Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationThu, 20 Dec 2012 10:47:04 -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/Dec/20/t1356018477gx64pvys53uef6h.htm/, Retrieved Thu, 28 Mar 2024 17:14:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202799, Retrieved Thu, 28 Mar 2024 17:14:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMP         [Spectral Analysis] [Births] [2010-11-29 09:38:20] [b98453cac15ba1066b407e146608df68]
- R  D          [Spectral Analysis] [WS9 3.2 CP d=0, D=0] [2010-12-07 10:39:32] [afe9379cca749d06b3d6872e02cc47ed]
-   PD            [Spectral Analysis] [Apple Inc - CP d=...] [2010-12-14 16:14:30] [afe9379cca749d06b3d6872e02cc47ed]
- RMPD              [Notched Boxplots] [] [2012-12-15 15:08:39] [d1865ed705b6ad9ba3d459a02c528b22]
- RM                    [Exponential Smoothing] [] [2012-12-20 15:47:04] [14d0a7ecb926325afa0eb6a607fbc7a0] [Current]
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Dataseries X:
26.81
28.24
27.58
27.98
26.81
28.24
27.58
27.98
27.84
27.49
26.97
27.71
27.46
27.04
28.00
27.32
26.36
26.15
25.94
24.00
24.32
23.10
22.92
23.56
22.17
22.36
19.86
20.07
19.21
19.99
20.47
21.17
21.25
21.18
21.21
21.11
21.94
22.56
23.23
19.50
19.32
19.00
18.98
19.88
19.48
19.52
19.52
19.75
19.64
20.23
20.40
20.91
21.95
21.83
22.27
21.99
21.66
20.32
20.62
20.28
20.79
22.86
22.59
23.29
21.87
21.52
22.00
27.84
27.49
26.97
27.71
27.46
27.04
28.00
27.32
26.36
26.15
25.94
24.00
24.32
23.10
22.92
23.56
22.17
22.36
19.86
20.07
19.21
19.99
20.47
21.17
21.25
21.18
21.21
21.11
21.94
22.56
23.23
19.50
19.32
19.00
18.98
19.88
19.48
19.52
19.52
19.75
19.64
20.23
20.40
20.91
21.95
21.83
22.27
21.99
21.66
20.32
20.62
20.28
20.79
22.86
22.59
23.29
21.87
21.52
22.00




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=202799&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=202799&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202799&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
alpha0.917553089744478
betaFALSE
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.917553089744478 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202799&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]0.917553089744478[/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=202799&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202799&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
alpha0.917553089744478
betaFALSE
gammaFALSE







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
228.2426.811.43
327.5828.1221009183346-0.542100918334604
427.9827.62469454576340.355305454236632
526.8127.9507061631013-1.14070616310126
628.2426.90404769865711.33595230134287
727.5828.1298548605055-0.549854860505526
827.9827.62533383433770.354666165662341
927.8427.950758870469-0.110758870468967
1027.4927.8491317266536-0.359131726653558
1126.9727.5196093012373-0.549609301237314
1227.7127.01531358873470.694686411265288
1327.4627.6527252517947-0.19272525179468
1427.0427.4758896015387-0.43588960153869
152827.07593775085940.924062249140626
1627.3227.9238139226746-0.603813922674586
1726.3627.3697825922938-1.00978259229379
1826.1526.4432534547644-0.293253454764436
1925.9426.1741778412671-0.234177841267083
202425.9593072394628-1.95930723946278
2124.3224.1615388281350.158461171865017
2223.124.3069353659843-1.20693536598426
2322.9223.1995080918035-0.279508091803521
2423.5622.94304457856060.616955421439378
2522.1723.5091339317369-1.33913393173693
2622.3622.280407455090.0795925449099606
2719.8622.3534378405928-2.4934378405928
2820.0720.06557624587110.00442375412892204
2919.2120.0696352751403-0.859635275140338
3019.9919.2808742723820.709125727618023
3120.4719.93153477477520.538465225224805
3221.1720.42560520590020.744394794099833
3321.2521.10862694921620.141373050783827
3421.1821.2383442287695-0.058344228769478
3521.2121.18481030139330.0251896986067166
3621.1121.2079231871796-0.0979231871796102
3721.9421.11807346422530.821926535774669
3822.5621.87223469666840.687765303331645
3923.2322.50329587575940.726704124240648
4019.523.1700854902864-3.67008549028641
4119.3219.8025872090477-0.482587209047736
