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 17:22:04 -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/t1352586134dypxnuh1kj15hgo.htm/, Retrieved Fri, 29 Mar 2024 08:30:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=187431, Retrieved Fri, 29 Mar 2024 08:30:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
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] [391561951b5d7f721cfaa4f5575ab127]
- R P         [Exponential Smoothing] [] [2012-11-10 22:22:04] [7338cd26db379c04f0557b08db763c32] [Current]
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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'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187431&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187431&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187431&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'Gwilym Jenkins' @ jenkins.wessa.net







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.054134723682192
beta0.0515900376014013
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.054134723682192 \tabularnewline
beta & 0.0515900376014013 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187431&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]0.054134723682192[/C][/ROW]
[ROW][C]beta[/C][C]0.0515900376014013[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187431&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187431&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
alpha0.054134723682192
beta0.0515900376014013
gammaFALSE







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
364761136
4580610.04939130005-30.0493913000499
5614605.4392947391498.56070526085125
6636602.94325353097933.0567464690206
7388601.865620037366-213.865620037366
8356586.823625899921-230.823625899921
9639570.21896770928468.7810322907158
10753570.025417425646182.974582574354
11611576.52471712154434.4752828784557
12639575.08133125996863.9186687400324
13630575.41036780886154.5896321911388
14586575.38683814530910.6131618546913
15695573.012294981927121.987705018073
16552577.007670716844-25.0076707168445
17619572.97565067016146.024349329839
18681572.917466776198108.082533223802
19421576.520639761839-155.520639761839
20307565.419387840258-258.419387840258
21754548.026023745616205.973976254384
22690556.347712777284133.652287222716
23644561.12755292608782.8724470739128
24643563.38988766012779.6101123398729
25608565.69795291610242.3020470838983
26651566.1044980512584.8955019487498
27691569.053925309297121.946074690703
28627574.34964759821952.6503524017807
29634576.04110766732157.9588923326792
30731578.181812393691152.818187606309
31475585.88449139231-110.88449139231
32337579.002019143196-242.002019143196
33803564.345669512174238.654330487826
34722576.376035335251145.623964664749
35590583.7769284291096.22307157089097
36724583.648772565918140.351227434082
37627591.17358201096935.8264179890311
38696593.140026244272102.859973755728
39825599.022582109725225.977417890275
40677612.20117933551264.7988206644876
41656616.83538868881939.1646113111808
42785620.191276615271164.808723384729
43412630.809153682411-218.809153682411
44352620.048890050703-268.048890050703
45839605.87443662445233.12556337555
46729619.481999722219109.518000277781
47696626.70396477917869.2960352208215
48641631.942055700369.05794429963953
49695633.94447135381461.0555286461856
50638638.932278507352-0.93227850735218
51762640.561789169964121.438210830036
52635649.154946604431-14.1549466044307
53721650.36827381715970.6317261828408
54854656.368765296016197.631234703984
55418669.796287045338-251.796287045338
56367658.190954289222-291.190954289222
57824643.639760390576180.360239609424
58687655.11957239475431.8804276052456
59601658.650506858202-57.6505068582023
60676657.17370187129618.826298128704
61740659.88952590988380.1104740901168
62691666.14668540806924.8533145919311
63683669.48192449128413.5180755087157
64594672.241286988173-78.2412869881726
65729669.81476851217459.1852314878258
66731674.99308989066956.0069101093308
67386680.155770514303-294.155770514303
68331665.540969286221-334.540969286221
69706647.80561630430358.1943836956971
70715651.49340913444863.5065908655519
71657655.6461388282521.35386117174835
72653656.438028754296-3.4380287542964
