FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationWed, 05 Jan 2011 19:06:39 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Jan/05/t1294254289gylhmifidujvwor.htm/, Retrieved Thu, 16 May 2024 21:21:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117280, Retrieved Thu, 16 May 2024 21:21:25 +0000
QR Codes:

Original text written by user:Verbetering opgave 10
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W92 - Natasha Van Linden
Estimated Impact189

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [Exponential Smoot...] [2011-01-05 19:06:39] [85d6e4146de3ee96ae2a9c7dd566a647] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97
100,7
101,4
101,5
101,8
101,5
102,2
101,8
98,5
98,4
97,5
97,7
98,3
99,6
99,4
96,7
96,9
96,1
97,9
99,2
97,8
94,9
93,3
91,5
89,1
92,3
91,8
92,1
94,4
92,8
92,6
92,3
92,1
89,8
87,4
87,7
86,3
89,1
90,4
87,1
86,7
84,4
88,4
88,9
88,5
87,2
86,2
83,4
87,5
85,7
87,4
86,8
87,9
85,9
87,7
87
86,8
86,2
86,1
87,5
85,7
88,9
89,8
91,4
95,2
94,1
96,8
96,1
96,6
94,2
93,9
96,5
93,4
95
95,2
94
97
96,9
96,3
96,3
97,3
95,7
96,4
95,1
94,6
95,9
96,2
94,3
98,3
95,9
92,1
94,6
94,7
96,7
97,5
96,2
97,1
95,9
94,5
99,4
101,3
101,4
100,9
101,4
103,1
102,4
101,1
102
103,9
101,7
101,2
101,9
101,1
103,1
103,3
101,4
102,8
103
102,6
102,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ www.wessa.org

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ www.wessa.org \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117280&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ www.wessa.org[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117280&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117280&T=0

As an alternative you can also use a QR Code:  
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 time1 seconds
R Server'Gwilym Jenkins' @ www.wessa.org






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.787135282216426
betaFALSE
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.787135282216426 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117280&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]0.787135282216426[/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=117280&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117280&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.787135282216426
betaFALSE
gammaFALSE






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
2100.7973.7
3101.499.91240054420081.48759945579923
4101.5101.0833425616660.416657438333687
5101.8101.4113083319770.388691668023327
6101.5101.717261257781-0.217261257781388
7102.2101.5462472563230.653752743677074
8101.8102.060839106717-0.260839106716944
998.5101.855523442838-3.35552344283822
1098.499.214272550676-0.81427255067591
1197.598.5733298966986-1.07332989669855
1297.797.7284740655494-0.0284740655494033
1398.397.70606112392730.59393887607267
1499.698.1735713687641.42642863123589
1599.499.29636367197360.103636328026454
1696.799.3779394822825-2.67793948228253
1796.997.2700388321376-0.370038832137567
1896.196.978768211572-0.878768211571938
1997.996.28705874735341.61294125264656
2099.297.5566617154541.64333828454609
2197.898.8501912598371-1.05019125983715
2294.998.023548666144-3.12354866614399
2393.395.564893305302-2.26489330530201
2491.593.782115874243-2.28211587424302
2589.191.9857819515202-2.88578195152016
2692.389.71428116069532.58571883930473
2791.891.74959168900370.050408310996275
2892.191.78926984910580.310730150894173
2994.492.0338565141232.36614348587696
3092.893.8963315346434-1.09633153464337