421919.3597878243148-0.359787824314822
4318.9819.0296633944623-0.0496633944623142
4419.8818.98409459342620.895905406573778
4519.4819.8061353673468-0.326135367346772
4619.5219.50688885336280.0131111466372076
4719.5219.51891902646990.00108097353014358
4819.7519.51991087707240.23008912292763
4919.6419.7310298627312-0.0910298627312116
5020.2319.64750513092320.582494869076829
5120.420.18197509780490.218024902195076
5220.9120.38202452045530.527975479544747
5321.9520.86647005302091.08352994697914
5421.8321.8606663037022-0.0306663037022403
5522.2721.83252834198920.437471658010793
5621.9922.2339318134726-0.243931813472649
5721.6622.0101114243338-0.350111424333843
5820.3221.6888656051815-1.36886560518149
5920.6220.43285873970230.187141260297732
6020.2820.6045707813071-0.324570781307127
6120.7920.3067598580780.483240141922003
6222.8620.75015834338712.10984165661291
6322.5922.6860500742839-0.0960500742838732
6423.2922.59791903185450.692080968145479
6521.8723.2329400625298-1.36294006252975
6621.5221.982370197019-0.462370197019048
672221.55812099413850.441879005861544
6827.8421.96356844125995.87643155874007
6927.4927.35550637465380.134493625346156
7026.9727.4789114161411-0.508911416141146
7127.7127.01195817385460.698041826145403
7227.4627.6524486082052-0.19244860820519
7327.0427.4758667931295-0.435866793129495
742827.07593587037650.924064129623492
7527.3227.9238137676346-0.603813767634584
7626.3627.3697825795112-1.00978257951122
7726.1526.4432534537106-0.293253453710552
7825.9426.1741778411802-0.234177841180195
792425.9593072394556-1.95930723945562
8024.3224.16153882813440.158461171865607
8123.124.3069353659842-1.20693536598421
8222.9223.1995080918035-0.279508091803514
8323.5622.94304457856060.616955421439382
8422.1723.5091339317369-1.33913393173693
8522.3622.280407455090.0795925449099606
8619.8622.3534378405928-2.4934378405928
8720.0720.06557624587110.00442375412892204
8819.2120.0696352751403-0.859635275140338
8919.9919.2808742723820.709125727618023
9020.4719.93153477477520.538465225224805
9121.1720.42560520590020.744394794099833
9221.2521.10862694921620.141373050783827
9321.1821.2383442287695-0.058344228769478
9421.2121.18481030139330.0251896986067166
9521.1121.2079231871796-0.0979231871796102
9621.9421.11807346422530.821926535774669
9722.5621.87223469666840.687765303331645
9823.2322.50329587575940.726704124240648
9919.523.1700854902864-3.67008549028641
10019.3219.8025872090477-0.482587209047736
1011919.3597878243148-0.359787824314822
10218.9819.0296633944623-0.0496633944623142
10319.8818.98409459342620.895905406573778
10419.4819.8061353673468-0.326135367346772
10519.5219.50688885336280.0131111466372076
10619.5219.51891902646990.00108097353014358
10719.7519.51991087707240.23008912292763
10819.6419.7310298627312-0.0910298627312116
10920.2319.64750513092320.582494869076829
11020.420.18197509780490.218024902195076
11120.9120.38202452045530.527975479544747
11221.9520.86647005302091.08352994697914
11321.8321.8606663037022-0.0306663037022403
11422.2721.83252834198920.437471658010793
11521.9922.2339318134726-0.243931813472649
11621.6622.0101114243338-0.350111424333843
11720.3221.6888656051815-1.36886560518149
11820.6220.43285873970230.187141260297732
11920.2820.6045707813071-0.324570781307127
12020.7920.3067598580780.483240141922003
12122.8620.75015834338712.10984165661291
12222.5922.6860500742839-0.0960500742838732
12323.2922.59791903185450.692080968145479
12421.8723.2329400625298-1.36294006252975
12521.5221.982370197019-0.462370197019048
1262221.55812099413850.441879005861544

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 28.24 & 26.81 & 1.43 \tabularnewline
3 & 27.58 & 28.1221009183346 & -0.542100918334604 \tabularnewline
4 & 27.98 & 27.6246945457634 & 0.355305454236632 \tabularnewline
5 & 26.81 & 27.9507061631013 & -1.14070616310126 \tabularnewline
6 & 28.24 & 26.9040476986571 & 1.33595230134287 \tabularnewline
7 & 27.58 & 28.1298548605055 & -0.549854860505526 \tabularnewline
8 & 27.98 & 27.6253338343377 & 0.354666165662341 \tabularnewline
9 & 27.84 & 27.950758870469 & -0.110758870468967 \tabularnewline
10 & 27.49 & 27.8491317266536 & -0.359131726653558 \tabularnewline
