Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationSat, 10 Nov 2012 17:13:06 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/10/t1352585628wpad4hpyzv2rcld.htm/, Retrieved Sat, 10 Dec 2022 06:01:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=187430, Retrieved Sat, 10 Dec 2022 06:01:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [HPC Retail Sales] [2008-03-08 13:40:54] [1c0f2c85e8a48e42648374b3bcceca26]
-  MPD  [Multiple Regression] [] [2012-11-10 21:11:43] [391561951b5d7f721cfaa4f5575ab127]
- RMP       [Exponential Smoothing] [] [2012-11-10 22:13:06] [7338cd26db379c04f0557b08db763c32] [Current]
- R P         [Exponential Smoothing] [] [2012-11-10 22:22:04] [391561951b5d7f721cfaa4f5575ab127]
- R P         [Exponential Smoothing] [] [2012-11-10 22:23:12] [391561951b5d7f721cfaa4f5575ab127]
-               [Exponential Smoothing] [] [2012-12-18 08:54:52] [391561951b5d7f721cfaa4f5575ab127]
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Dataseries X:
617
614
647
580
614
636
388
356
639
753
611
639
630
586
695
552
619
681
421
307
754
690
644
643
608
651
691
627
634
731
475
337
803
722
590
724
627
696
825
677
656
785
412
352
839
729
696
641
695
638
762
635
721
854
418
367
824
687
601
676
740
691
683
594
729
731
386
331
706
715
657
653
642
643
718
654
632
731
392
344
792
852
649
629
685
617
715
715
629
916
531
357
917
828
708
858
775
785
1006
789
734
906
532
387
991
841
892
782
811
792
978
773
796
946
594
438
1023
868
791
760
779
852
1001
734
996
869
599
426
1138
1091
830
909




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187430&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187430&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187430&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.0770948683103005
betaFALSE
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.0770948683103005 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187430&T=1

[TABLE]
[ROW][C]Estimated Parameters of Exponential Smoothing[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]alpha[/C][C]0.0770948683103005[/C][/ROW]
[ROW][C]beta[/C][C]FALSE[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187430&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187430&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.0770948683103005
betaFALSE
gammaFALSE







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
2614617-3
3647616.76871539506930.2312846049309
4580619.099392300537-39.0993923005375
5614616.085029800115-2.08502980011474
6636615.92428470225220.0757152977482
7388617.472019329367-229.472019329367
8356599.780904218271-243.780904218271
9639580.98664751099758.013352489003
10753585.459179281376167.540820718624
11611598.37571679127812.6242832087222
12639599.34898424276639.6510157572338
13630602.4058740809427.5941259190603
14586604.533239584808-18.5332395848076
15695603.10442191965491.8955780803465
16552610.189099410057-58.1890994100568
17619605.70301845394313.2969815460566
18681606.72814749516174.2718525048389
19421612.454126183184-191.454126183184
20307597.693995537628-290.693995537628
21754575.282980233059178.717019766941
22690589.061145336801100.938854663199
23644596.84301304445347.1569869555474
24643600.47857474370142.5214252562989
25608603.7567584242024.24324157579827
26651604.08389057469746.9161094253033
27691607.70088185247283.2991181475279
28627614.1228163964212.8771836035801
29634615.11558117054618.8844188294545
30731616.571472953319114.428527046681
31475625.393325176924-150.393325176924
32337613.798771577661-276.798771577661
33803592.459006734428210.540993265572
34722608.690636884158113.309363115842
35590617.426207311897-27.4262073118974
36724615.311787470936108.688212529064
37627623.6910909027463.30890909725417
38696623.94619081384972.0538091861506
39825629.501169744311195.498830255689
40677644.57312631769132.4268736823086
41656647.0730718739448.92692812605628
42785647.761292222238137.238707777762
43412658.34169232544-246.34169232544
44352639.350011996274-287.350011996274
45839617.196800662458221.803199337542
46729634.29668910618994.7033108938111
47696641.59782838809754.4021716119033
48641645.791956644311-4.79195664431074
49695645.42252137786949.577478622131
50638649.244690563399-11.2446905633989
51762648.377782625224113.622217374776
52635657.137472510856-22.1374725108562
53721655.43078698290965.5692130170911
54854660.485836825672193.514163174328
55418675.404785751774-257.404785751774
56367655.5601976918-288.5601976918
57824633.313687251157190.686312748843
58687648.01462342110538.9853765788946
59601651.020195894483-50.0201958944828