3192.693.033370302719-0.433370302719084
3292.392.692249247184-0.392249247184083
3392.192.3834960253027-0.283496025302668
3489.892.1603463014188-2.3603463014188
3587.490.302434449323-2.90243444932301
3687.788.0178258899405-0.317825889940465
3786.387.7676539183665-1.46765391836649
3889.186.6124117371372.48758826286294
3990.488.5704802264641.82951977353606
4087.190.0105597897268-2.91055978972679
4186.787.7195554882324-1.0195554882324
4284.486.9170273912673-2.51702739126728
4388.484.93578632529563.46421367470437
4488.987.6625911337921.23740886620794
4588.588.6365993109118-0.136599310911762
4687.288.5290771737667-1.32907717376666
4786.287.4829136375064-1.28291363750644
4883.486.4730870493885-3.0730870493885
4987.584.05415180749243.44584819250755
5085.786.7665004969768-1.06650049697684
5187.485.9270203273051.47297967269499
5286.887.0864545976708-0.28645459767084
5387.986.8609760770911.039023922909
5485.987.6788284658796-1.7788284658796
5587.786.27864981937491.42135018062514
568787.3974446949296-0.39744469492959
5786.887.0846019528208-0.284601952820765
5886.286.8605817143678-0.660581714367837
5986.186.3406145402019-0.240614540201904
6087.586.15121834619471.3487816538053
6185.787.212891973911-1.51289197391107
6288.986.02204132306362.87795867693639
6389.888.28738413844121.51261586155884
6491.489.47801745151431.92198254848569
6595.290.99087772723164.20912227276837
6694.194.3040263752906-0.20402637529061
6796.894.14343001679662.65656998320337
6896.196.234509980253-0.1345099802531
6996.696.12863242898570.471367571014341
7094.296.4996624750237-2.29966247502369
7193.994.6895170037434-0.789517003743384
7296.594.06806031418722.43193968581282
7393.495.9823258451128-2.58232584511276
749593.94968606224511.05031393775485
7595.294.77642522005570.423574779944332
769495.1098358740069-1.1098358740069
779794.23624490010662.76375509989343
7896.996.41169405063830.488305949361731
7996.396.796056891897-0.496056891897084
8096.396.4055930102983-0.105593010298279
8197.396.3224770263370.977522973662943
8295.797.0919198480843-1.39191984808427
8396.495.99629062563980.403709374360176
8495.196.3140645179602-1.21406451796024
8594.695.3584315009866-0.758431500986646
8695.994.76144330741571.13855669258432
8796.295.65764145095240.542358549047563
8894.396.0845510005195-1.78455100051949
8998.394.6798679450963.62013205490402
9095.997.5294016117936-1.62940161179358
9192.196.2468421142505-4.14684211425055
9294.692.9827163763431.61728362365702
9394.794.25573737787430.444262622125748
9496.794.60543216231942.09456783768059
9597.596.25414040835361.24585959164642
9696.297.2348004496262-1.03480044962622
9797.196.4202725056720.679727494327992
9895.996.9553099987501-1.05530999875013
9994.596.1246382650581-1.62463826505814
10099.494.8458281657924.55417183420801
101101.398.43057749777342.86942250222657
102101.4100.6892011888620.710798811138304
103100.9101.248696011666-0.348696011666149
104101.4100.9742250781160.425774921884425
105103.1101.3093675414141.79063245858623
106102.4102.718837527049-0.318837527048927
107101.1102.467869260214-1.36786926021409
108102101.391171104040.608828895959704
109103.9101.8704018088832.02959819111695
110101.7103.467970153834-1.76797015383384
111101.2102.076338467846-0.876338467845628
112101.9101.3865415406410.513458459359157
113101.1101.790702809955-0.690702809954928
114103.1101.2470262587131.85297374128662
115103.3102.7055672675010.594432732499357
116101.4103.173466244155-1.7734662441552
117102.8101.7775083915611.0224916084392
118103102.5823476123340.417652387666479