11 & 26.97 & 27.5196093012373 & -0.549609301237314 \tabularnewline
12 & 27.71 & 27.0153135887347 & 0.694686411265288 \tabularnewline
13 & 27.46 & 27.6527252517947 & -0.19272525179468 \tabularnewline
14 & 27.04 & 27.4758896015387 & -0.43588960153869 \tabularnewline
15 & 28 & 27.0759377508594 & 0.924062249140626 \tabularnewline
16 & 27.32 & 27.9238139226746 & -0.603813922674586 \tabularnewline
17 & 26.36 & 27.3697825922938 & -1.00978259229379 \tabularnewline
18 & 26.15 & 26.4432534547644 & -0.293253454764436 \tabularnewline
19 & 25.94 & 26.1741778412671 & -0.234177841267083 \tabularnewline
20 & 24 & 25.9593072394628 & -1.95930723946278 \tabularnewline
21 & 24.32 & 24.161538828135 & 0.158461171865017 \tabularnewline
22 & 23.1 & 24.3069353659843 & -1.20693536598426 \tabularnewline
23 & 22.92 & 23.1995080918035 & -0.279508091803521 \tabularnewline
24 & 23.56 & 22.9430445785606 & 0.616955421439378 \tabularnewline
25 & 22.17 & 23.5091339317369 & -1.33913393173693 \tabularnewline
26 & 22.36 & 22.28040745509 & 0.0795925449099606 \tabularnewline
27 & 19.86 & 22.3534378405928 & -2.4934378405928 \tabularnewline
28 & 20.07 & 20.0655762458711 & 0.00442375412892204 \tabularnewline
29 & 19.21 & 20.0696352751403 & -0.859635275140338 \tabularnewline
30 & 19.99 & 19.280874272382 & 0.709125727618023 \tabularnewline
31 & 20.47 & 19.9315347747752 & 0.538465225224805 \tabularnewline
32 & 21.17 & 20.4256052059002 & 0.744394794099833 \tabularnewline
33 & 21.25 & 21.1086269492162 & 0.141373050783827 \tabularnewline
34 & 21.18 & 21.2383442287695 & -0.058344228769478 \tabularnewline
35 & 21.21 & 21.1848103013933 & 0.0251896986067166 \tabularnewline
36 & 21.11 & 21.2079231871796 & -0.0979231871796102 \tabularnewline
37 & 21.94 & 21.1180734642253 & 0.821926535774669 \tabularnewline
38 & 22.56 & 21.8722346966684 & 0.687765303331645 \tabularnewline
39 & 23.23 & 22.5032958757594 & 0.726704124240648 \tabularnewline
40 & 19.5 & 23.1700854902864 & -3.67008549028641 \tabularnewline
41 & 19.32 & 19.8025872090477 & -0.482587209047736 \tabularnewline
42 & 19 & 19.3597878243148 & -0.359787824314822 \tabularnewline
43 & 18.98 & 19.0296633944623 & -0.0496633944623142 \tabularnewline
44 & 19.88 & 18.9840945934262 & 0.895905406573778 \tabularnewline
45 & 19.48 & 19.8061353673468 & -0.326135367346772 \tabularnewline
46 & 19.52 & 19.5068888533628 & 0.0131111466372076 \tabularnewline
47 & 19.52 & 19.5189190264699 & 0.00108097353014358 \tabularnewline
48 & 19.75 & 19.5199108770724 & 0.23008912292763 \tabularnewline
49 & 19.64 & 19.7310298627312 & -0.0910298627312116 \tabularnewline
50 & 20.23 & 19.6475051309232 & 0.582494869076829 \tabularnewline
51 & 20.4 & 20.1819750978049 & 0.218024902195076 \tabularnewline
52 & 20.91 & 20.3820245204553 & 0.527975479544747 \tabularnewline
53 & 21.95 & 20.8664700530209 & 1.08352994697914 \tabularnewline
54 & 21.83 & 21.8606663037022 & -0.0306663037022403 \tabularnewline
55 & 22.27 & 21.8325283419892 & 0.437471658010793 \tabularnewline
56 & 21.99 & 22.2339318134726 & -0.243931813472649 \tabularnewline
57 & 21.66 & 22.0101114243338 & -0.350111424333843 \tabularnewline
58 & 20.32 & 21.6888656051815 & -1.36886560518149 \tabularnewline
59 & 20.62 & 20.4328587397023 & 0.187141260297732 \tabularnewline
60 & 20.28 & 20.6045707813071 & -0.324570781307127 \tabularnewline
61 & 20.79 & 20.306759858078 & 0.483240141922003 \tabularnewline
62 & 22.86 & 20.7501583433871 & 2.10984165661291 \tabularnewline
63 & 22.59 & 22.6860500742839 & -0.0960500742838732 \tabularnewline
64 & 23.29 & 22.5979190318545 & 0.692080968145479 \tabularnewline
65 & 21.87 & 23.2329400625298 & -1.36294006252975 \tabularnewline
66 & 21.52 & 21.982370197019 & -0.462370197019048 \tabularnewline
67 & 22 & 21.5581209941385 & 0.441879005861544 \tabularnewline
68 & 27.84 & 21.9635684412599 & 5.87643155874007 \tabularnewline
69 & 27.49 & 27.3555063746538 & 0.134493625346156 \tabularnewline
70 & 26.97 & 27.4789114161411 & -0.508911416141146 \tabularnewline
71 & 27.71 & 27.0119581738546 & 0.698041826145403 \tabularnewline
72 & 27.46 & 27.6524486082052 & -0.19244860820519 \tabularnewline