73642656.960909273839-14.9609092738386
74643656.818218827044-13.8182188270438
75718656.69879591853661.3012040814643
76654660.817144977097-6.81714497709675
77632661.228887024454-29.2288870244543
78731660.34574480983670.6542551901643
79392665.067072981664-273.067072981664
80344650.418516913503-306.418516913503
81792633.108720200779158.891279799221
82852641.432094301005210.567905698995
83649653.141044929603-4.1410449296028
84629653.215220682074-24.2152206820745
85685652.13505790771532.8649420922853
86617654.236699591676-37.2366995916761
87715652.43941315592562.5605868440748
88715656.21934523019158.7806547698086
89629659.958815069382-30.9588150693825
90916658.753801341889257.246198658111
91531673.869126777617-142.869126777617
92357666.925312956701-309.925312956701
93917650.072395391087266.927604608913
94828665.192729855297162.807270144703
95708675.13122895139432.8687710486062
96858678.127339615598179.872660384402
97775689.58381581099785.4161841890026
98785696.16546814851788.8345318514827
991006703.180269976991302.819730023009
100789722.62482008670866.3751799132915
101734729.4548832376024.5451167623977
102906732.950486663161173.049513336839
103532746.051323868417-214.051323868417
104387737.598759016399-350.598759016399
105991720.775079920901270.224920079099
106841738.314206664376102.685793335624
107892747.07043123568144.92956876432
108782758.51827201827323.4817279817269
109811763.45714756009447.5428524399058
110792769.83134369520822.168656304792
111978774.893827632781203.106172367219
112773790.318551448994-17.3185514489943
113796793.7622762829322.23772371706798
114946798.27092421185147.72907578815
115594811.068285882463-217.068285882463
116438803.511212172007-365.511212172007
1171023786.897417407521236.102582592479
118868803.51120941349764.4887905865033
119791811.014841305141-20.014841305141
120760813.887994736602-53.887994736602
121779814.776935305262-35.7769353052615
122852816.5463948021935.4536051978102
1231001822.270915196281178.729084803719
124734836.250770898968-102.250770898968
125996834.734292528953161.265707471047
126869847.9335907736121.0664092263896
127599853.602073276905-254.602073276905
128426843.636262816966-417.636262816966
1291138823.678261809384314.321738190616
1301091844.22244659085246.77755340915
131830861.799349011515-31.799349011515
132909864.20675817820144.793241821799

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 647 & 611 & 36 \tabularnewline
4 & 580 & 610.04939130005 & -30.0493913000499 \tabularnewline
5 & 614 & 605.439294739149 & 8.56070526085125 \tabularnewline
6 & 636 & 602.943253530979 & 33.0567464690206 \tabularnewline
7 & 388 & 601.865620037366 & -213.865620037366 \tabularnewline
8 & 356 & 586.823625899921 & -230.823625899921 \tabularnewline
9 & 639 & 570.218967709284 & 68.7810322907158 \tabularnewline
10 & 753 & 570.025417425646 & 182.974582574354 \tabularnewline
11 & 611 & 576.524717121544 & 34.4752828784557 \tabularnewline
12 & 639 & 575.081331259968 & 63.9186687400324 \tabularnewline
13 & 630 & 575.410367808861 & 54.5896321911388 \tabularnewline
14 & 586 & 575.386838145309 & 10.6131618546913 \tabularnewline
15 & 695 & 573.012294981927 & 121.987705018073 \tabularnewline
16 & 552 & 577.007670716844 & -25.0076707168445 \tabularnewline
17 & 619 & 572.975650670161 & 46.024349329839 \tabularnewline
18 & 681 & 572.917466776198 & 108.082533223802 \tabularnewline
19 & 421 & 576.520639761839 & -155.520639761839 \tabularnewline
20 & 307 & 565.419387840258 & -258.419387840258 \tabularnewline
21 & 754 & 548.026023745616 & 205.973976254384 \tabularnewline
22 & 690 & 556.347712777284 & 133.652287222716 \tabularnewline
23 & 644 & 561.127552926087 & 82.8724470739128 \tabularnewline
24 & 643 & 563.389887660127 & 79.6101123398729 \tabularnewline
25 & 608 & 565.697952916102 & 42.3020470838983 \tabularnewline
26 & 651 & 566.10449805125 & 84.8955019487498 \tabularnewline
27 & 691 & 569.053925309297 & 121.946074690703 \tabularnewline
28 & 627 & 574.349647598219 & 52.6503524017807 \tabularnewline
29 & 634 & 576.041107667321 & 57.9588923326792 \tabularnewline
30 & 731 & 578.181812393691 & 152.818187606309 \tabularnewline
31 & 475 & 585.88449139231 & -110.88449139231 \tabularnewline
32 & 337 & 579.002019143196 & -242.002019143196 \tabularnewline