119102.6102.911096542368-0.31109654236775
120102.2102.666221477695-0.466221477694546

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 100.7 & 97 & 3.7 \tabularnewline
3 & 101.4 & 99.9124005442008 & 1.48759945579923 \tabularnewline
4 & 101.5 & 101.083342561666 & 0.416657438333687 \tabularnewline
5 & 101.8 & 101.411308331977 & 0.388691668023327 \tabularnewline
6 & 101.5 & 101.717261257781 & -0.217261257781388 \tabularnewline
7 & 102.2 & 101.546247256323 & 0.653752743677074 \tabularnewline
8 & 101.8 & 102.060839106717 & -0.260839106716944 \tabularnewline
9 & 98.5 & 101.855523442838 & -3.35552344283822 \tabularnewline
10 & 98.4 & 99.214272550676 & -0.81427255067591 \tabularnewline
11 & 97.5 & 98.5733298966986 & -1.07332989669855 \tabularnewline
12 & 97.7 & 97.7284740655494 & -0.0284740655494033 \tabularnewline
13 & 98.3 & 97.7060611239273 & 0.59393887607267 \tabularnewline
14 & 99.6 & 98.173571368764 & 1.42642863123589 \tabularnewline
15 & 99.4 & 99.2963636719736 & 0.103636328026454 \tabularnewline
16 & 96.7 & 99.3779394822825 & -2.67793948228253 \tabularnewline
17 & 96.9 & 97.2700388321376 & -0.370038832137567 \tabularnewline
18 & 96.1 & 96.978768211572 & -0.878768211571938 \tabularnewline
19 & 97.9 & 96.2870587473534 & 1.61294125264656 \tabularnewline
20 & 99.2 & 97.556661715454 & 1.64333828454609 \tabularnewline
21 & 97.8 & 98.8501912598371 & -1.05019125983715 \tabularnewline
22 & 94.9 & 98.023548666144 & -3.12354866614399 \tabularnewline
23 & 93.3 & 95.564893305302 & -2.26489330530201 \tabularnewline
24 & 91.5 & 93.782115874243 & -2.28211587424302 \tabularnewline
25 & 89.1 & 91.9857819515202 & -2.88578195152016 \tabularnewline
26 & 92.3 & 89.7142811606953 & 2.58571883930473 \tabularnewline
27 & 91.8 & 91.7495916890037 & 0.050408310996275 \tabularnewline
28 & 92.1 & 91.7892698491058 & 0.310730150894173 \tabularnewline
29 & 94.4 & 92.033856514123 & 2.36614348587696 \tabularnewline
30 & 92.8 & 93.8963315346434 & -1.09633153464337 \tabularnewline
31 & 92.6 & 93.033370302719 & -0.433370302719084 \tabularnewline
32 & 92.3 & 92.692249247184 & -0.392249247184083 \tabularnewline
33 & 92.1 & 92.3834960253027 & -0.283496025302668 \tabularnewline
34 & 89.8 & 92.1603463014188 & -2.3603463014188 \tabularnewline
35 & 87.4 & 90.302434449323 & -2.90243444932301 \tabularnewline
36 & 87.7 & 88.0178258899405 & -0.317825889940465 \tabularnewline
37 & 86.3 & 87.7676539183665 & -1.46765391836649 \tabularnewline
38 & 89.1 & 86.612411737137 & 2.48758826286294 \tabularnewline
39 & 90.4 & 88.570480226464 & 1.82951977353606 \tabularnewline
40 & 87.1 & 90.0105597897268 & -2.91055978972679 \tabularnewline
41 & 86.7 & 87.7195554882324 & -1.0195554882324 \tabularnewline
42 & 84.4 & 86.9170273912673 & -2.51702739126728 \tabularnewline
43 & 88.4 & 84.9357863252956 & 3.46421367470437 \tabularnewline
44 & 88.9 & 87.662591133792 & 1.23740886620794 \tabularnewline
45 & 88.5 & 88.6365993109118 & -0.136599310911762 \tabularnewline
46 & 87.2 & 88.5290771737667 & -1.32907717376666 \tabularnewline
47 & 86.2 & 87.4829136375064 & -1.28291363750644 \tabularnewline
48 & 83.4 & 86.4730870493885 & -3.0730870493885 \tabularnewline
49 & 87.5 & 84.0541518074924 & 3.44584819250755 \tabularnewline
50 & 85.7 & 86.7665004969768 & -1.06650049697684 \tabularnewline
51 & 87.4 & 85.927020327305 & 1.47297967269499 \tabularnewline
52 & 86.8 & 87.0864545976708 & -0.28645459767084 \tabularnewline
53 & 87.9 & 86.860976077091 & 1.039023922909 \tabularnewline
54 & 85.9 & 87.6788284658796 & -1.7788284658796 \tabularnewline