60676647.16389547914228.8361045208578
61740649.3870111597690.6129888402402
62691656.37280760160134.6271923983991
63683659.04238643951123.9576135604891
64594660.889395501986-66.8893955019859
65729655.73256636440573.2674336355954
66731661.38110951197569.6188904880255
67386666.748368706058-280.748368706058
68331645.104110192333-314.104110192333
69706620.88829518133185.1117048186693
70715627.44997085599187.5500291440088
71657634.19962882341222.8003711765884
72653635.95742043669717.0425795633034
73642637.2713158637974.72868413620279
74643637.6358731445595.36412685544121
75718638.04941979807979.9505802019212
76654644.2131992500789.78680074992189
77632644.967711365072-12.9677113650724
78731643.96796736509687.0320326349038
79392650.677690459862-258.677690459862
80344630.734967979046-286.734967979046
81792608.629173382743183.370826617257
82852622.766123112752229.233876887248
83649640.4388786636348.56112133636623
84629641.098897185649-12.0988971856494
85685640.16613430042244.833865699578
86617643.622595272373-26.6225952723727
87715641.57012979577173.4298702042294
88715647.23119596920867.7688040307918
89629652.455822991509-23.4558229915086
90916650.647499406869265.352500593131
91531671.104815495905-140.104815495905
92357660.303453195609-303.303453195609
93917636.920313413434280.079686586566
94828658.513019967216169.486980032784
95708671.57959637315436.4204036268461
96858674.387422594574183.612577405426
97775688.5430100697686.4569899302403
98785695.20840032293789.7915996770635
991006702.130871875411303.869128124589
100789725.55762229174263.4423777082579
101734730.4487040464533.55129595354742
102906730.722490740322175.277509259678
103532744.235487234455-212.235487234455
104387727.873220295342-340.873220295342
105991701.593644266164289.406355733836
106841723.905389149628117.094610850372
107892732.932782752984159.067217247016
108782745.19604891912936.8039510808715
109811748.03344468100762.966555318993
110792752.88784297127839.112157028722
111978755.903189566739222.096810433261
112773773.025713919229-0.0257139192291334
113796773.02373150801222.9762684919875
114946774.795083901664171.204916098336
115594787.994104362342-193.994104362342
116438773.038154433552-335.038154433552
1171023747.208432038571275.791567961429
118868768.47054665164999.529453348351
119791776.14375675053614.8562432494637
120760777.28909686744-17.2890968674395
121779775.956196221243.04380377875975
122852776.19085787272675.8091421272738
1231001782.035353701745218.964646298255
124734798.916404272721-64.9164042727207
125996793.911682634137202.088317365863
126869809.49165484850859.5083451514915
127599814.079442881327-215.079442881327
128426797.497921556138-371.497921556138
1291138768.857338216217369.142661783783
1301091797.316343114152293.683656885848
131830819.95784596665410.0421540333464
132909820.73204450940688.2679554905937

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 614 & 617 & -3 \tabularnewline
3 & 647 & 616.768715395069 & 30.2312846049309 \tabularnewline
4 & 580 & 619.099392300537 & -39.0993923005375 \tabularnewline
5 & 614 & 616.085029800115 & -2.08502980011474 \tabularnewline
6 & 636 & 615.924284702252 & 20.0757152977482 \tabularnewline
7 & 388 & 617.472019329367 & -229.472019329367 \tabularnewline
8 & 356 & 599.780904218271 & -243.780904218271 \tabularnewline
9 & 639 & 580.986647510997 & 58.013352489003 \tabularnewline
10 & 753 & 585.459179281376 & 167.540820718624 \tabularnewline
11 & 611 & 598.375716791278 & 12.6242832087222 \tabularnewline
12 & 639 & 599.348984242766 & 39.6510157572338 \tabularnewline
13 & 630 & 602.40587408094 & 27.5941259190603 \tabularnewline
14 & 586 & 604.533239584808 & -18.5332395848076 \tabularnewline
15 & 695 & 603.104421919654 & 91.8955780803465 \tabularnewline
16 & 552 & 610.189099410057 & -58.1890994100568 \tabularnewline
17 & 619 & 605.703018453943 & 13.2969815460566 \tabularnewline
18 & 681 & 606.728147495161 & 74.2718525048389 \tabularnewline
19 & 421 & 612.454126183184 & -191.454126183184 \tabularnewline
20 & 307 & 597.693995537628 & -290.693995537628 \tabularnewline
21 & 754 & 575.282980233059 & 178.717019766941 \tabularnewline
22 & 690 & 589.061145336801 & 100.938854663199 \tabularnewline
23 & 644 & 596.843013044453 & 47.1569869555474 \tabularnewline
24 & 643 & 600.478574743701 & 42.5214252562989 \tabularnewline
25 & 608 & 603.756758424202 & 4.24324157579827 \tabularnewline
26 & 651 & 604.083890574697 & 46.9161094253033 \tabularnewline
27 & 691 & 607.700881852472 & 83.2991181475279 \tabularnewline
28 & 627 & 614.12281639642 & 12.8771836035801 \tabularnewline