73 & 27.04 & 27.4758667931295 & -0.435866793129495 \tabularnewline
74 & 28 & 27.0759358703765 & 0.924064129623492 \tabularnewline
75 & 27.32 & 27.9238137676346 & -0.603813767634584 \tabularnewline
76 & 26.36 & 27.3697825795112 & -1.00978257951122 \tabularnewline
77 & 26.15 & 26.4432534537106 & -0.293253453710552 \tabularnewline
78 & 25.94 & 26.1741778411802 & -0.234177841180195 \tabularnewline
79 & 24 & 25.9593072394556 & -1.95930723945562 \tabularnewline
80 & 24.32 & 24.1615388281344 & 0.158461171865607 \tabularnewline
81 & 23.1 & 24.3069353659842 & -1.20693536598421 \tabularnewline
82 & 22.92 & 23.1995080918035 & -0.279508091803514 \tabularnewline
83 & 23.56 & 22.9430445785606 & 0.616955421439382 \tabularnewline
84 & 22.17 & 23.5091339317369 & -1.33913393173693 \tabularnewline
85 & 22.36 & 22.28040745509 & 0.0795925449099606 \tabularnewline
86 & 19.86 & 22.3534378405928 & -2.4934378405928 \tabularnewline
87 & 20.07 & 20.0655762458711 & 0.00442375412892204 \tabularnewline
88 & 19.21 & 20.0696352751403 & -0.859635275140338 \tabularnewline
89 & 19.99 & 19.280874272382 & 0.709125727618023 \tabularnewline
90 & 20.47 & 19.9315347747752 & 0.538465225224805 \tabularnewline
91 & 21.17 & 20.4256052059002 & 0.744394794099833 \tabularnewline
92 & 21.25 & 21.1086269492162 & 0.141373050783827 \tabularnewline
93 & 21.18 & 21.2383442287695 & -0.058344228769478 \tabularnewline
94 & 21.21 & 21.1848103013933 & 0.0251896986067166 \tabularnewline
95 & 21.11 & 21.2079231871796 & -0.0979231871796102 \tabularnewline
96 & 21.94 & 21.1180734642253 & 0.821926535774669 \tabularnewline
97 & 22.56 & 21.8722346966684 & 0.687765303331645 \tabularnewline
98 & 23.23 & 22.5032958757594 & 0.726704124240648 \tabularnewline
99 & 19.5 & 23.1700854902864 & -3.67008549028641 \tabularnewline
100 & 19.32 & 19.8025872090477 & -0.482587209047736 \tabularnewline
101 & 19 & 19.3597878243148 & -0.359787824314822 \tabularnewline
102 & 18.98 & 19.0296633944623 & -0.0496633944623142 \tabularnewline
103 & 19.88 & 18.9840945934262 & 0.895905406573778 \tabularnewline
104 & 19.48 & 19.8061353673468 & -0.326135367346772 \tabularnewline
105 & 19.52 & 19.5068888533628 & 0.0131111466372076 \tabularnewline
106 & 19.52 & 19.5189190264699 & 0.00108097353014358 \tabularnewline
107 & 19.75 & 19.5199108770724 & 0.23008912292763 \tabularnewline
108 & 19.64 & 19.7310298627312 & -0.0910298627312116 \tabularnewline
109 & 20.23 & 19.6475051309232 & 0.582494869076829 \tabularnewline
110 & 20.4 & 20.1819750978049 & 0.218024902195076 \tabularnewline
111 & 20.91 & 20.3820245204553 & 0.527975479544747 \tabularnewline
112 & 21.95 & 20.8664700530209 & 1.08352994697914 \tabularnewline
113 & 21.83 & 21.8606663037022 & -0.0306663037022403 \tabularnewline
114 & 22.27 & 21.8325283419892 & 0.437471658010793 \tabularnewline
115 & 21.99 & 22.2339318134726 & -0.243931813472649 \tabularnewline
116 & 21.66 & 22.0101114243338 & -0.350111424333843 \tabularnewline
117 & 20.32 & 21.6888656051815 & -1.36886560518149 \tabularnewline
118 & 20.62 & 20.4328587397023 & 0.187141260297732 \tabularnewline
119 & 20.28 & 20.6045707813071 & -0.324570781307127 \tabularnewline
120 & 20.79 & 20.306759858078 & 0.483240141922003 \tabularnewline
121 & 22.86 & 20.7501583433871 & 2.10984165661291 \tabularnewline
122 & 22.59 & 22.6860500742839 & -0.0960500742838732 \tabularnewline
123 & 23.29 & 22.5979190318545 & 0.692080968145479 \tabularnewline
124 & 21.87 & 23.2329400625298 & -1.36294006252975 \tabularnewline
125 & 21.52 & 21.982370197019 & -0.462370197019048 \tabularnewline
126 & 22 & 21.5581209941385 & 0.441879005861544 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202799&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]28.24[/C][C]26.81[/C][C]1.43[/C][/ROW]
[ROW][C]3[/C][C]27.58[/C][C]28.1221009183346[/C][C]-0.542100918334604[/C][/ROW]
[ROW][C]4[/C][C]27.98[/C][C]27.6246945457634[/C][C]0.355305454236632[/C][/ROW]
[ROW][C]5[/C][C]26.81[/C][C]27.9507061631013[/C][C]-1.14070616310126[/C][/ROW]
[ROW][C]6[/C][C]28.24[/C][C]26.9040476986571[/C][C]1.33595230134287[/C][/ROW]
[ROW][C]7[/C][C]27.58[/C][C]28.1298548605055[/C][C]-0.549854860505526[/C][/ROW]