33 & 803 & 564.345669512174 & 238.654330487826 \tabularnewline
34 & 722 & 576.376035335251 & 145.623964664749 \tabularnewline
35 & 590 & 583.776928429109 & 6.22307157089097 \tabularnewline
36 & 724 & 583.648772565918 & 140.351227434082 \tabularnewline
37 & 627 & 591.173582010969 & 35.8264179890311 \tabularnewline
38 & 696 & 593.140026244272 & 102.859973755728 \tabularnewline
39 & 825 & 599.022582109725 & 225.977417890275 \tabularnewline
40 & 677 & 612.201179335512 & 64.7988206644876 \tabularnewline
41 & 656 & 616.835388688819 & 39.1646113111808 \tabularnewline
42 & 785 & 620.191276615271 & 164.808723384729 \tabularnewline
43 & 412 & 630.809153682411 & -218.809153682411 \tabularnewline
44 & 352 & 620.048890050703 & -268.048890050703 \tabularnewline
45 & 839 & 605.87443662445 & 233.12556337555 \tabularnewline
46 & 729 & 619.481999722219 & 109.518000277781 \tabularnewline
47 & 696 & 626.703964779178 & 69.2960352208215 \tabularnewline
48 & 641 & 631.94205570036 & 9.05794429963953 \tabularnewline
49 & 695 & 633.944471353814 & 61.0555286461856 \tabularnewline
50 & 638 & 638.932278507352 & -0.93227850735218 \tabularnewline
51 & 762 & 640.561789169964 & 121.438210830036 \tabularnewline
52 & 635 & 649.154946604431 & -14.1549466044307 \tabularnewline
53 & 721 & 650.368273817159 & 70.6317261828408 \tabularnewline
54 & 854 & 656.368765296016 & 197.631234703984 \tabularnewline
55 & 418 & 669.796287045338 & -251.796287045338 \tabularnewline
56 & 367 & 658.190954289222 & -291.190954289222 \tabularnewline
57 & 824 & 643.639760390576 & 180.360239609424 \tabularnewline
58 & 687 & 655.119572394754 & 31.8804276052456 \tabularnewline
59 & 601 & 658.650506858202 & -57.6505068582023 \tabularnewline
60 & 676 & 657.173701871296 & 18.826298128704 \tabularnewline
61 & 740 & 659.889525909883 & 80.1104740901168 \tabularnewline
62 & 691 & 666.146685408069 & 24.8533145919311 \tabularnewline
63 & 683 & 669.481924491284 & 13.5180755087157 \tabularnewline
64 & 594 & 672.241286988173 & -78.2412869881726 \tabularnewline
65 & 729 & 669.814768512174 & 59.1852314878258 \tabularnewline
66 & 731 & 674.993089890669 & 56.0069101093308 \tabularnewline
67 & 386 & 680.155770514303 & -294.155770514303 \tabularnewline
68 & 331 & 665.540969286221 & -334.540969286221 \tabularnewline
69 & 706 & 647.805616304303 & 58.1943836956971 \tabularnewline
70 & 715 & 651.493409134448 & 63.5065908655519 \tabularnewline
71 & 657 & 655.646138828252 & 1.35386117174835 \tabularnewline
72 & 653 & 656.438028754296 & -3.4380287542964 \tabularnewline
73 & 642 & 656.960909273839 & -14.9609092738386 \tabularnewline
74 & 643 & 656.818218827044 & -13.8182188270438 \tabularnewline
75 & 718 & 656.698795918536 & 61.3012040814643 \tabularnewline
76 & 654 & 660.817144977097 & -6.81714497709675 \tabularnewline
77 & 632 & 661.228887024454 & -29.2288870244543 \tabularnewline
78 & 731 & 660.345744809836 & 70.6542551901643 \tabularnewline
79 & 392 & 665.067072981664 & -273.067072981664 \tabularnewline
80 & 344 & 650.418516913503 & -306.418516913503 \tabularnewline
81 & 792 & 633.108720200779 & 158.891279799221 \tabularnewline
82 & 852 & 641.432094301005 & 210.567905698995 \tabularnewline
83 & 649 & 653.141044929603 & -4.1410449296028 \tabularnewline
84 & 629 & 653.215220682074 & -24.2152206820745 \tabularnewline
85 & 685 & 652.135057907715 & 32.8649420922853 \tabularnewline
86 & 617 & 654.236699591676 & -37.2366995916761 \tabularnewline
87 & 715 & 652.439413155925 & 62.5605868440748 \tabularnewline
88 & 715 & 656.219345230191 & 58.7806547698086 \tabularnewline
89 & 629 & 659.958815069382 & -30.9588150693825 \tabularnewline
90 & 916 & 658.753801341889 & 257.246198658111 \tabularnewline
91 & 531 & 673.869126777617 & -142.869126777617 \tabularnewline
92 & 357 & 666.925312956701 & -309.925312956701 \tabularnewline
93 & 917 & 650.072395391087 & 266.927604608913 \tabularnewline
94 & 828 & 665.192729855297 & 162.807270144703 \tabularnewline
95 & 708 & 675.131228951394 & 32.8687710486062 \tabularnewline
96 & 858 & 678.127339615598 & 179.872660384402 \tabularnewline
97 & 775 & 689.583815810997 & 85.4161841890026 \tabularnewline
98 & 785 & 696.165468148517 & 88.8345318514827 \tabularnewline
99 & 1006 & 703.180269976991 & 302.819730023009 \tabularnewline