29 & 634 & 615.115581170546 & 18.8844188294545 \tabularnewline
30 & 731 & 616.571472953319 & 114.428527046681 \tabularnewline
31 & 475 & 625.393325176924 & -150.393325176924 \tabularnewline
32 & 337 & 613.798771577661 & -276.798771577661 \tabularnewline
33 & 803 & 592.459006734428 & 210.540993265572 \tabularnewline
34 & 722 & 608.690636884158 & 113.309363115842 \tabularnewline
35 & 590 & 617.426207311897 & -27.4262073118974 \tabularnewline
36 & 724 & 615.311787470936 & 108.688212529064 \tabularnewline
37 & 627 & 623.691090902746 & 3.30890909725417 \tabularnewline
38 & 696 & 623.946190813849 & 72.0538091861506 \tabularnewline
39 & 825 & 629.501169744311 & 195.498830255689 \tabularnewline
40 & 677 & 644.573126317691 & 32.4268736823086 \tabularnewline
41 & 656 & 647.073071873944 & 8.92692812605628 \tabularnewline
42 & 785 & 647.761292222238 & 137.238707777762 \tabularnewline
43 & 412 & 658.34169232544 & -246.34169232544 \tabularnewline
44 & 352 & 639.350011996274 & -287.350011996274 \tabularnewline
45 & 839 & 617.196800662458 & 221.803199337542 \tabularnewline
46 & 729 & 634.296689106189 & 94.7033108938111 \tabularnewline
47 & 696 & 641.597828388097 & 54.4021716119033 \tabularnewline
48 & 641 & 645.791956644311 & -4.79195664431074 \tabularnewline
49 & 695 & 645.422521377869 & 49.577478622131 \tabularnewline
50 & 638 & 649.244690563399 & -11.2446905633989 \tabularnewline
51 & 762 & 648.377782625224 & 113.622217374776 \tabularnewline
52 & 635 & 657.137472510856 & -22.1374725108562 \tabularnewline
53 & 721 & 655.430786982909 & 65.5692130170911 \tabularnewline
54 & 854 & 660.485836825672 & 193.514163174328 \tabularnewline
55 & 418 & 675.404785751774 & -257.404785751774 \tabularnewline
56 & 367 & 655.5601976918 & -288.5601976918 \tabularnewline
57 & 824 & 633.313687251157 & 190.686312748843 \tabularnewline
58 & 687 & 648.014623421105 & 38.9853765788946 \tabularnewline
59 & 601 & 651.020195894483 & -50.0201958944828 \tabularnewline
60 & 676 & 647.163895479142 & 28.8361045208578 \tabularnewline
61 & 740 & 649.38701115976 & 90.6129888402402 \tabularnewline
62 & 691 & 656.372807601601 & 34.6271923983991 \tabularnewline
63 & 683 & 659.042386439511 & 23.9576135604891 \tabularnewline
64 & 594 & 660.889395501986 & -66.8893955019859 \tabularnewline
65 & 729 & 655.732566364405 & 73.2674336355954 \tabularnewline
66 & 731 & 661.381109511975 & 69.6188904880255 \tabularnewline
67 & 386 & 666.748368706058 & -280.748368706058 \tabularnewline
68 & 331 & 645.104110192333 & -314.104110192333 \tabularnewline
69 & 706 & 620.888295181331 & 85.1117048186693 \tabularnewline
70 & 715 & 627.449970855991 & 87.5500291440088 \tabularnewline
71 & 657 & 634.199628823412 & 22.8003711765884 \tabularnewline
72 & 653 & 635.957420436697 & 17.0425795633034 \tabularnewline
73 & 642 & 637.271315863797 & 4.72868413620279 \tabularnewline
74 & 643 & 637.635873144559 & 5.36412685544121 \tabularnewline
75 & 718 & 638.049419798079 & 79.9505802019212 \tabularnewline
76 & 654 & 644.213199250078 & 9.78680074992189 \tabularnewline
77 & 632 & 644.967711365072 & -12.9677113650724 \tabularnewline
78 & 731 & 643.967967365096 & 87.0320326349038 \tabularnewline
79 & 392 & 650.677690459862 & -258.677690459862 \tabularnewline
80 & 344 & 630.734967979046 & -286.734967979046 \tabularnewline
81 & 792 & 608.629173382743 & 183.370826617257 \tabularnewline
82 & 852 & 622.766123112752 & 229.233876887248 \tabularnewline
83 & 649 & 640.438878663634 & 8.56112133636623 \tabularnewline
84 & 629 & 641.098897185649 & -12.0988971856494 \tabularnewline
85 & 685 & 640.166134300422 & 44.833865699578 \tabularnewline
86 & 617 & 643.622595272373 & -26.6225952723727 \tabularnewline
87 & 715 & 641.570129795771 & 73.4298702042294 \tabularnewline
88 & 715 & 647.231195969208 & 67.7688040307918 \tabularnewline
89 & 629 & 652.455822991509 & -23.4558229915086 \tabularnewline
90 & 916 & 650.647499406869 & 265.352500593131 \tabularnewline
91 & 531 & 671.104815495905 & -140.104815495905 \tabularnewline
92 & 357 & 660.303453195609 & -303.303453195609 \tabularnewline
93 & 917 & 636.920313413434 & 280.079686586566 \tabularnewline
94 & 828 & 658.513019967216 & 169.486980032784 \tabularnewline
95 & 708 & 671.579596373154 & 36.4204036268461 \tabularnewline
96 & 858 & 674.387422594574 & 183.612577405426 \tabularnewline
97 & 775 & 688.54301006976 & 86.4569899302403 \tabularnewline
98 & 785 & 695.208400322937 & 89.7915996770635 \tabularnewline
99 & 1006 & 702.130871875411 & 303.869128124589 \tabularnewline
