R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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(1.2998
+ ,1.3146
+ ,1.3225
+ ,1.3321
+ ,1.3339
+ ,1.3496
+ ,1.3647
+ ,1.3674
+ ,1.3647
+ ,1.3481
+ ,1.3612
+ ,1.3626
+ ,1.3711
+ ,1.37
+ ,1.377
+ ,1.3945
+ ,1.3917
+ ,1.4084
+ ,1.4244
+ ,1.4014
+ ,1.4018
+ ,1.3926
+ ,1.3857
+ ,1.3857
+ ,1.3803
+ ,1.3912
+ ,1.4031
+ ,1.3934
+ ,1.4016
+ ,1.3861
+ ,1.3859
+ ,1.3896
+ ,1.4089
+ ,1.4101
+ ,1.3958
+ ,1.3833
+ ,1.3936
+ ,1.3874
+ ,1.397
+ ,1.3856
+ ,1.378
+ ,1.3705
+ ,1.3726
+ ,1.3648
+ ,1.3611
+ ,1.346
+ ,1.3477
+ ,1.3412
+ ,1.3323
+ ,1.3364
+ ,1.312
+ ,1.3074
+ ,1.306
+ ,1.3078
+ ,1.2989
+ ,1.285
+ ,1.2801
+ ,1.2725
+ ,1.2715
+ ,1.2697
+ ,1.2744
+ ,1.2874
+ ,1.2834
+ ,1.2818
+ ,1.28
+ ,1.268
+ ,1.27
+ ,1.2713
+ ,1.2693
+ ,1.2613
+ ,1.2611
+ ,1.2704
+ ,1.2711
+ ,1.2836
+ ,1.288
+ ,1.286
+ ,1.282
+ ,1.2799
+ ,1.279
+ ,1.3016
+ ,1.3133
+ ,1.3253
+ ,1.3176
+ ,1.3184
+ ,1.3206
+ ,1.3221
+ ,1.3073
+ ,1.3028
+ ,1.3069
+ ,1.2992
+ ,1.3033
+ ,1.2931
+ ,1.2897
+ ,1.285
+ ,1.2817
+ ,1.2844
+ ,1.2957
+ ,1.3
+ ,1.2828
+ ,1.2703
+ ,1.2569
+ ,1.2572
+ ,1.2637
+ ,1.266
+ ,1.2567
+ ,1.2579
+ ,1.2531
+ ,1.2548
+ ,1.2328
+ ,1.2271
+ ,1.2198
+ ,1.2339
+ ,1.2294
+ ,1.2262
+ ,1.2271
+ ,1.2258
+ ,1.2391
+ ,1.2372
+ ,1.2363
+ ,1.2277
+ ,1.2258
+ ,1.2249
+ ,1.2127
+ ,1.2045
+ ,1.201
+ ,1.1942
+ ,1.1959
+ ,1.206
+ ,1.2268
+ ,1.2218
+ ,1.2155
+ ,1.2307
+ ,1.2384
+ ,1.2255
+ ,1.2309
+ ,1.2223
+ ,1.236
+ ,1.2497
+ ,1.2334
+ ,1.227
+ ,1.2428
+ ,1.2349
+ ,1.2492
+ ,1.2587
+ ,1.2686
+ ,1.2698
+ ,1.2969
+ ,1.2746
+ ,1.2727
+ ,1.2924
+ ,1.3089
+ ,1.3238
+ ,1.3315
+ ,1.3256
+ ,1.3245
+ ,1.329
+ ,1.3321
+ ,1.3311
+ ,1.3339
+ ,1.3373
+ ,1.3486
+ ,1.3432
+ ,1.3535
+ ,1.3544
+ ,1.3615
+ ,1.3583
+ ,1.3585
+ ,1.3384
+ ,1.3296
+ ,1.334
+ ,1.3396
+ ,1.3468
+ ,1.3479
+ ,1.3482
+ ,1.3471
+ ,1.3353
+ ,1.3356
+ ,1.3338
+ ,1.3519
+ ,1.3471
+ ,1.3548
+ ,1.366
+ ,1.3756
+ ,1.3723
+ ,1.3705
+ ,1.3765
+ ,1.3657
+ ,1.361
+ ,1.3557
+ ,1.3662
+ ,1.3582
+ ,1.3668
+ ,1.3641
+ ,1.3548
+ ,1.3525
+ ,1.357
+ ,1.3489
+ ,1.3547
+ ,1.3577
+ ,1.3626
+ ,1.3519
+ ,1.3567
+ ,1.3726
+ ,1.3649
+ ,1.3607
+ ,1.3572
+ ,1.3718
+ ,1.374
+ ,1.376
+ ,1.3675
+ ,1.3691
+ ,1.3847
+ ,1.3984
+ ,1.3937
+ ,1.3913
+ ,1.3966
+ ,1.3999
+ ,1.4072
+ ,1.4085
+ ,1.4151
+ ,1.4135
+ ,1.4064
+ ,1.4132
+ ,1.4279
+ ,1.4369
+ ,1.4374
+ ,1.4486
+ ,1.4563
+ ,1.4481
+ ,1.4528
+ ,1.4273
+ ,1.4304
+ ,1.435
+ ,1.4442
+ ,1.4389