100 & 789 & 722.624820086708 & 66.3751799132915 \tabularnewline
101 & 734 & 729.454883237602 & 4.5451167623977 \tabularnewline
102 & 906 & 732.950486663161 & 173.049513336839 \tabularnewline
103 & 532 & 746.051323868417 & -214.051323868417 \tabularnewline
104 & 387 & 737.598759016399 & -350.598759016399 \tabularnewline
105 & 991 & 720.775079920901 & 270.224920079099 \tabularnewline
106 & 841 & 738.314206664376 & 102.685793335624 \tabularnewline
107 & 892 & 747.07043123568 & 144.92956876432 \tabularnewline
108 & 782 & 758.518272018273 & 23.4817279817269 \tabularnewline
109 & 811 & 763.457147560094 & 47.5428524399058 \tabularnewline
110 & 792 & 769.831343695208 & 22.168656304792 \tabularnewline
111 & 978 & 774.893827632781 & 203.106172367219 \tabularnewline
112 & 773 & 790.318551448994 & -17.3185514489943 \tabularnewline
113 & 796 & 793.762276282932 & 2.23772371706798 \tabularnewline
114 & 946 & 798.27092421185 & 147.72907578815 \tabularnewline
115 & 594 & 811.068285882463 & -217.068285882463 \tabularnewline
116 & 438 & 803.511212172007 & -365.511212172007 \tabularnewline
117 & 1023 & 786.897417407521 & 236.102582592479 \tabularnewline
118 & 868 & 803.511209413497 & 64.4887905865033 \tabularnewline
119 & 791 & 811.014841305141 & -20.014841305141 \tabularnewline
120 & 760 & 813.887994736602 & -53.887994736602 \tabularnewline
121 & 779 & 814.776935305262 & -35.7769353052615 \tabularnewline
122 & 852 & 816.54639480219 & 35.4536051978102 \tabularnewline
123 & 1001 & 822.270915196281 & 178.729084803719 \tabularnewline
124 & 734 & 836.250770898968 & -102.250770898968 \tabularnewline
125 & 996 & 834.734292528953 & 161.265707471047 \tabularnewline
126 & 869 & 847.93359077361 & 21.0664092263896 \tabularnewline
127 & 599 & 853.602073276905 & -254.602073276905 \tabularnewline
128 & 426 & 843.636262816966 & -417.636262816966 \tabularnewline
129 & 1138 & 823.678261809384 & 314.321738190616 \tabularnewline
130 & 1091 & 844.22244659085 & 246.77755340915 \tabularnewline
131 & 830 & 861.799349011515 & -31.799349011515 \tabularnewline
132 & 909 & 864.206758178201 & 44.793241821799 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187431&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]3[/C][C]647[/C][C]611[/C][C]36[/C][/ROW]
[ROW][C]4[/C][C]580[/C][C]610.04939130005[/C][C]-30.0493913000499[/C][/ROW]
[ROW][C]5[/C][C]614[/C][C]605.439294739149[/C][C]8.56070526085125[/C][/ROW]
[ROW][C]6[/C][C]636[/C][C]602.943253530979[/C][C]33.0567464690206[/C][/ROW]
[ROW][C]7[/C][C]388[/C][C]601.865620037366[/C][C]-213.865620037366[/C][/ROW]
[ROW][C]8[/C][C]356[/C][C]586.823625899921[/C][C]-230.823625899921[/C][/ROW]
[ROW][C]9[/C][C]639[/C][C]570.218967709284[/C][C]68.7810322907158[/C][/ROW]
[ROW][C]10[/C][C]753[/C][C]570.025417425646[/C][C]182.974582574354[/C][/ROW]
[ROW][C]11[/C][C]611[/C][C]576.524717121544[/C][C]34.4752828784557[/C][/ROW]
[ROW][C]12[/C][C]639[/C][C]575.081331259968[/C][C]63.9186687400324[/C][/ROW]
[ROW][C]13[/C][C]630[/C][C]575.410367808861[/C][C]54.5896321911388[/C][/ROW]
[ROW][C]14[/C][C]586[/C][C]575.386838145309[/C][C]10.6131618546913[/C][/ROW]
[ROW][C]15[/C][C]695[/C][C]573.012294981927[/C][C]121.987705018073[/C][/ROW]
[ROW][C]16[/C][C]552[/C][C]577.007670716844[/C][C]-25.0076707168445[/C][/ROW]
[ROW][C]17[/C][C]619[/C][C]572.975650670161[/C][C]46.024349329839[/C][/ROW]
[ROW][C]18[/C][C]681[/C][C]572.917466776198[/C][C]108.082533223802[/C][/ROW]
[ROW][C]19[/C][C]421[/C][C]576.520639761839[/C][C]-155.520639761839[/C][/ROW]
[ROW][C]20[/C][C]307[/C][C]565.419387840258[/C][C]-258.419387840258[/C][/ROW]
[ROW][C]21[/C][C]754[/C][C]548.026023745616[/C][C]205.973976254384[/C][/ROW]
[ROW][C]22[/C][C]690[/C][C]556.347712777284[/C][C]133.652287222716[/C][/ROW]
[ROW][C]23[/C][C]644[/C][C]561.127552926087[/C][C]82.8724470739128[/C][/ROW]
[ROW][C]24[/C][C]643[/C][C]563.389887660127[/C][C]79.6101123398729[/C][/ROW]
[ROW][C]25[/C][C]608[/C][C]565.697952916102[/C][C]42.3020470838983[/C][/ROW]
[ROW][C]26[/C][C]651[/C][C]566.10449805125[/C][C]84.8955019487498[/C][/ROW]
[ROW][C]27[/C][C]691[/C][C]569.053925309297[/C][C]121.946074690703[/C][/ROW]
[ROW][C]28[/C][C]627[/C][C]574.349647598219[/C][C]52.6503524017807[/C][/ROW]
[ROW][C]29[/C][C]634[/C][C]576.041107667321[/C][C]57.9588923326792[/C][/ROW]