[ROW][C]8[/C][C]27.98[/C][C]27.6253338343377[/C][C]0.354666165662341[/C][/ROW]
[ROW][C]9[/C][C]27.84[/C][C]27.950758870469[/C][C]-0.110758870468967[/C][/ROW]
[ROW][C]10[/C][C]27.49[/C][C]27.8491317266536[/C][C]-0.359131726653558[/C][/ROW]
[ROW][C]11[/C][C]26.97[/C][C]27.5196093012373[/C][C]-0.549609301237314[/C][/ROW]
[ROW][C]12[/C][C]27.71[/C][C]27.0153135887347[/C][C]0.694686411265288[/C][/ROW]
[ROW][C]13[/C][C]27.46[/C][C]27.6527252517947[/C][C]-0.19272525179468[/C][/ROW]
[ROW][C]14[/C][C]27.04[/C][C]27.4758896015387[/C][C]-0.43588960153869[/C][/ROW]
[ROW][C]15[/C][C]28[/C][C]27.0759377508594[/C][C]0.924062249140626[/C][/ROW]
[ROW][C]16[/C][C]27.32[/C][C]27.9238139226746[/C][C]-0.603813922674586[/C][/ROW]
[ROW][C]17[/C][C]26.36[/C][C]27.3697825922938[/C][C]-1.00978259229379[/C][/ROW]
[ROW][C]18[/C][C]26.15[/C][C]26.4432534547644[/C][C]-0.293253454764436[/C][/ROW]
[ROW][C]19[/C][C]25.94[/C][C]26.1741778412671[/C][C]-0.234177841267083[/C][/ROW]
[ROW][C]20[/C][C]24[/C][C]25.9593072394628[/C][C]-1.95930723946278[/C][/ROW]
[ROW][C]21[/C][C]24.32[/C][C]24.161538828135[/C][C]0.158461171865017[/C][/ROW]
[ROW][C]22[/C][C]23.1[/C][C]24.3069353659843[/C][C]-1.20693536598426[/C][/ROW]
[ROW][C]23[/C][C]22.92[/C][C]23.1995080918035[/C][C]-0.279508091803521[/C][/ROW]
[ROW][C]24[/C][C]23.56[/C][C]22.9430445785606[/C][C]0.616955421439378[/C][/ROW]
[ROW][C]25[/C][C]22.17[/C][C]23.5091339317369[/C][C]-1.33913393173693[/C][/ROW]
[ROW][C]26[/C][C]22.36[/C][C]22.28040745509[/C][C]0.0795925449099606[/C][/ROW]
[ROW][C]27[/C][C]19.86[/C][C]22.3534378405928[/C][C]-2.4934378405928[/C][/ROW]
[ROW][C]28[/C][C]20.07[/C][C]20.0655762458711[/C][C]0.00442375412892204[/C][/ROW]
[ROW][C]29[/C][C]19.21[/C][C]20.0696352751403[/C][C]-0.859635275140338[/C][/ROW]
[ROW][C]30[/C][C]19.99[/C][C]19.280874272382[/C][C]0.709125727618023[/C][/ROW]
[ROW][C]31[/C][C]20.47[/C][C]19.9315347747752[/C][C]0.538465225224805[/C][/ROW]
[ROW][C]32[/C][C]21.17[/C][C]20.4256052059002[/C][C]0.744394794099833[/C][/ROW]
[ROW][C]33[/C][C]21.25[/C][C]21.1086269492162[/C][C]0.141373050783827[/C][/ROW]
[ROW][C]34[/C][C]21.18[/C][C]21.2383442287695[/C][C]-0.058344228769478[/C][/ROW]
[ROW][C]35[/C][C]21.21[/C][C]21.1848103013933[/C][C]0.0251896986067166[/C][/ROW]
[ROW][C]36[/C][C]21.11[/C][C]21.2079231871796[/C][C]-0.0979231871796102[/C][/ROW]
[ROW][C]37[/C][C]21.94[/C][C]21.1180734642253[/C][C]0.821926535774669[/C][/ROW]
[ROW][C]38[/C][C]22.56[/C][C]21.8722346966684[/C][C]0.687765303331645[/C][/ROW]
[ROW][C]39[/C][C]23.23[/C][C]22.5032958757594[/C][C]0.726704124240648[/C][/ROW]
[ROW][C]40[/C][C]19.5[/C][C]23.1700854902864[/C][C]-3.67008549028641[/C][/ROW]
[ROW][C]41[/C][C]19.32[/C][C]19.8025872090477[/C][C]-0.482587209047736[/C][/ROW]
[ROW][C]42[/C][C]19[/C][C]19.3597878243148[/C][C]-0.359787824314822[/C][/ROW]
[ROW][C]43[/C][C]18.98[/C][C]19.0296633944623[/C][C]-0.0496633944623142[/C][/ROW]
[ROW][C]44[/C][C]19.88[/C][C]18.9840945934262[/C][C]0.895905406573778[/C][/ROW]
[ROW][C]45[/C][C]19.48[/C][C]19.8061353673468[/C][C]-0.326135367346772[/C][/ROW]
[ROW][C]46[/C][C]19.52[/C][C]19.5068888533628[/C][C]0.0131111466372076[/C][/ROW]
[ROW][C]47[/C][C]19.52[/C][C]19.5189190264699[/C][C]0.00108097353014358[/C][/ROW]
[ROW][C]48[/C][C]19.75[/C][C]19.5199108770724[/C][C]0.23008912292763[/C][/ROW]
[ROW][C]49[/C][C]19.64[/C][C]19.7310298627312[/C][C]-0.0910298627312116[/C][/ROW]
[ROW][C]50[/C][C]20.23[/C][C]19.6475051309232[/C][C]0.582494869076829[/C][/ROW]
[ROW][C]51[/C][C]20.4[/C][C]20.1819750978049[/C][C]0.218024902195076[/C][/ROW]
[ROW][C]52[/C][C]20.91[/C][C]20.3820245204553[/C][C]0.527975479544747[/C][/ROW]
[ROW][C]53[/C][C]21.95[/C][C]20.8664700530209[/C][C]1.08352994697914[/C][/ROW]
[ROW][C]54[/C][C]21.83[/C][C]21.8606663037022[/C][C]-0.0306663037022403[/C][/ROW]
[ROW][C]55[/C][C]22.27[/C][C]21.8325283419892[/C][C]0.437471658010793[/C][/ROW]
[ROW][C]56[/C][C]21.99[/C][C]22.2339318134726[/C][C]-0.243931813472649[/C][/ROW]
[ROW][C]57[/C][C]21.66[/C][C]22.0101114243338[/C][C]-0.350111424333843[/C][/ROW]
[ROW][C]58[/C][C]20.32[/C][C]21.6888656051815[/C][C]-1.36886560518149[/C][/ROW]