+ ,1.4406
+ ,1.4338
+ ,1.4433
+ ,1.4405
+ ,1.4398
+ ,1.4276
+ ,1.4279
+ ,1.4368
+ ,1.4337
+ ,1.4343
+ ,1.456
+ ,1.4541
+ ,1.4647
+ ,1.4757
+ ,1.473
+ ,1.4768
+ ,1.4774
+ ,1.4787
+ ,1.5068
+ ,1.512
+ ,1.509
+ ,1.5074
+ ,1.5023
+ ,1.4918
+ ,1.5071
+ ,1.5083
+ ,1.4969
+ ,1.4968
+ ,1.4815
+ ,1.4863
+ ,1.4957
+ ,1.4875
+ ,1.4965
+ ,1.4868
+ ,1.4922
+ ,1.5037
+ ,1.4966
+ ,1.4984
+ ,1.4862
+ ,1.4867
+ ,1.4761
+ ,1.4658
+ ,1.4772
+ ,1.48
+ ,1.4788
+ ,1.4785
+ ,1.4874
+ ,1.5019
+ ,1.502
+ ,1.5
+ ,1.4921
+ ,1.4971
+ ,1.4918
+ ,1.4869
+ ,1.4864
+ ,1.4881
+ ,1.4864
+ ,1.4765
+ ,1.475
+ ,1.4763
+ ,1.4694
+ ,1.4722
+ ,1.4616
+ ,1.4537
+ ,1.4539
+ ,1.4643
+ ,1.4549
+ ,1.465
+ ,1.467
+ ,1.4768
+ ,1.4783
+ ,1.478
+ ,1.4658
+ ,1.4705
+ ,1.4712
+ ,1.4671
+ ,1.4611
+ ,1.4561
+ ,1.4594
+ ,1.4545
+ ,1.4522
+ ,1.4473
+ ,1.433
+ ,1.4262
+ ,1.4335
+ ,1.422
+ ,1.4314
+ ,1.4272
+ ,1.4364
+ ,1.4268
+ ,1.427
+ ,1.4324
+ ,1.4323
+ ,1.433
+ ,1.4243
+ ,1.4112
+ ,1.4101
+ ,1.4072
+ ,1.4294
+ ,1.4293
+ ,1.417
+ ,1.4166
+ ,1.4202
+ ,1.4357
+ ,1.437
+ ,1.441
+ ,1.4384
+ ,1.4303
+ ,1.4138
+ ,1.4053
+ ,1.4104
+ ,1.4229
+ ,1.4269
+ ,1.4227
+ ,1.4229
+ ,1.4191
+ ,1.4223
+ ,1.4217
+ ,1.409
+ ,1.413
+ ,1.4089
+ ,1.3991
+ ,1.3975
+ ,1.3901
+ ,1.399
+ ,1.3901
+ ,1.4019
+ ,1.3897
+ ,1.4009
+ ,1.4049
+ ,1.4096
+ ,1.4134
+ ,1.4058
+ ,1.4096
+ ,1.394
+ ,1.4029
+ ,1.3978
+ ,1.3858
+ ,1.3932
+ ,1.392
+ ,1.384
+ ,1.389
+ ,1.385
+ ,1.4004
+ ,1.3969
+ ,1.4102
+ ,1.3959
+ ,1.3866
+ ,1.4177
+ ,1.4095
+ ,1.4207
+ ,1.4238
+ ,1.422
+ ,1.4098
+ ,1.3856
+ ,1.3901
+ ,1.3908
+ ,1.401
+ ,1.3972
+ ,1.3771
+ ,1.369
+ ,1.3612
+ ,1.3494
+ ,1.3518
+ ,1.3563
+ ,1.3623
+ ,1.3683
+ ,1.3574
+ ,1.3425
+ ,1.3363
+ ,1.3322
+ ,1.3403
+ ,1.3223
+ ,1.3275
+ ,1.3266
+ ,1.2992
+ ,1.3125
+ ,1.3232
+ ,1.305
+ ,1.2947
+ ,1.2932
+ ,1.2966
+ ,1.3058
+ ,1.3196
+ ,1.3173
+ ,1.3276
+ ,1.3273
+ ,1.3231
+ ,1.3255
+ ,1.3496
+ ,1.3425
+ ,1.3392
+ ,1.3246
+ ,1.3308
+ ,1.3193
+ ,1.3295
+ ,1.3607
+ ,1.3494
+ ,1.3507
+ ,1.3558
+ ,1.3549
+ ,1.3671
+ ,1.313
+ ,1.2942
+ ,1.3042
+ ,1.2905
+ ,1.2782
+ ,1.2786
+ ,1.2783
+ ,1.2565
+ ,1.2658
+ ,1.2555
+ ,1.2555
+ ,1.2615
+ ,1.2596
+ ,1.2644
+ ,1.2782
+ ,1.2795
+ ,1.2763
+ ,1.2798
+ ,1.2591
+ ,1.2705
+ ,1.2596
+ ,1.2634
+ ,1.2765
+ ,1.2823
+ ,1.2833
+ ,1.2938
+ ,1.2967
+ ,1.3008
+ ,1.2796
+ ,1.2829
+ ,1.2818
+ ,1.2849
+ ,1.276
+ ,1.2816
+ ,1.3111
+ ,1.326
+ ,1.3174
+ ,1.299
+ ,1.2795
+ ,1.2984
+ ,1.291
+ ,1.293
+ ,1.3182
+ ,1.327