[ROW][C]30[/C][C]731[/C][C]578.181812393691[/C][C]152.818187606309[/C][/ROW]
[ROW][C]31[/C][C]475[/C][C]585.88449139231[/C][C]-110.88449139231[/C][/ROW]
[ROW][C]32[/C][C]337[/C][C]579.002019143196[/C][C]-242.002019143196[/C][/ROW]
[ROW][C]33[/C][C]803[/C][C]564.345669512174[/C][C]238.654330487826[/C][/ROW]
[ROW][C]34[/C][C]722[/C][C]576.376035335251[/C][C]145.623964664749[/C][/ROW]
[ROW][C]35[/C][C]590[/C][C]583.776928429109[/C][C]6.22307157089097[/C][/ROW]
[ROW][C]36[/C][C]724[/C][C]583.648772565918[/C][C]140.351227434082[/C][/ROW]
[ROW][C]37[/C][C]627[/C][C]591.173582010969[/C][C]35.8264179890311[/C][/ROW]
[ROW][C]38[/C][C]696[/C][C]593.140026244272[/C][C]102.859973755728[/C][/ROW]
[ROW][C]39[/C][C]825[/C][C]599.022582109725[/C][C]225.977417890275[/C][/ROW]
[ROW][C]40[/C][C]677[/C][C]612.201179335512[/C][C]64.7988206644876[/C][/ROW]
[ROW][C]41[/C][C]656[/C][C]616.835388688819[/C][C]39.1646113111808[/C][/ROW]
[ROW][C]42[/C][C]785[/C][C]620.191276615271[/C][C]164.808723384729[/C][/ROW]
[ROW][C]43[/C][C]412[/C][C]630.809153682411[/C][C]-218.809153682411[/C][/ROW]
[ROW][C]44[/C][C]352[/C][C]620.048890050703[/C][C]-268.048890050703[/C][/ROW]
[ROW][C]45[/C][C]839[/C][C]605.87443662445[/C][C]233.12556337555[/C][/ROW]
[ROW][C]46[/C][C]729[/C][C]619.481999722219[/C][C]109.518000277781[/C][/ROW]
[ROW][C]47[/C][C]696[/C][C]626.703964779178[/C][C]69.2960352208215[/C][/ROW]
[ROW][C]48[/C][C]641[/C][C]631.94205570036[/C][C]9.05794429963953[/C][/ROW]
[ROW][C]49[/C][C]695[/C][C]633.944471353814[/C][C]61.0555286461856[/C][/ROW]
[ROW][C]50[/C][C]638[/C][C]638.932278507352[/C][C]-0.93227850735218[/C][/ROW]
[ROW][C]51[/C][C]762[/C][C]640.561789169964[/C][C]121.438210830036[/C][/ROW]
[ROW][C]52[/C][C]635[/C][C]649.154946604431[/C][C]-14.1549466044307[/C][/ROW]
[ROW][C]53[/C][C]721[/C][C]650.368273817159[/C][C]70.6317261828408[/C][/ROW]
[ROW][C]54[/C][C]854[/C][C]656.368765296016[/C][C]197.631234703984[/C][/ROW]
[ROW][C]55[/C][C]418[/C][C]669.796287045338[/C][C]-251.796287045338[/C][/ROW]
[ROW][C]56[/C][C]367[/C][C]658.190954289222[/C][C]-291.190954289222[/C][/ROW]
[ROW][C]57[/C][C]824[/C][C]643.639760390576[/C][C]180.360239609424[/C][/ROW]
[ROW][C]58[/C][C]687[/C][C]655.119572394754[/C][C]31.8804276052456[/C][/ROW]
[ROW][C]59[/C][C]601[/C][C]658.650506858202[/C][C]-57.6505068582023[/C][/ROW]
[ROW][C]60[/C][C]676[/C][C]657.173701871296[/C][C]18.826298128704[/C][/ROW]
[ROW][C]61[/C][C]740[/C][C]659.889525909883[/C][C]80.1104740901168[/C][/ROW]
[ROW][C]62[/C][C]691[/C][C]666.146685408069[/C][C]24.8533145919311[/C][/ROW]
[ROW][C]63[/C][C]683[/C][C]669.481924491284[/C][C]13.5180755087157[/C][/ROW]
[ROW][C]64[/C][C]594[/C][C]672.241286988173[/C][C]-78.2412869881726[/C][/ROW]
[ROW][C]65[/C][C]729[/C][C]669.814768512174[/C][C]59.1852314878258[/C][/ROW]
[ROW][C]66[/C][C]731[/C][C]674.993089890669[/C][C]56.0069101093308[/C][/ROW]
[ROW][C]67[/C][C]386[/C][C]680.155770514303[/C][C]-294.155770514303[/C][/ROW]
[ROW][C]68[/C][C]331[/C][C]665.540969286221[/C][C]-334.540969286221[/C][/ROW]
[ROW][C]69[/C][C]706[/C][C]647.805616304303[/C][C]58.1943836956971[/C][/ROW]
[ROW][C]70[/C][C]715[/C][C]651.493409134448[/C][C]63.5065908655519[/C][/ROW]
[ROW][C]71[/C][C]657[/C][C]655.646138828252[/C][C]1.35386117174835[/C][/ROW]
[ROW][C]72[/C][C]653[/C][C]656.438028754296[/C][C]-3.4380287542964[/C][/ROW]
[ROW][C]73[/C][C]642[/C][C]656.960909273839[/C][C]-14.9609092738386[/C][/ROW]
[ROW][C]74[/C][C]643[/C][C]656.818218827044[/C][C]-13.8182188270438[/C][/ROW]
[ROW][C]75[/C][C]718[/C][C]656.698795918536[/C][C]61.3012040814643[/C][/ROW]
[ROW][C]76[/C][C]654[/C][C]660.817144977097[/C][C]-6.81714497709675[/C][/ROW]
[ROW][C]77[/C][C]632[/C][C]661.228887024454[/C][C]-29.2288870244543[/C][/ROW]
[ROW][C]78[/C][C]731[/C][C]660.345744809836[/C][C]70.6542551901643[/C][/ROW]
[ROW][C]79[/C][C]392[/C][C]665.067072981664[/C][C]-273.067072981664[/C][/ROW]
[ROW][C]80[/C][C]344[/C][C]650.418516913503[/C][C]-306.418516913503[/C][/ROW]
[ROW][C]81[/C][C]792[/C][C]633.108720200779[/C][C]158.891279799221[/C][/ROW]
[ROW][C]82[/C][C]852[/C][C]641.432094301005[/C][C]210.567905698995[/C][/ROW]
[ROW][C]83[/C][C]649[/C][C]653.141044929603[/C][C]-4.1410449296028[/C][/ROW]
