ,460
+ ,467
+ ,460
+ ,448
+ ,443
+ ,436
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+ ,484
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+ ,517
+ ,525
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+ ,512
+ ,519
+ ,517
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+ ,507
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+ ,580
+ ,578
+ ,565
+ ,547
+ ,555
+ ,562
+ ,561
+ ,555
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+ ,613
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+ ,567
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+ ,629
+ ,628
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+ ,626
+ ,620
+ ,588
+ ,566
+ ,557
+ ,561
+ ,549
+ ,532
+ ,526
+ ,511
+ ,499
+ ,555
+ ,565
+ ,542
+ ,527
+ ,510
+ ,514
+ ,517
+ ,508
+ ,493
+ ,490
+ ,469
+ ,478
+ ,528
+ ,534
+ ,518
+ ,506
+ ,502
+ ,516
+ ,528
+ ,533
+ ,536
+ ,537
+ ,524
+ ,536
+ ,587
+ ,597
+ ,581
+ ,564
+ ,558
+ ,575
+ ,580
+ ,575
+ ,563
+ ,552
+ ,537
+ ,545
+ ,601
+ ,604
+ ,586
+ ,564
+ ,549
+ ,551
+ ,556
+ ,548
+ ,540
+ ,531
+ ,521
+ ,519
+ ,572
+ ,581
+ ,563
+ ,548)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '2'
> par7 = '1'
> par6 = '1'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '1.0'
> par1 = '12'
> par1 <- as.numeric(par1) #cut off periods
> par2 <- as.numeric(par2) #lambda
> par3 <- as.numeric(par3) #degree of non-seasonal differencing
> par4 <- as.numeric(par4) #degree of seasonal differencing
> par5 <- as.numeric(par5) #seasonal period
> par6 <- as.numeric(par6) #p
> par7 <- as.numeric(par7) #q
> par8 <- as.numeric(par8) #P
> par9 <- as.numeric(par9) #Q
> if (par10 == 'TRUE') par10 <- TRUE
> if (par10 == 'FALSE') par10 <- FALSE
> if (par2 == 0) x <- log(x)
> if (par2 != 0) x <- x^par2
> lx <- length(x)
> first <- lx - 2*par1
> nx <- lx - par1
> nx1 <- nx + 1
> fx <- lx - nx
> if (fx < 1) {
+ fx <- par5
+ nx1 <- lx + fx - 1
+ first <- lx - 2*fx
+ }
> first <- 1
> if (fx < 3) fx <- round(lx/10,0)
> (arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))

Call:
arima(x = x[1:nx], order = c(par6, par3, par7), seasonal = list(order = c(par8, 
    par4, par9), period = par5), include.mean = par10, method = "ML")

Coefficients:
         ar1      ma1     sar1     sar2
      0.8394  -0.7196  -0.3896  -0.2076
s.e.  0.0617   0.0777   0.0369   0.0362

sigma^2 estimated as 83.16:  log likelihood = -2617.95,  aic = 5245.91
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 735 
End = 746 
Frequency = 1 
 [1] 554.8835 567.6342 572.6495 567.9312 557.4440 549.7897 533.4895 542.0736
 [9] 595.6233 600.2724 582.8437 563.6549

$se
Time Series:
Start = 735 
End = 746 
Frequency = 1 
 [1]  9.119443 13.691029 17.643419 21.280436 24.702474 27.954678 31.061775
 [8] 34.039609 36.899693 39.651216 42.301990 44.858923

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 735 
End = 746 
Frequency = 1 
 [1] 537.0094 540.7998 538.0684 526.2215 509.0271 494.9985 472.6085 475.3560