+ ,1.3085
+ ,1.3173
+ ,1.3262
+ ,1.3394
+ ,1.3684
+ ,1.3617
+ ,1.3595
+ ,1.3332
+ ,1.3582
+ ,1.3866)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '1'
> par7 = '0'
> par6 = '0'
> par5 = '1'
> par4 = '0'
> par3 = '1'
> par2 = '1.9'
> par1 = '15'
> 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 <- 5 #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:
        sar1
      0.0781
s.e.  0.0467

sigma^2 estimated as 0.0006136:  log likelihood = 1082.58,  aic = -2161.15
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 477 
End = 491 
Frequency = 1 
 [1] 1.602795 1.605632 1.603991 1.600513 1.596874 1.597306 1.597528 1.597400
 [9] 1.597128 1.596844 1.596877 1.596895 1.596885 1.596863 1.596841

$se
Time Series:
Start = 477 
End = 491 
Frequency = 1 
 [1] 0.02477106 0.03503156 0.04290473 0.04954211 0.05538976 0.06149197
 [7] 0.06704103 0.07216465 0.07694787 0.08145067 0.08576449 0.08987149
[13] 0.09379884 0.09756823 0.10119732

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 477 
End = 491 
Frequency = 1 
 [1] 1.554244 1.536970 1.519898 1.503410 1.488310 1.476782 1.466127 1.455957
 [9] 1.446310 1.437200 1.428779 1.420747 1.413039 1.405630 1.398494
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 477 
End = 491 
Frequency = 1 
 [1] 1.651346 1.674294 1.688084 1.697615 1.705438 1.717830 1.728928 1.738842
 [9] 1.747946 1.756487 1.764976 1.773043 1.780730 1.788097 1.795188
> 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] 1.299800 1.314600 1.322500 1.332100 1.333900 1.349600 1.364700 1.367400
  [9] 1.364700 1.348100 1.361200 1.362600 1.371100 1.370000 1.377000 1.394500
 [17] 1.391700 1.408400 1.424400 1.401400 1.401800 1.392600 1.385700 1.385700
 [25] 1.380300 1.391200 1.403100 1.393400 1.401600 1.386100 1.385900 1.389600
 [33] 1.408900 1.410100 1.395800 1.383300 1.393600 1.387400 1.397000 1.385600
 [41] 1.378000 1.370500 1.372600 1.364800 1.361100 1.346000 1.347700 1.341200
 [49] 1.332300 1.336400 1.312000 1.307400 1.306000 1.307800 1.298900 1.285000
 [57] 1.280100 1.272500 1.271500 1.269700 1.274400 1.287400 1.283400 1.281800
 [65] 1.280000 1.268000 1.270000 1.271300 1.269300 1.261300 1.261100 1.270400
 [73] 1.271100 1.283600 1.288000 1.286000 1.282000 1.279900 1.279000 1.301600
 [81] 1.313300 1.325300 1.317600 1.318400 1.320600 1.322100 1.307300 1.302800
 [89] 1.306900 1.299200 1.303300 1.293100 1.289700 1.285000 1.281700 1.284400