[ROW][C]84[/C][C]629[/C][C]653.215220682074[/C][C]-24.2152206820745[/C][/ROW]
[ROW][C]85[/C][C]685[/C][C]652.135057907715[/C][C]32.8649420922853[/C][/ROW]
[ROW][C]86[/C][C]617[/C][C]654.236699591676[/C][C]-37.2366995916761[/C][/ROW]
[ROW][C]87[/C][C]715[/C][C]652.439413155925[/C][C]62.5605868440748[/C][/ROW]
[ROW][C]88[/C][C]715[/C][C]656.219345230191[/C][C]58.7806547698086[/C][/ROW]
[ROW][C]89[/C][C]629[/C][C]659.958815069382[/C][C]-30.9588150693825[/C][/ROW]
[ROW][C]90[/C][C]916[/C][C]658.753801341889[/C][C]257.246198658111[/C][/ROW]
[ROW][C]91[/C][C]531[/C][C]673.869126777617[/C][C]-142.869126777617[/C][/ROW]
[ROW][C]92[/C][C]357[/C][C]666.925312956701[/C][C]-309.925312956701[/C][/ROW]
[ROW][C]93[/C][C]917[/C][C]650.072395391087[/C][C]266.927604608913[/C][/ROW]
[ROW][C]94[/C][C]828[/C][C]665.192729855297[/C][C]162.807270144703[/C][/ROW]
[ROW][C]95[/C][C]708[/C][C]675.131228951394[/C][C]32.8687710486062[/C][/ROW]
[ROW][C]96[/C][C]858[/C][C]678.127339615598[/C][C]179.872660384402[/C][/ROW]
[ROW][C]97[/C][C]775[/C][C]689.583815810997[/C][C]85.4161841890026[/C][/ROW]
[ROW][C]98[/C][C]785[/C][C]696.165468148517[/C][C]88.8345318514827[/C][/ROW]
[ROW][C]99[/C][C]1006[/C][C]703.180269976991[/C][C]302.819730023009[/C][/ROW]
[ROW][C]100[/C][C]789[/C][C]722.624820086708[/C][C]66.3751799132915[/C][/ROW]
[ROW][C]101[/C][C]734[/C][C]729.454883237602[/C][C]4.5451167623977[/C][/ROW]
[ROW][C]102[/C][C]906[/C][C]732.950486663161[/C][C]173.049513336839[/C][/ROW]
[ROW][C]103[/C][C]532[/C][C]746.051323868417[/C][C]-214.051323868417[/C][/ROW]
[ROW][C]104[/C][C]387[/C][C]737.598759016399[/C][C]-350.598759016399[/C][/ROW]
[ROW][C]105[/C][C]991[/C][C]720.775079920901[/C][C]270.224920079099[/C][/ROW]
[ROW][C]106[/C][C]841[/C][C]738.314206664376[/C][C]102.685793335624[/C][/ROW]
[ROW][C]107[/C][C]892[/C][C]747.07043123568[/C][C]144.92956876432[/C][/ROW]
[ROW][C]108[/C][C]782[/C][C]758.518272018273[/C][C]23.4817279817269[/C][/ROW]
[ROW][C]109[/C][C]811[/C][C]763.457147560094[/C][C]47.5428524399058[/C][/ROW]
[ROW][C]110[/C][C]792[/C][C]769.831343695208[/C][C]22.168656304792[/C][/ROW]
[ROW][C]111[/C][C]978[/C][C]774.893827632781[/C][C]203.106172367219[/C][/ROW]
[ROW][C]112[/C][C]773[/C][C]790.318551448994[/C][C]-17.3185514489943[/C][/ROW]
[ROW][C]113[/C][C]796[/C][C]793.762276282932[/C][C]2.23772371706798[/C][/ROW]
[ROW][C]114[/C][C]946[/C][C]798.27092421185[/C][C]147.72907578815[/C][/ROW]
[ROW][C]115[/C][C]594[/C][C]811.068285882463[/C][C]-217.068285882463[/C][/ROW]
[ROW][C]116[/C][C]438[/C][C]803.511212172007[/C][C]-365.511212172007[/C][/ROW]
[ROW][C]117[/C][C]1023[/C][C]786.897417407521[/C][C]236.102582592479[/C][/ROW]
[ROW][C]118[/C][C]868[/C][C]803.511209413497[/C][C]64.4887905865033[/C][/ROW]
[ROW][C]119[/C][C]791[/C][C]811.014841305141[/C][C]-20.014841305141[/C][/ROW]
[ROW][C]120[/C][C]760[/C][C]813.887994736602[/C][C]-53.887994736602[/C][/ROW]
[ROW][C]121[/C][C]779[/C][C]814.776935305262[/C][C]-35.7769353052615[/C][/ROW]
[ROW][C]122[/C][C]852[/C][C]816.54639480219[/C][C]35.4536051978102[/C][/ROW]
[ROW][C]123[/C][C]1001[/C][C]822.270915196281[/C][C]178.729084803719[/C][/ROW]
[ROW][C]124[/C][C]734[/C][C]836.250770898968[/C][C]-102.250770898968[/C][/ROW]
[ROW][C]125[/C][C]996[/C][C]834.734292528953[/C][C]161.265707471047[/C][/ROW]
[ROW][C]126[/C][C]869[/C][C]847.93359077361[/C][C]21.0664092263896[/C][/ROW]
[ROW][C]127[/C][C]599[/C][C]853.602073276905[/C][C]-254.602073276905[/C][/ROW]
[ROW][C]128[/C][C]426[/C][C]843.636262816966[/C][C]-417.636262816966[/C][/ROW]
[ROW][C]129[/C][C]1138[/C][C]823.678261809384[/C][C]314.321738190616[/C][/ROW]
[ROW][C]130[/C][C]1091[/C][C]844.22244659085[/C][C]246.77755340915[/C][/ROW]
[ROW][C]131[/C][C]830[/C][C]861.799349011515[/C][C]-31.799349011515[/C][/ROW]
[ROW][C]132[/C][C]909[/C][C]864.206758178201[/C][C]44.793241821799[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187431&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187431&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
364761136
4580610.04939130005-30.0493913000499
5614605.4392947391498.56070526085125
6636602.94325353097933.0567464690206
7388601.865620037366-213.865620037366
8356586.823625899921-230.823625899921
9639570.21896770928468.7810322907158
10753570.025417425646182.974582574354