 [9] 523.2999 522.5560 499.9318 475.7315
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 735 
End = 746 
Frequency = 1 
 [1] 572.7576 594.4686 607.2306 609.6408 605.8608 604.5808 594.3706 608.7913
 [9] 667.9467 677.9888 665.7556 651.5784
> if (par2 == 0) {
+ x <- exp(x)
+ forecast$pred <- exp(forecast$pred)
+ lb <- exp(lb)
+ ub <- exp(ub)
+ }
> if (par2 != 0) {
+ x <- x^(1/par2)
+ forecast$pred <- forecast$pred^(1/par2)
+ lb <- lb^(1/par2)
+ ub <- ub^(1/par2)
+ }
> if (par2 < 0) {
+ olb <- lb
+ lb <- ub
+ ub <- olb
+ }
> (actandfor <- c(x[1:nx], forecast$pred))
  [1] 411.0000 410.0000 415.0000 414.0000 411.0000 408.0000 410.0000 411.0000
  [9] 416.0000 479.0000 498.0000 502.0000 498.0000 499.0000 506.0000 510.0000
 [17] 509.0000 502.0000 495.0000 490.0000 490.0000 553.0000 570.0000 573.0000
 [25] 572.0000 575.0000 580.0000 580.0000 574.0000 563.0000 556.0000 546.0000
 [33] 545.0000 605.0000 628.0000 631.0000 626.0000 614.0000 606.0000 602.0000
 [41] 589.0000 574.0000 558.0000 552.0000 546.0000 607.0000 636.0000 631.0000
 [49] 623.0000 618.0000 605.0000 619.0000 596.0000 570.0000 546.0000 528.0000
 [57] 506.0000 555.0000 568.0000 564.0000 553.0000 541.0000 542.0000 540.0000
 [65] 521.0000 505.0000 491.0000 482.0000 478.0000 523.0000 531.0000 532.0000
 [73] 540.0000 525.0000 533.0000 531.0000 508.0000 495.0000 482.0000 470.0000
 [81] 466.0000 515.0000 518.0000 516.0000 511.0000 500.0000 498.0000 494.0000
 [89] 476.0000 458.0000 443.0000 430.0000 424.0000 476.0000 481.0000 470.0000
 [97] 460.0000 451.0000 450.0000 444.0000 429.0000 421.0000 400.0000 389.0000
[105] 384.0000 432.0000 446.0000 431.0000 423.0000 416.0000 416.0000 413.0000
[113] 399.0000 386.0000 374.0000 365.0000 365.0000 418.0000 428.0000 424.0000
[121] 421.0000 417.0000 423.0000 423.0000 419.0000 406.0000 398.0000 390.0000
[129] 391.0000 444.0000 460.0000 455.0000 456.0000 452.0000 459.0000 461.0000
[137] 451.0000 443.0000 439.0000 430.0000 436.0000 488.0000 506.0000 502.0000
[145] 501.0000 501.0000 515.0000 521.0000 520.0000 512.0000 509.0000 505.0000
[153] 511.0000 570.0000 592.0000 594.0000 586.0000 586.0000 592.0000 594.0000
[161] 586.0000 572.0000 563.0000 555.0000 554.0000 601.0000 622.0000 617.0000
[169] 606.0000 595.0000 599.0000 600.0000 592.0000 575.0000 567.0000 555.0000
[177] 555.0000 608.0000 631.0000 629.0000 624.0000 610.0000 616.0000 621.0000
[185] 604.0000 584.0000 574.0000 555.0000 545.0000 599.0000 620.0000 608.0000
[193] 590.0000 579.0000 580.0000 579.0000 572.0000 560.0000 551.0000 537.0000
[201] 541.0000 588.0000 607.0000 599.0000 578.0000 563.0000 566.0000 561.0000
[209] 554.0000 540.0000 526.0000 512.0000 505.0000 554.0000 584.0000 569.0000
[217] 540.0000 522.0000 526.0000 527.0000 516.0000 503.0000 489.0000 479.0000
[225] 475.0000 524.0000 552.0000 532.0000 511.0000 492.0000 492.0000 493.0000
[233] 481.0000 462.0000 457.0000 442.0000 439.0000 488.0000 521.0000 501.0000