55 & 87.7 & 86.2786498193749 & 1.42135018062514 \tabularnewline
56 & 87 & 87.3974446949296 & -0.39744469492959 \tabularnewline
57 & 86.8 & 87.0846019528208 & -0.284601952820765 \tabularnewline
58 & 86.2 & 86.8605817143678 & -0.660581714367837 \tabularnewline
59 & 86.1 & 86.3406145402019 & -0.240614540201904 \tabularnewline
60 & 87.5 & 86.1512183461947 & 1.3487816538053 \tabularnewline
61 & 85.7 & 87.212891973911 & -1.51289197391107 \tabularnewline
62 & 88.9 & 86.0220413230636 & 2.87795867693639 \tabularnewline
63 & 89.8 & 88.2873841384412 & 1.51261586155884 \tabularnewline
64 & 91.4 & 89.4780174515143 & 1.92198254848569 \tabularnewline
65 & 95.2 & 90.9908777272316 & 4.20912227276837 \tabularnewline
66 & 94.1 & 94.3040263752906 & -0.20402637529061 \tabularnewline
67 & 96.8 & 94.1434300167966 & 2.65656998320337 \tabularnewline
68 & 96.1 & 96.234509980253 & -0.1345099802531 \tabularnewline
69 & 96.6 & 96.1286324289857 & 0.471367571014341 \tabularnewline
70 & 94.2 & 96.4996624750237 & -2.29966247502369 \tabularnewline
71 & 93.9 & 94.6895170037434 & -0.789517003743384 \tabularnewline
72 & 96.5 & 94.0680603141872 & 2.43193968581282 \tabularnewline
73 & 93.4 & 95.9823258451128 & -2.58232584511276 \tabularnewline
74 & 95 & 93.9496860622451 & 1.05031393775485 \tabularnewline
75 & 95.2 & 94.7764252200557 & 0.423574779944332 \tabularnewline
76 & 94 & 95.1098358740069 & -1.1098358740069 \tabularnewline
77 & 97 & 94.2362449001066 & 2.76375509989343 \tabularnewline
78 & 96.9 & 96.4116940506383 & 0.488305949361731 \tabularnewline
79 & 96.3 & 96.796056891897 & -0.496056891897084 \tabularnewline
80 & 96.3 & 96.4055930102983 & -0.105593010298279 \tabularnewline
81 & 97.3 & 96.322477026337 & 0.977522973662943 \tabularnewline
82 & 95.7 & 97.0919198480843 & -1.39191984808427 \tabularnewline
83 & 96.4 & 95.9962906256398 & 0.403709374360176 \tabularnewline
84 & 95.1 & 96.3140645179602 & -1.21406451796024 \tabularnewline
85 & 94.6 & 95.3584315009866 & -0.758431500986646 \tabularnewline
86 & 95.9 & 94.7614433074157 & 1.13855669258432 \tabularnewline
87 & 96.2 & 95.6576414509524 & 0.542358549047563 \tabularnewline
88 & 94.3 & 96.0845510005195 & -1.78455100051949 \tabularnewline
89 & 98.3 & 94.679867945096 & 3.62013205490402 \tabularnewline
90 & 95.9 & 97.5294016117936 & -1.62940161179358 \tabularnewline
91 & 92.1 & 96.2468421142505 & -4.14684211425055 \tabularnewline
92 & 94.6 & 92.982716376343 & 1.61728362365702 \tabularnewline
93 & 94.7 & 94.2557373778743 & 0.444262622125748 \tabularnewline
94 & 96.7 & 94.6054321623194 & 2.09456783768059 \tabularnewline
95 & 97.5 & 96.2541404083536 & 1.24585959164642 \tabularnewline
96 & 96.2 & 97.2348004496262 & -1.03480044962622 \tabularnewline
97 & 97.1 & 96.420272505672 & 0.679727494327992 \tabularnewline
98 & 95.9 & 96.9553099987501 & -1.05530999875013 \tabularnewline
99 & 94.5 & 96.1246382650581 & -1.62463826505814 \tabularnewline
100 & 99.4 & 94.845828165792 & 4.55417183420801 \tabularnewline
101 & 101.3 & 98.4305774977734 & 2.86942250222657 \tabularnewline
102 & 101.4 & 100.689201188862 & 0.710798811138304 \tabularnewline
103 & 100.9 & 101.248696011666 & -0.348696011666149 \tabularnewline
104 & 101.4 & 100.974225078116 & 0.425774921884425 \tabularnewline
105 & 103.1 & 101.309367541414 & 1.79063245858623 \tabularnewline
106 & 102.4 & 102.718837527049 & -0.318837527048927 \tabularnewline
107 & 101.1 & 102.467869260214 & -1.36786926021409 \tabularnewline
108 & 102 & 101.39117110404 & 0.608828895959704 \tabularnewline