11611576.52471712154434.4752828784557
12639575.08133125996863.9186687400324
13630575.41036780886154.5896321911388
14586575.38683814530910.6131618546913
15695573.012294981927121.987705018073
16552577.007670716844-25.0076707168445
17619572.97565067016146.024349329839
18681572.917466776198108.082533223802
19421576.520639761839-155.520639761839
20307565.419387840258-258.419387840258
21754548.026023745616205.973976254384
22690556.347712777284133.652287222716
23644561.12755292608782.8724470739128
24643563.38988766012779.6101123398729
25608565.69795291610242.3020470838983
26651566.1044980512584.8955019487498
27691569.053925309297121.946074690703
28627574.34964759821952.6503524017807
29634576.04110766732157.9588923326792
30731578.181812393691152.818187606309
31475585.88449139231-110.88449139231
32337579.002019143196-242.002019143196
33803564.345669512174238.654330487826
34722576.376035335251145.623964664749
35590583.7769284291096.22307157089097
36724583.648772565918140.351227434082
37627591.17358201096935.8264179890311
38696593.140026244272102.859973755728
39825599.022582109725225.977417890275
40677612.20117933551264.7988206644876
41656616.83538868881939.1646113111808
42785620.191276615271164.808723384729
43412630.809153682411-218.809153682411
44352620.048890050703-268.048890050703
45839605.87443662445233.12556337555
46729619.481999722219109.518000277781
47696626.70396477917869.2960352208215
48641631.942055700369.05794429963953
49695633.94447135381461.0555286461856
50638638.932278507352-0.93227850735218
51762640.561789169964121.438210830036
52635649.154946604431-14.1549466044307
53721650.36827381715970.6317261828408
54854656.368765296016197.631234703984
55418669.796287045338-251.796287045338
56367658.190954289222-291.190954289222
57824643.639760390576180.360239609424
58687655.11957239475431.8804276052456
59601658.650506858202-57.6505068582023
60676657.17370187129618.826298128704
61740659.88952590988380.1104740901168
62691666.14668540806924.8533145919311
63683669.48192449128413.5180755087157
64594672.241286988173-78.2412869881726
65729669.81476851217459.1852314878258
66731674.99308989066956.0069101093308
67386680.155770514303-294.155770514303
68331665.540969286221-334.540969286221
69706647.80561630430358.1943836956971
70715651.49340913444863.5065908655519
71657655.6461388282521.35386117174835
72653656.438028754296-3.4380287542964
73642656.960909273839-14.9609092738386
74643656.818218827044-13.8182188270438
75718656.69879591853661.3012040814643
76654660.817144977097-6.81714497709675
77632661.228887024454-29.2288870244543
78731660.34574480983670.6542551901643
79392665.067072981664-273.067072981664
80344650.418516913503-306.418516913503
81792633.108720200779158.891279799221
82852641.432094301005210.567905698995
83649653.141044929603-4.1410449296028
84629653.215220682074-24.2152206820745
85685652.13505790771532.8649420922853
86617654.236699591676-37.2366995916761
87715652.43941315592562.5605868440748
88715656.21934523019158.7806547698086
89629659.958815069382-30.9588150693825
90916658.753801341889257.246198658111
91531673.869126777617-142.869126777617
92357666.925312956701-309.925312956701
93917650.072395391087266.927604608913
94828665.192729855297162.807270144703
95708675.13122895139432.8687710486062
96858678.127339615598179.872660384402
97775689.58381581099785.4161841890026
98785696.16546814851788.8345318514827
991006703.180269976991302.819730023009
100789722.62482008670866.3751799132915
101734729.4548832376024.5451167623977
102906732.950486663161173.049513336839
103532746.051323868417-214.051323868417
104387737.598759016399-350.598759016399
105991720.775079920901270.224920079099
106841738.314206664376102.685793335624
107892747.07043123568144.92956876432
108782758.51827201827323.4817279817269
109811763.45714756009447.5428524399058
110792769.83134369520822.168656304792
111978774.893827632781203.106172367219
112773790.318551448994-17.3185514489943
113796793.7622762829322.23772371706798
114946798.27092421185147.72907578815
115594811.068285882463-217.068285882463
116438803.511212172007-365.511212172007
1171023786.897417407521236.102582592479
118868803.51120941349764.4887905865033
119791811.014841305141-20.014841305141
120760813.887994736602-53.887994736602
121779814.776935305262-35.7769353052615