[ROW][C]59[/C][C]20.62[/C][C]20.4328587397023[/C][C]0.187141260297732[/C][/ROW]
[ROW][C]60[/C][C]20.28[/C][C]20.6045707813071[/C][C]-0.324570781307127[/C][/ROW]
[ROW][C]61[/C][C]20.79[/C][C]20.306759858078[/C][C]0.483240141922003[/C][/ROW]
[ROW][C]62[/C][C]22.86[/C][C]20.7501583433871[/C][C]2.10984165661291[/C][/ROW]
[ROW][C]63[/C][C]22.59[/C][C]22.6860500742839[/C][C]-0.0960500742838732[/C][/ROW]
[ROW][C]64[/C][C]23.29[/C][C]22.5979190318545[/C][C]0.692080968145479[/C][/ROW]
[ROW][C]65[/C][C]21.87[/C][C]23.2329400625298[/C][C]-1.36294006252975[/C][/ROW]
[ROW][C]66[/C][C]21.52[/C][C]21.982370197019[/C][C]-0.462370197019048[/C][/ROW]
[ROW][C]67[/C][C]22[/C][C]21.5581209941385[/C][C]0.441879005861544[/C][/ROW]
[ROW][C]68[/C][C]27.84[/C][C]21.9635684412599[/C][C]5.87643155874007[/C][/ROW]
[ROW][C]69[/C][C]27.49[/C][C]27.3555063746538[/C][C]0.134493625346156[/C][/ROW]
[ROW][C]70[/C][C]26.97[/C][C]27.4789114161411[/C][C]-0.508911416141146[/C][/ROW]
[ROW][C]71[/C][C]27.71[/C][C]27.0119581738546[/C][C]0.698041826145403[/C][/ROW]
[ROW][C]72[/C][C]27.46[/C][C]27.6524486082052[/C][C]-0.19244860820519[/C][/ROW]
[ROW][C]73[/C][C]27.04[/C][C]27.4758667931295[/C][C]-0.435866793129495[/C][/ROW]
[ROW][C]74[/C][C]28[/C][C]27.0759358703765[/C][C]0.924064129623492[/C][/ROW]
[ROW][C]75[/C][C]27.32[/C][C]27.9238137676346[/C][C]-0.603813767634584[/C][/ROW]
[ROW][C]76[/C][C]26.36[/C][C]27.3697825795112[/C][C]-1.00978257951122[/C][/ROW]
[ROW][C]77[/C][C]26.15[/C][C]26.4432534537106[/C][C]-0.293253453710552[/C][/ROW]
[ROW][C]78[/C][C]25.94[/C][C]26.1741778411802[/C][C]-0.234177841180195[/C][/ROW]
[ROW][C]79[/C][C]24[/C][C]25.9593072394556[/C][C]-1.95930723945562[/C][/ROW]
[ROW][C]80[/C][C]24.32[/C][C]24.1615388281344[/C][C]0.158461171865607[/C][/ROW]
[ROW][C]81[/C][C]23.1[/C][C]24.3069353659842[/C][C]-1.20693536598421[/C][/ROW]
[ROW][C]82[/C][C]22.92[/C][C]23.1995080918035[/C][C]-0.279508091803514[/C][/ROW]
[ROW][C]83[/C][C]23.56[/C][C]22.9430445785606[/C][C]0.616955421439382[/C][/ROW]
[ROW][C]84[/C][C]22.17[/C][C]23.5091339317369[/C][C]-1.33913393173693[/C][/ROW]
[ROW][C]85[/C][C]22.36[/C][C]22.28040745509[/C][C]0.0795925449099606[/C][/ROW]
[ROW][C]86[/C][C]19.86[/C][C]22.3534378405928[/C][C]-2.4934378405928[/C][/ROW]
[ROW][C]87[/C][C]20.07[/C][C]20.0655762458711[/C][C]0.00442375412892204[/C][/ROW]
[ROW][C]88[/C][C]19.21[/C][C]20.0696352751403[/C][C]-0.859635275140338[/C][/ROW]
[ROW][C]89[/C][C]19.99[/C][C]19.280874272382[/C][C]0.709125727618023[/C][/ROW]
[ROW][C]90[/C][C]20.47[/C][C]19.9315347747752[/C][C]0.538465225224805[/C][/ROW]
[ROW][C]91[/C][C]21.17[/C][C]20.4256052059002[/C][C]0.744394794099833[/C][/ROW]
[ROW][C]92[/C][C]21.25[/C][C]21.1086269492162[/C][C]0.141373050783827[/C][/ROW]
[ROW][C]93[/C][C]21.18[/C][C]21.2383442287695[/C][C]-0.058344228769478[/C][/ROW]
[ROW][C]94[/C][C]21.21[/C][C]21.1848103013933[/C][C]0.0251896986067166[/C][/ROW]
[ROW][C]95[/C][C]21.11[/C][C]21.2079231871796[/C][C]-0.0979231871796102[/C][/ROW]
[ROW][C]96[/C][C]21.94[/C][C]21.1180734642253[/C][C]0.821926535774669[/C][/ROW]
[ROW][C]97[/C][C]22.56[/C][C]21.8722346966684[/C][C]0.687765303331645[/C][/ROW]
[ROW][C]98[/C][C]23.23[/C][C]22.5032958757594[/C][C]0.726704124240648[/C][/ROW]
[ROW][C]99[/C][C]19.5[/C][C]23.1700854902864[/C][C]-3.67008549028641[/C][/ROW]
[ROW][C]100[/C][C]19.32[/C][C]19.8025872090477[/C][C]-0.482587209047736[/C][/ROW]
[ROW][C]101[/C][C]19[/C][C]19.3597878243148[/C][C]-0.359787824314822[/C][/ROW]
[ROW][C]102[/C][C]18.98[/C][C]19.0296633944623[/C][C]-0.0496633944623142[/C][/ROW]
[ROW][C]103[/C][C]19.88[/C][C]18.9840945934262[/C][C]0.895905406573778[/C][/ROW]
[ROW][C]104[/C][C]19.48[/C][C]19.8061353673468[/C][C]-0.326135367346772[/C][/ROW]
[ROW][C]105[/C][C]19.52[/C][C]19.5068888533628[/C][C]0.0131111466372076[/C][/ROW]
[ROW][C]106[/C][C]19.52[/C][C]19.5189190264699[/C][C]0.00108097353014358[/C][/ROW]
[ROW][C]107[/C][C]19.75[/C][C]19.5199108770724[/C][C]0.23008912292763[/C][/ROW]
[ROW][C]108[/C][C]19.64[/C][C]19.7310298627312[/C][C]-0.0910298627312116[/C][/ROW]
[ROW][C]109[/C][C]20.23[/C][C]19.6475051309232[/C][C]0.582494869076829[/C][/ROW]