100 & 789 & 725.557622291742 & 63.4423777082579 \tabularnewline
101 & 734 & 730.448704046453 & 3.55129595354742 \tabularnewline
102 & 906 & 730.722490740322 & 175.277509259678 \tabularnewline
103 & 532 & 744.235487234455 & -212.235487234455 \tabularnewline
104 & 387 & 727.873220295342 & -340.873220295342 \tabularnewline
105 & 991 & 701.593644266164 & 289.406355733836 \tabularnewline
106 & 841 & 723.905389149628 & 117.094610850372 \tabularnewline
107 & 892 & 732.932782752984 & 159.067217247016 \tabularnewline
108 & 782 & 745.196048919129 & 36.8039510808715 \tabularnewline
109 & 811 & 748.033444681007 & 62.966555318993 \tabularnewline
110 & 792 & 752.887842971278 & 39.112157028722 \tabularnewline
111 & 978 & 755.903189566739 & 222.096810433261 \tabularnewline
112 & 773 & 773.025713919229 & -0.0257139192291334 \tabularnewline
113 & 796 & 773.023731508012 & 22.9762684919875 \tabularnewline
114 & 946 & 774.795083901664 & 171.204916098336 \tabularnewline
115 & 594 & 787.994104362342 & -193.994104362342 \tabularnewline
116 & 438 & 773.038154433552 & -335.038154433552 \tabularnewline
117 & 1023 & 747.208432038571 & 275.791567961429 \tabularnewline
118 & 868 & 768.470546651649 & 99.529453348351 \tabularnewline
119 & 791 & 776.143756750536 & 14.8562432494637 \tabularnewline
120 & 760 & 777.28909686744 & -17.2890968674395 \tabularnewline
121 & 779 & 775.95619622124 & 3.04380377875975 \tabularnewline
122 & 852 & 776.190857872726 & 75.8091421272738 \tabularnewline
123 & 1001 & 782.035353701745 & 218.964646298255 \tabularnewline
124 & 734 & 798.916404272721 & -64.9164042727207 \tabularnewline
125 & 996 & 793.911682634137 & 202.088317365863 \tabularnewline
126 & 869 & 809.491654848508 & 59.5083451514915 \tabularnewline
127 & 599 & 814.079442881327 & -215.079442881327 \tabularnewline
128 & 426 & 797.497921556138 & -371.497921556138 \tabularnewline
129 & 1138 & 768.857338216217 & 369.142661783783 \tabularnewline
130 & 1091 & 797.316343114152 & 293.683656885848 \tabularnewline
131 & 830 & 819.957845966654 & 10.0421540333464 \tabularnewline
132 & 909 & 820.732044509406 & 88.2679554905937 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187430&T=2

[TABLE]
[ROW][C]Interpolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Residuals[/C][/ROW]
[ROW][C]2[/C][C]614[/C][C]617[/C][C]-3[/C][/ROW]
[ROW][C]3[/C][C]647[/C][C]616.768715395069[/C][C]30.2312846049309[/C][/ROW]
[ROW][C]4[/C][C]580[/C][C]619.099392300537[/C][C]-39.0993923005375[/C][/ROW]
[ROW][C]5[/C][C]614[/C][C]616.085029800115[/C][C]-2.08502980011474[/C][/ROW]
[ROW][C]6[/C][C]636[/C][C]615.924284702252[/C][C]20.0757152977482[/C][/ROW]
[ROW][C]7[/C][C]388[/C][C]617.472019329367[/C][C]-229.472019329367[/C][/ROW]
[ROW][C]8[/C][C]356[/C][C]599.780904218271[/C][C]-243.780904218271[/C][/ROW]
[ROW][C]9[/C][C]639[/C][C]580.986647510997[/C][C]58.013352489003[/C][/ROW]
[ROW][C]10[/C][C]753[/C][C]585.459179281376[/C][C]167.540820718624[/C][/ROW]
[ROW][C]11[/C][C]611[/C][C]598.375716791278[/C][C]12.6242832087222[/C][/ROW]
[ROW][C]12[/C][C]639[/C][C]599.348984242766[/C][C]39.6510157572338[/C][/ROW]
[ROW][C]13[/C][C]630[/C][C]602.40587408094[/C][C]27.5941259190603[/C][/ROW]
[ROW][C]14[/C][C]586[/C][C]604.533239584808[/C][C]-18.5332395848076[/C][/ROW]
[ROW][C]15[/C][C]695[/C][C]603.104421919654[/C][C]91.8955780803465[/C][/ROW]
[ROW][C]16[/C][C]552[/C][C]610.189099410057[/C][C]-58.1890994100568[/C][/ROW]
[ROW][C]17[/C][C]619[/C][C]605.703018453943[/C][C]13.2969815460566[/C][/ROW]
[ROW][C]18[/C][C]681[/C][C]606.728147495161[/C][C]74.2718525048389[/C][/ROW]
[ROW][C]19[/C][C]421[/C][C]612.454126183184[/C][C]-191.454126183184[/C][/ROW]
[ROW][C]20[/C][C]307[/C][C]597.693995537628[/C][C]-290.693995537628[/C][/ROW]
[ROW][C]21[/C][C]754[/C][C]575.282980233059[/C][C]178.717019766941[/C][/ROW]
[ROW][C]22[/C][C]690[/C][C]589.061145336801[/C][C]100.938854663199[/C][/ROW]
[ROW][C]23[/C][C]644[/C][C]596.843013044453[/C][C]47.1569869555474[/C][/ROW]
[ROW][C]24[/C][C]643[/C][C]600.478574743701[/C][C]42.5214252562989[/C][/ROW]
[ROW][C]25[/C][C]608[/C][C]603.756758424202[/C][C]4.24324157579827[/C][/ROW]
[ROW][C]26[/C][C]651[/C][C]604.083890574697[/C][C]46.9161094253033[/C][/ROW]
[ROW][C]27[/C][C]691[/C][C]607.700881852472[/C][C]83.2991181475279[/C][/ROW]
[ROW][C]28[/C][C]627[/C][C]614.12281639642[/C][C]12.8771836035801[/C][/ROW]
[ROW][C]29[/C][C]634[/C][C]615.115581170546[/C][C]18.8844188294545[/C][/ROW]
[ROW][C]30[/C][C]731[/C][C]616.571472953319[/C][C]114.428527046681[/C][/ROW]
[ROW][C]31[/C][C]475[/C][C]625.393325176924[/C][C]-150.393325176924[/C][/ROW]