 [97] 1.295700 1.300000 1.282800 1.270300 1.256900 1.257200 1.263700 1.266000
[105] 1.256700 1.257900 1.253100 1.254800 1.232800 1.227100 1.219800 1.233900
[113] 1.229400 1.226200 1.227100 1.225800 1.239100 1.237200 1.236300 1.227700
[121] 1.225800 1.224900 1.212700 1.204500 1.201000 1.194200 1.195900 1.206000
[129] 1.226800 1.221800 1.215500 1.230700 1.238400 1.225500 1.230900 1.222300
[137] 1.236000 1.249700 1.233400 1.227000 1.242800 1.234900 1.249200 1.258700
[145] 1.268600 1.269800 1.296900 1.274600 1.272700 1.292400 1.308900 1.323800
[153] 1.331500 1.325600 1.324500 1.329000 1.332100 1.331100 1.333900 1.337300
[161] 1.348600 1.343200 1.353500 1.354400 1.361500 1.358300 1.358500 1.338400
[169] 1.329600 1.334000 1.339600 1.346800 1.347900 1.348200 1.347100 1.335300
[177] 1.335600 1.333800 1.351900 1.347100 1.354800 1.366000 1.375600 1.372300
[185] 1.370500 1.376500 1.365700 1.361000 1.355700 1.366200 1.358200 1.366800
[193] 1.364100 1.354800 1.352500 1.357000 1.348900 1.354700 1.357700 1.362600
[201] 1.351900 1.356700 1.372600 1.364900 1.360700 1.357200 1.371800 1.374000
[209] 1.376000 1.367500 1.369100 1.384700 1.398400 1.393700 1.391300 1.396600
[217] 1.399900 1.407200 1.408500 1.415100 1.413500 1.406400 1.413200 1.427900
[225] 1.436900 1.437400 1.448600 1.456300 1.448100 1.452800 1.427300 1.430400
[233] 1.435000 1.444200 1.438900 1.440600 1.433800 1.443300 1.440500 1.439800
[241] 1.427600 1.427900 1.436800 1.433700 1.434300 1.456000 1.454100 1.464700
[249] 1.475700 1.473000 1.476800 1.477400 1.478700 1.506800 1.512000 1.509000
[257] 1.507400 1.502300 1.491800 1.507100 1.508300 1.496900 1.496800 1.481500
[265] 1.486300 1.495700 1.487500 1.496500 1.486800 1.492200 1.503700 1.496600
[273] 1.498400 1.486200 1.486700 1.476100 1.465800 1.477200 1.480000 1.478800
[281] 1.478500 1.487400 1.501900 1.502000 1.500000 1.492100 1.497100 1.491800
[289] 1.486900 1.486400 1.488100 1.486400 1.476500 1.475000 1.476300 1.469400
[297] 1.472200 1.461600 1.453700 1.453900 1.464300 1.454900 1.465000 1.467000
[305] 1.476800 1.478300 1.478000 1.465800 1.470500 1.471200 1.467100 1.461100
[313] 1.456100 1.459400 1.454500 1.452200 1.447300 1.433000 1.426200 1.433500
[321] 1.422000 1.431400 1.427200 1.436400 1.426800 1.427000 1.432400 1.432300
[329] 1.433000 1.424300 1.411200 1.410100 1.407200 1.429400 1.429300 1.417000
[337] 1.416600 1.420200 1.435700 1.437000 1.441000 1.438400 1.430300 1.413800