[ROW][C]110[/C][C]20.4[/C][C]20.1819750978049[/C][C]0.218024902195076[/C][/ROW]
[ROW][C]111[/C][C]20.91[/C][C]20.3820245204553[/C][C]0.527975479544747[/C][/ROW]
[ROW][C]112[/C][C]21.95[/C][C]20.8664700530209[/C][C]1.08352994697914[/C][/ROW]
[ROW][C]113[/C][C]21.83[/C][C]21.8606663037022[/C][C]-0.0306663037022403[/C][/ROW]
[ROW][C]114[/C][C]22.27[/C][C]21.8325283419892[/C][C]0.437471658010793[/C][/ROW]
[ROW][C]115[/C][C]21.99[/C][C]22.2339318134726[/C][C]-0.243931813472649[/C][/ROW]
[ROW][C]116[/C][C]21.66[/C][C]22.0101114243338[/C][C]-0.350111424333843[/C][/ROW]
[ROW][C]117[/C][C]20.32[/C][C]21.6888656051815[/C][C]-1.36886560518149[/C][/ROW]
[ROW][C]118[/C][C]20.62[/C][C]20.4328587397023[/C][C]0.187141260297732[/C][/ROW]
[ROW][C]119[/C][C]20.28[/C][C]20.6045707813071[/C][C]-0.324570781307127[/C][/ROW]
[ROW][C]120[/C][C]20.79[/C][C]20.306759858078[/C][C]0.483240141922003[/C][/ROW]
[ROW][C]121[/C][C]22.86[/C][C]20.7501583433871[/C][C]2.10984165661291[/C][/ROW]
[ROW][C]122[/C][C]22.59[/C][C]22.6860500742839[/C][C]-0.0960500742838732[/C][/ROW]
[ROW][C]123[/C][C]23.29[/C][C]22.5979190318545[/C][C]0.692080968145479[/C][/ROW]
[ROW][C]124[/C][C]21.87[/C][C]23.2329400625298[/C][C]-1.36294006252975[/C][/ROW]
[ROW][C]125[/C][C]21.52[/C][C]21.982370197019[/C][C]-0.462370197019048[/C][/ROW]
[ROW][C]126[/C][C]22[/C][C]21.5581209941385[/C][C]0.441879005861544[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202799&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202799&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
228.2426.811.43
327.5828.1221009183346-0.542100918334604
427.9827.62469454576340.355305454236632
526.8127.9507061631013-1.14070616310126
628.2426.90404769865711.33595230134287
727.5828.1298548605055-0.549854860505526
827.9827.62533383433770.354666165662341
927.8427.950758870469-0.110758870468967
1027.4927.8491317266536-0.359131726653558
1126.9727.5196093012373-0.549609301237314
1227.7127.01531358873470.694686411265288
1327.4627.6527252517947-0.19272525179468
1427.0427.4758896015387-0.43588960153869
152827.07593775085940.924062249140626
1627.3227.9238139226746-0.603813922674586
1726.3627.3697825922938-1.00978259229379
1826.1526.4432534547644-0.293253454764436
1925.9426.1741778412671-0.234177841267083
202425.9593072394628-1.95930723946278
2124.3224.1615388281350.158461171865017
2223.124.3069353659843-1.20693536598426
2322.9223.1995080918035-0.279508091803521
2423.5622.94304457856060.616955421439378
2522.1723.5091339317369-1.33913393173693
2622.3622.280407455090.0795925449099606
2719.8622.3534378405928-2.4934378405928
2820.0720.06557624587110.00442375412892204
2919.2120.0696352751403-0.859635275140338
3019.9919.2808742723820.709125727618023
3120.4719.93153477477520.538465225224805
3221.1720.42560520590020.744394794099833
3321.2521.10862694921620.141373050783827
3421.1821.2383442287695-0.058344228769478
3521.2121.18481030139330.0251896986067166
3621.1121.2079231871796-0.0979231871796102
3721.9421.11807346422530.821926535774669
3822.5621.87223469666840.687765303331645
3923.2322.50329587575940.726704124240648
4019.523.1700854902864-3.67008549028641
4119.3219.8025872090477-0.482587209047736
421919.3597878243148-0.359787824314822
4318.9819.0296633944623-0.0496633944623142
4419.8818.98409459342620.895905406573778
4519.4819.8061353673468-0.326135367346772
4619.5219.50688885336280.0131111466372076
4719.5219.51891902646990.00108097353014358
4819.7519.51991087707240.23008912292763
4919.6419.7310298627312-0.0910298627312116
5020.2319.64750513092320.582494869076829
5120.420.18197509780490.218024902195076
5220.9120.38202452045530.527975479544747
5321.9520.86647005302091.08352994697914
5421.8321.8606663037022-0.0306663037022403
5522.2721.83252834198920.437471658010793
5621.9922.2339318134726-0.243931813472649
5721.6622.0101114243338-0.350111424333843
5820.3221.6888656051815-1.36886560518149
5920.6220.43285873970230.187141260297732
6020.2820.6045707813071-0.324570781307127
6120.7920.3067598580780.483240141922003
6222.8620.75015834338712.10984165661291
6322.5922.6860500742839-0.0960500742838732
6423.2922.59791903185450.692080968145479
6521.8723.2329400625298-1.36294006252975