[ROW][C]32[/C][C]337[/C][C]613.798771577661[/C][C]-276.798771577661[/C][/ROW]
[ROW][C]33[/C][C]803[/C][C]592.459006734428[/C][C]210.540993265572[/C][/ROW]
[ROW][C]34[/C][C]722[/C][C]608.690636884158[/C][C]113.309363115842[/C][/ROW]
[ROW][C]35[/C][C]590[/C][C]617.426207311897[/C][C]-27.4262073118974[/C][/ROW]
[ROW][C]36[/C][C]724[/C][C]615.311787470936[/C][C]108.688212529064[/C][/ROW]
[ROW][C]37[/C][C]627[/C][C]623.691090902746[/C][C]3.30890909725417[/C][/ROW]
[ROW][C]38[/C][C]696[/C][C]623.946190813849[/C][C]72.0538091861506[/C][/ROW]
[ROW][C]39[/C][C]825[/C][C]629.501169744311[/C][C]195.498830255689[/C][/ROW]
[ROW][C]40[/C][C]677[/C][C]644.573126317691[/C][C]32.4268736823086[/C][/ROW]
[ROW][C]41[/C][C]656[/C][C]647.073071873944[/C][C]8.92692812605628[/C][/ROW]
[ROW][C]42[/C][C]785[/C][C]647.761292222238[/C][C]137.238707777762[/C][/ROW]
[ROW][C]43[/C][C]412[/C][C]658.34169232544[/C][C]-246.34169232544[/C][/ROW]
[ROW][C]44[/C][C]352[/C][C]639.350011996274[/C][C]-287.350011996274[/C][/ROW]
[ROW][C]45[/C][C]839[/C][C]617.196800662458[/C][C]221.803199337542[/C][/ROW]
[ROW][C]46[/C][C]729[/C][C]634.296689106189[/C][C]94.7033108938111[/C][/ROW]
[ROW][C]47[/C][C]696[/C][C]641.597828388097[/C][C]54.4021716119033[/C][/ROW]
[ROW][C]48[/C][C]641[/C][C]645.791956644311[/C][C]-4.79195664431074[/C][/ROW]
[ROW][C]49[/C][C]695[/C][C]645.422521377869[/C][C]49.577478622131[/C][/ROW]
[ROW][C]50[/C][C]638[/C][C]649.244690563399[/C][C]-11.2446905633989[/C][/ROW]
[ROW][C]51[/C][C]762[/C][C]648.377782625224[/C][C]113.622217374776[/C][/ROW]
[ROW][C]52[/C][C]635[/C][C]657.137472510856[/C][C]-22.1374725108562[/C][/ROW]
[ROW][C]53[/C][C]721[/C][C]655.430786982909[/C][C]65.5692130170911[/C][/ROW]
[ROW][C]54[/C][C]854[/C][C]660.485836825672[/C][C]193.514163174328[/C][/ROW]
[ROW][C]55[/C][C]418[/C][C]675.404785751774[/C][C]-257.404785751774[/C][/ROW]
[ROW][C]56[/C][C]367[/C][C]655.5601976918[/C][C]-288.5601976918[/C][/ROW]
[ROW][C]57[/C][C]824[/C][C]633.313687251157[/C][C]190.686312748843[/C][/ROW]
[ROW][C]58[/C][C]687[/C][C]648.014623421105[/C][C]38.9853765788946[/C][/ROW]
[ROW][C]59[/C][C]601[/C][C]651.020195894483[/C][C]-50.0201958944828[/C][/ROW]
[ROW][C]60[/C][C]676[/C][C]647.163895479142[/C][C]28.8361045208578[/C][/ROW]
[ROW][C]61[/C][C]740[/C][C]649.38701115976[/C][C]90.6129888402402[/C][/ROW]
[ROW][C]62[/C][C]691[/C][C]656.372807601601[/C][C]34.6271923983991[/C][/ROW]
[ROW][C]63[/C][C]683[/C][C]659.042386439511[/C][C]23.9576135604891[/C][/ROW]
[ROW][C]64[/C][C]594[/C][C]660.889395501986[/C][C]-66.8893955019859[/C][/ROW]
[ROW][C]65[/C][C]729[/C][C]655.732566364405[/C][C]73.2674336355954[/C][/ROW]
[ROW][C]66[/C][C]731[/C][C]661.381109511975[/C][C]69.6188904880255[/C][/ROW]
[ROW][C]67[/C][C]386[/C][C]666.748368706058[/C][C]-280.748368706058[/C][/ROW]
[ROW][C]68[/C][C]331[/C][C]645.104110192333[/C][C]-314.104110192333[/C][/ROW]
[ROW][C]69[/C][C]706[/C][C]620.888295181331[/C][C]85.1117048186693[/C][/ROW]
[ROW][C]70[/C][C]715[/C][C]627.449970855991[/C][C]87.5500291440088[/C][/ROW]
[ROW][C]71[/C][C]657[/C][C]634.199628823412[/C][C]22.8003711765884[/C][/ROW]
[ROW][C]72[/C][C]653[/C][C]635.957420436697[/C][C]17.0425795633034[/C][/ROW]
[ROW][C]73[/C][C]642[/C][C]637.271315863797[/C][C]4.72868413620279[/C][/ROW]
[ROW][C]74[/C][C]643[/C][C]637.635873144559[/C][C]5.36412685544121[/C][/ROW]
[ROW][C]75[/C][C]718[/C][C]638.049419798079[/C][C]79.9505802019212[/C][/ROW]
[ROW][C]76[/C][C]654[/C][C]644.213199250078[/C][C]9.78680074992189[/C][/ROW]
[ROW][C]77[/C][C]632[/C][C]644.967711365072[/C][C]-12.9677113650724[/C][/ROW]
[ROW][C]78[/C][C]731[/C][C]643.967967365096[/C][C]87.0320326349038[/C][/ROW]
[ROW][C]79[/C][C]392[/C][C]650.677690459862[/C][C]-258.677690459862[/C][/ROW]
[ROW][C]80[/C][C]344[/C][C]630.734967979046[/C][C]-286.734967979046[/C][/ROW]
[ROW][C]81[/C][C]792[/C][C]608.629173382743[/C][C]183.370826617257[/C][/ROW]
[ROW][C]82[/C][C]852[/C][C]622.766123112752[/C][C]229.233876887248[/C][/ROW]
[ROW][C]83[/C][C]649[/C][C]640.438878663634[/C][C]8.56112133636623[/C][/ROW]
[ROW][C]84[/C][C]629[/C][C]641.098897185649[/C][C]-12.0988971856494[/C][/ROW]
[ROW][C]85[/C][C]685[/C][C]640.166134300422[/C][C]44.833865699578[/C][/ROW]
[ROW][C]86[/C][C]617[/C][C]643.622595272373[/C][C]-26.6225952723727[/C][/ROW]
[ROW][C]87[/C][C]715[/C][C]641.570129795771[/C][C]73.4298702042294[/C][/ROW]
[ROW][C]88[/C][C]715[/C][C]647.231195969208[/C][C]67.7688040307918[/C][/ROW]
[ROW][C]89[/C][C]629[/C][C]652.455822991509[/C][C]-23.4558229915086[/C][/ROW]
[ROW][C]90[/C][C]916[/C][C]650.647499406869[/C][C]265.352500593131[/C][/ROW]