[345] 1.405300 1.410400 1.422900 1.426900 1.422700 1.422900 1.419100 1.422300
[353] 1.421700 1.409000 1.413000 1.408900 1.399100 1.397500 1.390100 1.399000
[361] 1.390100 1.401900 1.389700 1.400900 1.404900 1.409600 1.413400 1.405800
[369] 1.409600 1.394000 1.402900 1.397800 1.385800 1.393200 1.392000 1.384000
[377] 1.389000 1.385000 1.400400 1.396900 1.410200 1.395900 1.386600 1.417700
[385] 1.409500 1.420700 1.423800 1.422000 1.409800 1.385600 1.390100 1.390800
[393] 1.401000 1.397200 1.377100 1.369000 1.361200 1.349400 1.351800 1.356300
[401] 1.362300 1.368300 1.357400 1.342500 1.336300 1.332200 1.340300 1.322300
[409] 1.327500 1.326600 1.299200 1.312500 1.323200 1.305000 1.294700 1.293200
[417] 1.296600 1.305800 1.319600 1.317300 1.327600 1.327300 1.323100 1.325500
[425] 1.349600 1.342500 1.339200 1.324600 1.330800 1.319300 1.329500 1.360700
[433] 1.349400 1.350700 1.355800 1.354900 1.367100 1.313000 1.294200 1.304200
[441] 1.290500 1.278200 1.278600 1.278300 1.256500 1.265800 1.255500 1.255500
[449] 1.261500 1.259600 1.264400 1.278200 1.279500 1.276300 1.279800 1.259100
[457] 1.270500 1.259600 1.263400 1.276500 1.282300 1.283300 1.293800 1.296700
[465] 1.300800 1.279600 1.282900 1.281800 1.284900 1.276000 1.281600 1.311100
[473] 1.326000 1.317400 1.299000 1.279500 1.281830 1.283024 1.282334 1.280869
[481] 1.279336 1.279518 1.279611 1.279557 1.279443 1.279323 1.279337 1.279345
[489] 1.279340 1.279331 1.279322
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 477 
End = 491 
Frequency = 1 
 [1] 0.008076658 0.011369196 0.013907829 0.016064159 0.017971454 0.019912468
 [7] 0.021673371 0.023298949 0.024815061 0.026239694 0.027596657 0.028885911
[13] 0.030116641 0.031295740 0.032428836
> postscript(file="/var/www/html/rcomp/tmp/19h241292884291.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$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/html/rcomp/tmp/2y1381292884292.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/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/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/html/rcomp/tmp/3xthn1292884292.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/html/rcomp/tmp/4jcyt1292884292.tab") 
> 
> try(system("convert tmp/19h241292884291.ps tmp/19h241292884291.png",intern=TRUE))
character(0)
> try(system("convert tmp/2y1381292884292.ps tmp/2y1381292884292.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.658   0.354   1.414