6621.5221.982370197019-0.462370197019048
672221.55812099413850.441879005861544
6827.8421.96356844125995.87643155874007
6927.4927.35550637465380.134493625346156
7026.9727.4789114161411-0.508911416141146
7127.7127.01195817385460.698041826145403
7227.4627.6524486082052-0.19244860820519
7327.0427.4758667931295-0.435866793129495
742827.07593587037650.924064129623492
7527.3227.9238137676346-0.603813767634584
7626.3627.3697825795112-1.00978257951122
7726.1526.4432534537106-0.293253453710552
7825.9426.1741778411802-0.234177841180195
792425.9593072394556-1.95930723945562
8024.3224.16153882813440.158461171865607
8123.124.3069353659842-1.20693536598421
8222.9223.1995080918035-0.279508091803514
8323.5622.94304457856060.616955421439382
8422.1723.5091339317369-1.33913393173693
8522.3622.280407455090.0795925449099606
8619.8622.3534378405928-2.4934378405928
8720.0720.06557624587110.00442375412892204
8819.2120.0696352751403-0.859635275140338
8919.9919.2808742723820.709125727618023
9020.4719.93153477477520.538465225224805
9121.1720.42560520590020.744394794099833
9221.2521.10862694921620.141373050783827
9321.1821.2383442287695-0.058344228769478
9421.2121.18481030139330.0251896986067166
9521.1121.2079231871796-0.0979231871796102
9621.9421.11807346422530.821926535774669
9722.5621.87223469666840.687765303331645
9823.2322.50329587575940.726704124240648
9919.523.1700854902864-3.67008549028641
10019.3219.8025872090477-0.482587209047736
1011919.3597878243148-0.359787824314822
10218.9819.0296633944623-0.0496633944623142
10319.8818.98409459342620.895905406573778
10419.4819.8061353673468-0.326135367346772
10519.5219.50688885336280.0131111466372076
10619.5219.51891902646990.00108097353014358
10719.7519.51991087707240.23008912292763
10819.6419.7310298627312-0.0910298627312116
10920.2319.64750513092320.582494869076829
11020.420.18197509780490.218024902195076
11120.9120.38202452045530.527975479544747
11221.9520.86647005302091.08352994697914
11321.8321.8606663037022-0.0306663037022403
11422.2721.83252834198920.437471658010793
11521.9922.2339318134726-0.243931813472649
11621.6622.0101114243338-0.350111424333843
11720.3221.6888656051815-1.36886560518149
11820.6220.43285873970230.187141260297732
11920.2820.6045707813071-0.324570781307127
12020.7920.3067598580780.483240141922003
12122.8620.75015834338712.10984165661291
12222.5922.6860500742839-0.0960500742838732
12323.2922.59791903185450.692080968145479
12421.8723.2329400625298-1.36294006252975
12521.5221.982370197019-0.462370197019048
1262221.55812099413850.441879005861544







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
12721.963568441259919.916085693701424.0110511888185
12821.963568441259919.184791362166524.7423455203534
12921.963568441259918.609314648083325.3178222344366
13021.963568441259918.11903531449625.8081015680239

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
127 & 21.9635684412599 & 19.9160856937014 & 24.0110511888185 \tabularnewline
128 & 21.9635684412599 & 19.1847913621665 & 24.7423455203534 \tabularnewline
129 & 21.9635684412599 & 18.6093146480833 & 25.3178222344366 \tabularnewline
130 & 21.9635684412599 & 18.119035314496 & 25.8081015680239 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202799&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]127[/C][C]21.9635684412599[/C][C]19.9160856937014[/C][C]24.0110511888185[/C][/ROW]
[ROW][C]128[/C][C]21.9635684412599[/C][C]19.1847913621665[/C][C]24.7423455203534[/C][/ROW]
[ROW][C]129[/C][C]21.9635684412599[/C][C]18.6093146480833[/C][C]25.3178222344366[/C][/ROW]
[ROW][C]130[/C][C]21.9635684412599[/C][C]18.119035314496[/C][C]25.8081015680239[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202799&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202799&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
12721.963568441259919.916085693701424.0110511888185
12821.963568441259919.184791362166524.7423455203534
12921.963568441259918.609314648083325.3178222344366
13021.963568441259918.11903531449625.8081015680239



Parameters (Session):
par1 = grey ;
Parameters (R input):
par1 = 4 ; 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')