122852816.5463948021935.4536051978102
1231001822.270915196281178.729084803719
124734836.250770898968-102.250770898968
125996834.734292528953161.265707471047
126869847.9335907736121.0664092263896
127599853.602073276905-254.602073276905
128426843.636262816966-417.636262816966
1291138823.678261809384314.321738190616
1301091844.22244659085246.77755340915
131830861.799349011515-31.799349011515
132909864.20675817820144.793241821799







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
133870.885585208305569.7318332345671172.03933718204
134875.139542469557573.498204049261176.78088088985
135879.393499730808577.2164696276591181.57052983396
136883.64745699206580.8845488516941186.41036513243
137887.901414253312584.5004009082971191.30242759833
138892.155371514563588.0620283363421196.24871469278
139896.409328775815591.5674800590971201.25117749253
140900.663286037066595.0148543810631206.31171769307
141904.917243298318598.4023019277691211.43218466887
142909.17120055957601.7280285070541216.61437261209
143913.425157820821604.9902978704961221.86001777115
144917.679115082073608.1874343541081227.17079581004

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
133 & 870.885585208305 & 569.731833234567 & 1172.03933718204 \tabularnewline
134 & 875.139542469557 & 573.49820404926 & 1176.78088088985 \tabularnewline
135 & 879.393499730808 & 577.216469627659 & 1181.57052983396 \tabularnewline
136 & 883.64745699206 & 580.884548851694 & 1186.41036513243 \tabularnewline
137 & 887.901414253312 & 584.500400908297 & 1191.30242759833 \tabularnewline
138 & 892.155371514563 & 588.062028336342 & 1196.24871469278 \tabularnewline
139 & 896.409328775815 & 591.567480059097 & 1201.25117749253 \tabularnewline
140 & 900.663286037066 & 595.014854381063 & 1206.31171769307 \tabularnewline
141 & 904.917243298318 & 598.402301927769 & 1211.43218466887 \tabularnewline
142 & 909.17120055957 & 601.728028507054 & 1216.61437261209 \tabularnewline
143 & 913.425157820821 & 604.990297870496 & 1221.86001777115 \tabularnewline
144 & 917.679115082073 & 608.187434354108 & 1227.17079581004 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187431&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]870.885585208305[/C][C]569.731833234567[/C][C]1172.03933718204[/C][/ROW]
[ROW][C]134[/C][C]875.139542469557[/C][C]573.49820404926[/C][C]1176.78088088985[/C][/ROW]
[ROW][C]135[/C][C]879.393499730808[/C][C]577.216469627659[/C][C]1181.57052983396[/C][/ROW]
[ROW][C]136[/C][C]883.64745699206[/C][C]580.884548851694[/C][C]1186.41036513243[/C][/ROW]
[ROW][C]137[/C][C]887.901414253312[/C][C]584.500400908297[/C][C]1191.30242759833[/C][/ROW]
[ROW][C]138[/C][C]892.155371514563[/C][C]588.062028336342[/C][C]1196.24871469278[/C][/ROW]
[ROW][C]139[/C][C]896.409328775815[/C][C]591.567480059097[/C][C]1201.25117749253[/C][/ROW]
[ROW][C]140[/C][C]900.663286037066[/C][C]595.014854381063[/C][C]1206.31171769307[/C][/ROW]
[ROW][C]141[/C][C]904.917243298318[/C][C]598.402301927769[/C][C]1211.43218466887[/C][/ROW]
[ROW][C]142[/C][C]909.17120055957[/C][C]601.728028507054[/C][C]1216.61437261209[/C][/ROW]
[ROW][C]143[/C][C]913.425157820821[/C][C]604.990297870496[/C][C]1221.86001777115[/C][/ROW]
[ROW][C]144[/C][C]917.679115082073[/C][C]608.187434354108[/C][C]1227.17079581004[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187431&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187431&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
133870.885585208305569.7318332345671172.03933718204
134875.139542469557573.498204049261176.78088088985
135879.393499730808577.2164696276591181.57052983396
136883.64745699206580.8845488516941186.41036513243
137887.901414253312584.5004009082971191.30242759833
138892.155371514563588.0620283363421196.24871469278
139896.409328775815591.5674800590971201.25117749253
140900.663286037066595.0148543810631206.31171769307
141904.917243298318598.4023019277691211.43218466887
142909.17120055957601.7280285070541216.61437261209
143913.425157820821604.9902978704961221.86001777115
144917.679115082073608.1874343541081227.17079581004



Parameters (Session):
par1 = 12 ; par2 = Double ; par3 = additive ;
Parameters (R input):
par1 = 12 ; par2 = Double ; 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')