[ROW][C]91[/C][C]531[/C][C]671.104815495905[/C][C]-140.104815495905[/C][/ROW]
[ROW][C]92[/C][C]357[/C][C]660.303453195609[/C][C]-303.303453195609[/C][/ROW]
[ROW][C]93[/C][C]917[/C][C]636.920313413434[/C][C]280.079686586566[/C][/ROW]
[ROW][C]94[/C][C]828[/C][C]658.513019967216[/C][C]169.486980032784[/C][/ROW]
[ROW][C]95[/C][C]708[/C][C]671.579596373154[/C][C]36.4204036268461[/C][/ROW]
[ROW][C]96[/C][C]858[/C][C]674.387422594574[/C][C]183.612577405426[/C][/ROW]
[ROW][C]97[/C][C]775[/C][C]688.54301006976[/C][C]86.4569899302403[/C][/ROW]
[ROW][C]98[/C][C]785[/C][C]695.208400322937[/C][C]89.7915996770635[/C][/ROW]
[ROW][C]99[/C][C]1006[/C][C]702.130871875411[/C][C]303.869128124589[/C][/ROW]
[ROW][C]100[/C][C]789[/C][C]725.557622291742[/C][C]63.4423777082579[/C][/ROW]
[ROW][C]101[/C][C]734[/C][C]730.448704046453[/C][C]3.55129595354742[/C][/ROW]
[ROW][C]102[/C][C]906[/C][C]730.722490740322[/C][C]175.277509259678[/C][/ROW]
[ROW][C]103[/C][C]532[/C][C]744.235487234455[/C][C]-212.235487234455[/C][/ROW]
[ROW][C]104[/C][C]387[/C][C]727.873220295342[/C][C]-340.873220295342[/C][/ROW]
[ROW][C]105[/C][C]991[/C][C]701.593644266164[/C][C]289.406355733836[/C][/ROW]
[ROW][C]106[/C][C]841[/C][C]723.905389149628[/C][C]117.094610850372[/C][/ROW]
[ROW][C]107[/C][C]892[/C][C]732.932782752984[/C][C]159.067217247016[/C][/ROW]
[ROW][C]108[/C][C]782[/C][C]745.196048919129[/C][C]36.8039510808715[/C][/ROW]
[ROW][C]109[/C][C]811[/C][C]748.033444681007[/C][C]62.966555318993[/C][/ROW]
[ROW][C]110[/C][C]792[/C][C]752.887842971278[/C][C]39.112157028722[/C][/ROW]
[ROW][C]111[/C][C]978[/C][C]755.903189566739[/C][C]222.096810433261[/C][/ROW]
[ROW][C]112[/C][C]773[/C][C]773.025713919229[/C][C]-0.0257139192291334[/C][/ROW]
[ROW][C]113[/C][C]796[/C][C]773.023731508012[/C][C]22.9762684919875[/C][/ROW]
[ROW][C]114[/C][C]946[/C][C]774.795083901664[/C][C]171.204916098336[/C][/ROW]
[ROW][C]115[/C][C]594[/C][C]787.994104362342[/C][C]-193.994104362342[/C][/ROW]
[ROW][C]116[/C][C]438[/C][C]773.038154433552[/C][C]-335.038154433552[/C][/ROW]
[ROW][C]117[/C][C]1023[/C][C]747.208432038571[/C][C]275.791567961429[/C][/ROW]
[ROW][C]118[/C][C]868[/C][C]768.470546651649[/C][C]99.529453348351[/C][/ROW]
[ROW][C]119[/C][C]791[/C][C]776.143756750536[/C][C]14.8562432494637[/C][/ROW]
[ROW][C]120[/C][C]760[/C][C]777.28909686744[/C][C]-17.2890968674395[/C][/ROW]
[ROW][C]121[/C][C]779[/C][C]775.95619622124[/C][C]3.04380377875975[/C][/ROW]
[ROW][C]122[/C][C]852[/C][C]776.190857872726[/C][C]75.8091421272738[/C][/ROW]
[ROW][C]123[/C][C]1001[/C][C]782.035353701745[/C][C]218.964646298255[/C][/ROW]
[ROW][C]124[/C][C]734[/C][C]798.916404272721[/C][C]-64.9164042727207[/C][/ROW]
[ROW][C]125[/C][C]996[/C][C]793.911682634137[/C][C]202.088317365863[/C][/ROW]
[ROW][C]126[/C][C]869[/C][C]809.491654848508[/C][C]59.5083451514915[/C][/ROW]
[ROW][C]127[/C][C]599[/C][C]814.079442881327[/C][C]-215.079442881327[/C][/ROW]
[ROW][C]128[/C][C]426[/C][C]797.497921556138[/C][C]-371.497921556138[/C][/ROW]
[ROW][C]129[/C][C]1138[/C][C]768.857338216217[/C][C]369.142661783783[/C][/ROW]
[ROW][C]130[/C][C]1091[/C][C]797.316343114152[/C][C]293.683656885848[/C][/ROW]
[ROW][C]131[/C][C]830[/C][C]819.957845966654[/C][C]10.0421540333464[/C][/ROW]
[ROW][C]132[/C][C]909[/C][C]820.732044509406[/C][C]88.2679554905937[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187430&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187430&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
2614617-3
3647616.76871539506930.2312846049309
4580619.099392300537-39.0993923005375
5614616.085029800115-2.08502980011474
6636615.92428470225220.0757152977482
7388617.472019329367-229.472019329367
8356599.780904218271-243.780904218271
9639580.98664751099758.013352489003
10753585.459179281376167.540820718624
11611598.37571679127812.6242832087222
12639599.34898424276639.6510157572338
13630602.4058740809427.5941259190603
14586604.533239584808-18.5332395848076
15695603.10442191965491.8955780803465
16552610.189099410057-58.1890994100568
17619605.70301845394313.2969815460566
18681606.72814749516174.2718525048389
19421612.454126183184-191.454126183184
20307597.693995537628-290.693995537628
21754575.282980233059178.717019766941
22690589.061145336801100.938854663199
23644596.84301304445347.1569869555474
24643600.47857474370142.5214252562989
25608603.7567584242024.24324157579827
26651604.08389057469746.9161094253033
27691607.70088185247283.2991181475279
28627614.1228163964212.8771836035801
29634615.11558117054618.8844188294545
30731616.571472953319114.428527046681
31475625.393325176924-150.393325176924