[241] 485.0000 464.0000 460.0000 467.0000 460.0000 448.0000 443.0000 436.0000
[249] 431.0000 484.0000 510.0000 513.0000 503.0000 471.0000 471.0000 476.0000
[257] 475.0000 470.0000 461.0000 455.0000 456.0000 517.0000 525.0000 523.0000
[265] 519.0000 509.0000 512.0000 519.0000 517.0000 510.0000 509.0000 501.0000
[273] 507.0000 569.0000 580.0000 578.0000 565.0000 547.0000 555.0000 562.0000
[281] 561.0000 555.0000 544.0000 537.0000 543.0000 594.0000 611.0000 613.0000
[289] 611.0000 594.0000 595.0000 591.0000 589.0000 584.0000 573.0000 567.0000
[297] 569.0000 621.0000 629.0000 628.0000 612.0000 595.0000 597.0000 593.0000
[305] 590.0000 580.0000 574.0000 573.0000 573.0000 620.0000 626.0000 620.0000
[313] 588.0000 566.0000 557.0000 561.0000 549.0000 532.0000 526.0000 511.0000
[321] 499.0000 555.0000 565.0000 542.0000 527.0000 510.0000 514.0000 517.0000
[329] 508.0000 493.0000 490.0000 469.0000 478.0000 528.0000 534.0000 518.0000
[337] 506.0000 502.0000 516.0000 528.0000 533.0000 536.0000 537.0000 524.0000
[345] 536.0000 587.0000 597.0000 581.0000 564.0000 558.0000 575.0000 580.0000
[353] 575.0000 563.0000 552.0000 537.0000 545.0000 601.0000 604.0000 586.0000
[361] 564.0000 549.0000 551.0000 556.0000 548.0000 540.0000 531.0000 521.0000
[369] 519.0000 572.0000 581.0000 563.0000 548.0000 411.0000 410.0000 415.0000
[377] 414.0000 411.0000 408.0000 410.0000 411.0000 416.0000 479.0000 498.0000
[385] 502.0000 498.0000 499.0000 506.0000 510.0000 509.0000 502.0000 495.0000
[393] 490.0000 490.0000 553.0000 570.0000 573.0000 572.0000 575.0000 580.0000
[401] 580.0000 574.0000 563.0000 556.0000 546.0000 545.0000 605.0000 628.0000
[409] 631.0000 626.0000 614.0000 606.0000 602.0000 589.0000 574.0000 558.0000
[417] 552.0000 546.0000 607.0000 636.0000 631.0000 623.0000 618.0000 605.0000
[425] 619.0000 596.0000 570.0000 546.0000 528.0000 506.0000 555.0000 568.0000
[433] 564.0000 553.0000 541.0000 542.0000 540.0000 521.0000 505.0000 491.0000
[441] 482.0000 478.0000 523.0000 531.0000 532.0000 540.0000 525.0000 533.0000
[449] 531.0000 508.0000 495.0000 482.0000 470.0000 466.0000 515.0000 518.0000
[457] 516.0000 511.0000 500.0000 498.0000 494.0000 476.0000 458.0000 443.0000
[465] 430.0000 424.0000 476.0000 481.0000 470.0000 460.0000 451.0000 450.0000
[473] 444.0000 429.0000 421.0000 400.0000 389.0000 384.0000 432.0000 446.0000
[481] 431.0000 423.0000 416.0000 416.0000 413.0000 399.0000 386.0000 374.0000
[489] 365.0000 365.0000 418.0000 428.0000 424.0000 421.0000 417.0000 423.0000
[497] 423.0000 419.0000 406.0000 398.0000 390.0000 391.0000 444.0000 460.0000
[505] 455.0000 456.0000 452.0000 459.0000 461.0000 451.0000 443.0000 439.0000
[513] 430.0000 436.0000 488.0000 506.0000 502.0000 501.0000 501.0000 515.0000
[521] 521.0000 520.0000 512.0000 509.0000 505.0000 511.0000 570.0000 592.0000
[529] 594.0000 586.0000 586.0000 592.0000 594.0000 586.0000 572.0000 563.0000
[537] 555.0000 554.0000 601.0000 622.0000 617.0000 606.0000 595.0000 599.0000