32337613.798771577661-276.798771577661
33803592.459006734428210.540993265572
34722608.690636884158113.309363115842
35590617.426207311897-27.4262073118974
36724615.311787470936108.688212529064
37627623.6910909027463.30890909725417
38696623.94619081384972.0538091861506
39825629.501169744311195.498830255689
40677644.57312631769132.4268736823086
41656647.0730718739448.92692812605628
42785647.761292222238137.238707777762
43412658.34169232544-246.34169232544
44352639.350011996274-287.350011996274
45839617.196800662458221.803199337542
46729634.29668910618994.7033108938111
47696641.59782838809754.4021716119033
48641645.791956644311-4.79195664431074
49695645.42252137786949.577478622131
50638649.244690563399-11.2446905633989
51762648.377782625224113.622217374776
52635657.137472510856-22.1374725108562
53721655.43078698290965.5692130170911
54854660.485836825672193.514163174328
55418675.404785751774-257.404785751774
56367655.5601976918-288.5601976918
57824633.313687251157190.686312748843
58687648.01462342110538.9853765788946
59601651.020195894483-50.0201958944828
60676647.16389547914228.8361045208578
61740649.3870111597690.6129888402402
62691656.37280760160134.6271923983991
63683659.04238643951123.9576135604891
64594660.889395501986-66.8893955019859
65729655.73256636440573.2674336355954
66731661.38110951197569.6188904880255
67386666.748368706058-280.748368706058
68331645.104110192333-314.104110192333
69706620.88829518133185.1117048186693
70715627.44997085599187.5500291440088
71657634.19962882341222.8003711765884
72653635.95742043669717.0425795633034
73642637.2713158637974.72868413620279
74643637.6358731445595.36412685544121
75718638.04941979807979.9505802019212
76654644.2131992500789.78680074992189
77632644.967711365072-12.9677113650724
78731643.96796736509687.0320326349038
79392650.677690459862-258.677690459862
80344630.734967979046-286.734967979046
81792608.629173382743183.370826617257
82852622.766123112752229.233876887248
83649640.4388786636348.56112133636623
84629641.098897185649-12.0988971856494
85685640.16613430042244.833865699578
86617643.622595272373-26.6225952723727
87715641.57012979577173.4298702042294
88715647.23119596920867.7688040307918
89629652.455822991509-23.4558229915086
90916650.647499406869265.352500593131
91531671.104815495905-140.104815495905
92357660.303453195609-303.303453195609
93917636.920313413434280.079686586566
94828658.513019967216169.486980032784
95708671.57959637315436.4204036268461
96858674.387422594574183.612577405426
97775688.5430100697686.4569899302403
98785695.20840032293789.7915996770635
991006702.130871875411303.869128124589
100789725.55762229174263.4423777082579
101734730.4487040464533.55129595354742
102906730.722490740322175.277509259678
103532744.235487234455-212.235487234455
104387727.873220295342-340.873220295342
105991701.593644266164289.406355733836
106841723.905389149628117.094610850372
107892732.932782752984159.067217247016
108782745.19604891912936.8039510808715
109811748.03344468100762.966555318993
110792752.88784297127839.112157028722
111978755.903189566739222.096810433261
112773773.025713919229-0.0257139192291334
113796773.02373150801222.9762684919875
114946774.795083901664171.204916098336
115594787.994104362342-193.994104362342
116438773.038154433552-335.038154433552
1171023747.208432038571275.791567961429
118868768.47054665164999.529453348351
119791776.14375675053614.8562432494637
120760777.28909686744-17.2890968674395
121779775.956196221243.04380377875975
122852776.19085787272675.8091421272738
1231001782.035353701745218.964646298255
124734798.916404272721-64.9164042727207
125996793.911682634137202.088317365863
126869809.49165484850859.5083451514915
127599814.079442881327-215.079442881327
128426797.497921556138-371.497921556138
1291138768.857338216217369.142661783783
1301091797.316343114152293.683656885848
131830819.95784596665410.0421540333464
132909820.73204450940688.2679554905937







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
133827.537050913973527.0157167992641128.05838502868
134827.537050913973526.1239478062961128.95015402165
135827.537050913973525.2348094536871129.83929237426
136827.537050913973524.3482785974061130.72582323054
137827.537050913973523.4643324308091131.60976939714
138827.537050913973522.5829484777941132.49115335015
139827.537050913973521.7041045861291133.36999724182
140827.537050913973520.827778920961134.24632290699
141827.537050913973519.9539499584781135.12015186947
142827.537050913973519.0825964797521135.99150534819
143827.537050913973518.213697564721136.86040426323