[545] 600.0000 592.0000 575.0000 567.0000 555.0000 555.0000 608.0000 631.0000
[553] 629.0000 624.0000 610.0000 616.0000 621.0000 604.0000 584.0000 574.0000
[561] 555.0000 545.0000 599.0000 620.0000 608.0000 590.0000 579.0000 580.0000
[569] 579.0000 572.0000 560.0000 551.0000 537.0000 541.0000 588.0000 607.0000
[577] 599.0000 578.0000 563.0000 566.0000 561.0000 554.0000 540.0000 526.0000
[585] 512.0000 505.0000 554.0000 584.0000 569.0000 540.0000 522.0000 526.0000
[593] 527.0000 516.0000 503.0000 489.0000 479.0000 475.0000 524.0000 552.0000
[601] 532.0000 511.0000 492.0000 492.0000 493.0000 481.0000 462.0000 457.0000
[609] 442.0000 439.0000 488.0000 521.0000 501.0000 485.0000 464.0000 460.0000
[617] 467.0000 460.0000 448.0000 443.0000 436.0000 431.0000 484.0000 510.0000
[625] 513.0000 503.0000 471.0000 471.0000 476.0000 475.0000 470.0000 461.0000
[633] 455.0000 456.0000 517.0000 525.0000 523.0000 519.0000 509.0000 512.0000
[641] 519.0000 517.0000 510.0000 509.0000 501.0000 507.0000 569.0000 580.0000
[649] 578.0000 565.0000 547.0000 555.0000 562.0000 561.0000 555.0000 544.0000
[657] 537.0000 543.0000 594.0000 611.0000 613.0000 611.0000 594.0000 595.0000
[665] 591.0000 589.0000 584.0000 573.0000 567.0000 569.0000 621.0000 629.0000
[673] 628.0000 612.0000 595.0000 597.0000 593.0000 590.0000 580.0000 574.0000
[681] 573.0000 573.0000 620.0000 626.0000 620.0000 588.0000 566.0000 557.0000
[689] 561.0000 549.0000 532.0000 526.0000 511.0000 499.0000 555.0000 565.0000
[697] 542.0000 527.0000 510.0000 514.0000 517.0000 508.0000 493.0000 490.0000
[705] 469.0000 478.0000 528.0000 534.0000 518.0000 506.0000 502.0000 516.0000
[713] 528.0000 533.0000 536.0000 537.0000 524.0000 536.0000 587.0000 597.0000
[721] 581.0000 564.0000 558.0000 575.0000 580.0000 575.0000 563.0000 552.0000
[729] 537.0000 545.0000 601.0000 604.0000 586.0000 564.0000 554.8835 567.6342
[737] 572.6495 567.9312 557.4440 549.7897 533.4895 542.0736 595.6233 600.2724
[745] 582.8437 563.6549
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 735 
End = 746 
Frequency = 1 
 [1] 0.01643488 0.02411946 0.03081015 0.03747010 0.04431382 0.05084613
 [7] 0.05822378 0.06279517 0.06195139 0.06605537 0.07257862 0.07958579
> postscript(file="/var/www/rcomp/tmp/1c2vv1323208769.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
> box()
> par(opar)
> dev.off()
null device 
          1 
> prob.dec <- array(NA, dim=fx)
> prob.sdec <- array(NA, dim=fx)
> prob.ldec <- array(NA, dim=fx)
> prob.pval <- array(NA, dim=fx)
> perf.pe <- array(0, dim=fx)
> perf.mape <- array(0, dim=fx)
> perf.mape1 <- array(0, dim=fx)
> perf.se <- array(0, dim=fx)
> perf.mse <- array(0, dim=fx)
> perf.mse1 <- array(0, dim=fx)
> perf.rmse <- array(0, dim=fx)
> for (i in 1:fx) {
+ locSD <- (ub[i] - forecast$pred[i]) / 1.96
+ perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
+ perf.se[i] = (x[nx+i] - forecast$pred[i])^2
+ prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
+ prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
+ prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
+ prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
+ }
> perf.mape[1] = abs(perf.pe[1])
> perf.mse[1] = abs(perf.se[1])
> for (i in 2:fx) {
+ perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
+ perf.mape1[i] = perf.mape[i] / i
+ perf.mse[i] = perf.mse[i-1] + perf.se[i]
+ perf.mse1[i] = perf.mse[i] / i
+ }
> perf.rmse = sqrt(perf.mse1)
> postscript(file="/var/www/rcomp/tmp/2obk11323208769.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
> dum <- forecast$pred
> dum[1:par1] <- x[(nx+1):lx]
> lines(dum, lty=1)
> lines(ub,lty=3)
> lines(lb,lty=3)
> dev.off()
null device 
          1 
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'time',1,header=TRUE)
> a<-table.element(a,'Y[t]',1,header=TRUE)
> a<-table.element(a,'F[t]',1,header=TRUE)
> a<-table.element(a,'95% LB',1,header=TRUE)
> a<-table.element(a,'95% UB',1,header=TRUE)
> a<-table.element(a,'p-value<br />(H0: Y[t] = F[t])',1,header=TRUE)
> a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
> a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
> mylab <- paste('P(F[t]>Y[',nx,sep='')
> mylab <- paste(mylab,'])',sep='')
> a<-table.element(a,mylab,1,header=TRUE)
> a<-table.row.end(a)
> for (i in (nx-par5):nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.row.end(a)
+ }
> for (i in 1:fx) {
+ a<-table.row.start(a)
+ a<-table.element(a,nx+i,header=TRUE)
+ a<-table.element(a,round(x[nx+i],4))
+ a<-table.element(a,round(forecast$pred[i],4))
+ a<-table.element(a,round(lb[i],4))
+ a<-table.element(a,round(ub[i],4))
+ a<-table.element(a,round((1-prob.pval[i]),4))
+ a<-table.element(a,round((1-prob.dec[i]),4))
+ a<-table.element(a,round((1-prob.sdec[i]),4))
+ a<-table.element(a,round((1-prob.ldec[i]),4))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/35ajo1323208769.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'time',1,header=TRUE)
> a<-table.element(a,'% S.E.',1,header=TRUE)
> a<-table.element(a,'PE',1,header=TRUE)
> a<-table.element(a,'MAPE',1,header=TRUE)
> a<-table.element(a,'Sq.E',1,header=TRUE)
> a<-table.element(a,'MSE',1,header=TRUE)
> a<-table.element(a,'RMSE',1,header=TRUE)
> a<-table.row.end(a)
> for (i in 1:fx) {
+ a<-table.row.start(a)
+ a<-table.element(a,nx+i,header=TRUE)
+ a<-table.element(a,round(perc.se[i],4))
+ a<-table.element(a,round(perf.pe[i],4))
+ a<-table.element(a,round(perf.mape1[i],4))
+ a<-table.element(a,round(perf.se[i],4))
+ a<-table.element(a,round(perf.mse1[i],4))
+ a<-table.element(a,round(perf.rmse[i],4))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/4xwym1323208769.tab") 
> 
> try(system("convert tmp/1c2vv1323208769.ps tmp/1c2vv1323208769.png",intern=TRUE))
character(0)
> try(system("convert tmp/2obk11323208769.ps tmp/2obk11323208769.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  9.056   0.440  11.832