144827.537050913973517.3472325863221137.72686924162

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
133 & 827.537050913973 & 527.015716799264 & 1128.05838502868 \tabularnewline
134 & 827.537050913973 & 526.123947806296 & 1128.95015402165 \tabularnewline
135 & 827.537050913973 & 525.234809453687 & 1129.83929237426 \tabularnewline
136 & 827.537050913973 & 524.348278597406 & 1130.72582323054 \tabularnewline
137 & 827.537050913973 & 523.464332430809 & 1131.60976939714 \tabularnewline
138 & 827.537050913973 & 522.582948477794 & 1132.49115335015 \tabularnewline
139 & 827.537050913973 & 521.704104586129 & 1133.36999724182 \tabularnewline
140 & 827.537050913973 & 520.82777892096 & 1134.24632290699 \tabularnewline
141 & 827.537050913973 & 519.953949958478 & 1135.12015186947 \tabularnewline
142 & 827.537050913973 & 519.082596479752 & 1135.99150534819 \tabularnewline
143 & 827.537050913973 & 518.21369756472 & 1136.86040426323 \tabularnewline
144 & 827.537050913973 & 517.347232586322 & 1137.72686924162 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187430&T=3

[TABLE]
[ROW][C]Extrapolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Forecast[/C][C]95% Lower Bound[/C][C]95% Upper Bound[/C][/ROW]
[ROW][C]133[/C][C]827.537050913973[/C][C]527.015716799264[/C][C]1128.05838502868[/C][/ROW]
[ROW][C]134[/C][C]827.537050913973[/C][C]526.123947806296[/C][C]1128.95015402165[/C][/ROW]
[ROW][C]135[/C][C]827.537050913973[/C][C]525.234809453687[/C][C]1129.83929237426[/C][/ROW]
[ROW][C]136[/C][C]827.537050913973[/C][C]524.348278597406[/C][C]1130.72582323054[/C][/ROW]
[ROW][C]137[/C][C]827.537050913973[/C][C]523.464332430809[/C][C]1131.60976939714[/C][/ROW]
[ROW][C]138[/C][C]827.537050913973[/C][C]522.582948477794[/C][C]1132.49115335015[/C][/ROW]
[ROW][C]139[/C][C]827.537050913973[/C][C]521.704104586129[/C][C]1133.36999724182[/C][/ROW]
[ROW][C]140[/C][C]827.537050913973[/C][C]520.82777892096[/C][C]1134.24632290699[/C][/ROW]
[ROW][C]141[/C][C]827.537050913973[/C][C]519.953949958478[/C][C]1135.12015186947[/C][/ROW]
[ROW][C]142[/C][C]827.537050913973[/C][C]519.082596479752[/C][C]1135.99150534819[/C][/ROW]
[ROW][C]143[/C][C]827.537050913973[/C][C]518.21369756472[/C][C]1136.86040426323[/C][/ROW]
[ROW][C]144[/C][C]827.537050913973[/C][C]517.347232586322[/C][C]1137.72686924162[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187430&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187430&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
133827.537050913973527.0157167992641128.05838502868
134827.537050913973526.1239478062961128.95015402165
135827.537050913973525.2348094536871129.83929237426
136827.537050913973524.3482785974061130.72582323054
137827.537050913973523.4643324308091131.60976939714
138827.537050913973522.5829484777941132.49115335015
139827.537050913973521.7041045861291133.36999724182
140827.537050913973520.827778920961134.24632290699
141827.537050913973519.9539499584781135.12015186947
142827.537050913973519.0825964797521135.99150534819
143827.537050913973518.213697564721136.86040426323
144827.537050913973517.3472325863221137.72686924162



Parameters (Session):
par1 = 12 ; par2 = Single ; par3 = additive ;
Parameters (R input):
par1 = 12 ; par2 = Single ; par3 = additive ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
if (par2 == 'Single') K <- 1
if (par2 == 'Double') K <- 2
if (par2 == 'Triple') K <- par1
nx <- length(x)
nxmK <- nx - K
x <- ts(x, frequency = par1)
if (par2 == 'Single') fit <- HoltWinters(x, gamma=F, beta=F)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=F)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par1, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$alpha)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,fit$beta)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'gamma',header=TRUE)
a<-table.element(a,fit$gamma)
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,'Interpolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nxmK) {
a<-table.row.start(a)
a<-table.element(a,i+K,header=TRUE)
a<-table.element(a,x[i+K])
a<-table.element(a,fit$fitted[i,'xhat'])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Extrapolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Forecast',header=TRUE)
a<-table.element(a,'95% Lower Bound',header=TRUE)
a<-table.element(a,'95% Upper Bound',header=TRUE)
a<-table.row.end(a)
for (i in 1:np) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,p[i,'fit'])
a<-table.element(a,p[i,'lwr'])
a<-table.element(a,p[i,'upr'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')