109 & 103.9 & 101.870401808883 & 2.02959819111695 \tabularnewline
110 & 101.7 & 103.467970153834 & -1.76797015383384 \tabularnewline
111 & 101.2 & 102.076338467846 & -0.876338467845628 \tabularnewline
112 & 101.9 & 101.386541540641 & 0.513458459359157 \tabularnewline
113 & 101.1 & 101.790702809955 & -0.690702809954928 \tabularnewline
114 & 103.1 & 101.247026258713 & 1.85297374128662 \tabularnewline
115 & 103.3 & 102.705567267501 & 0.594432732499357 \tabularnewline
116 & 101.4 & 103.173466244155 & -1.7734662441552 \tabularnewline
117 & 102.8 & 101.777508391561 & 1.0224916084392 \tabularnewline
118 & 103 & 102.582347612334 & 0.417652387666479 \tabularnewline
119 & 102.6 & 102.911096542368 & -0.31109654236775 \tabularnewline
120 & 102.2 & 102.666221477695 & -0.466221477694546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117280&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]100.7[/C][C]97[/C][C]3.7[/C][/ROW]
[ROW][C]3[/C][C]101.4[/C][C]99.9124005442008[/C][C]1.48759945579923[/C][/ROW]
[ROW][C]4[/C][C]101.5[/C][C]101.083342561666[/C][C]0.416657438333687[/C][/ROW]
[ROW][C]5[/C][C]101.8[/C][C]101.411308331977[/C][C]0.388691668023327[/C][/ROW]
[ROW][C]6[/C][C]101.5[/C][C]101.717261257781[/C][C]-0.217261257781388[/C][/ROW]
[ROW][C]7[/C][C]102.2[/C][C]101.546247256323[/C][C]0.653752743677074[/C][/ROW]
[ROW][C]8[/C][C]101.8[/C][C]102.060839106717[/C][C]-0.260839106716944[/C][/ROW]
[ROW][C]9[/C][C]98.5[/C][C]101.855523442838[/C][C]-3.35552344283822[/C][/ROW]
[ROW][C]10[/C][C]98.4[/C][C]99.214272550676[/C][C]-0.81427255067591[/C][/ROW]
[ROW][C]11[/C][C]97.5[/C][C]98.5733298966986[/C][C]-1.07332989669855[/C][/ROW]
[ROW][C]12[/C][C]97.7[/C][C]97.7284740655494[/C][C]-0.0284740655494033[/C][/ROW]
[ROW][C]13[/C][C]98.3[/C][C]97.7060611239273[/C][C]0.59393887607267[/C][/ROW]
[ROW][C]14[/C][C]99.6[/C][C]98.173571368764[/C][C]1.42642863123589[/C][/ROW]
[ROW][C]15[/C][C]99.4[/C][C]99.2963636719736[/C][C]0.103636328026454[/C][/ROW]
[ROW][C]16[/C][C]96.7[/C][C]99.3779394822825[/C][C]-2.67793948228253[/C][/ROW]
[ROW][C]17[/C][C]96.9[/C][C]97.2700388321376[/C][C]-0.370038832137567[/C][/ROW]
[ROW][C]18[/C][C]96.1[/C][C]96.978768211572[/C][C]-0.878768211571938[/C][/ROW]
[ROW][C]19[/C][C]97.9[/C][C]96.2870587473534[/C][C]1.61294125264656[/C][/ROW]
[ROW][C]20[/C][C]99.2[/C][C]97.556661715454[/C][C]1.64333828454609[/C][/ROW]
[ROW][C]21[/C][C]97.8[/C][C]98.8501912598371[/C][C]-1.05019125983715[/C][/ROW]
[ROW][C]22[/C][C]94.9[/C][C]98.023548666144[/C][C]-3.12354866614399[/C][/ROW]
[ROW][C]23[/C][C]93.3[/C][C]95.564893305302[/C][C]-2.26489330530201[/C][/ROW]
[ROW][C]24[/C][C]91.5[/C][C]93.782115874243[/C][C]-2.28211587424302[/C][/ROW]
[ROW][C]25[/C][C]89.1[/C][C]91.9857819515202[/C][C]-2.88578195152016[/C][/ROW]
[ROW][C]26[/C][C]92.3[/C][C]89.7142811606953[/C][C]2.58571883930473[/C][/ROW]
[ROW][C]27[/C][C]91.8[/C][C]91.7495916890037[/C][C]0.050408310996275[/C][/ROW]
[ROW][C]28[/C][C]92.1[/C][C]91.7892698491058[/C][C]0.310730150894173[/C][/ROW]
[ROW][C]29[/C][C]94.4[/C][C]92.033856514123[/C][C]2.36614348587696[/C][/ROW]
[ROW][C]30[/C][C]92.8[/C][C]93.8963315346434[/C][C]-1.09633153464337[/C][/ROW]
[ROW][C]31[/C][C]92.6[/C][C]93.033370302719[/C][C]-0.433370302719084[/C][/ROW]
[ROW][C]32[/C][C]92.3[/C][C]92.692249247184[/C][C]-0.392249247184083[/C][/ROW]
[ROW][C]33[/C][C]92.1[/C][C]92.3834960253027[/C][C]-0.283496025302668[/C][/ROW]
[ROW][C]34[/C][C]89.8[/C][C]92.1603463014188[/C][C]-2.3603463014188[/C][/ROW]
[ROW][C]35[/C][C]87.4[/C][C]90.302434449323[/C][C]-2.90243444932301[/C][/ROW]
[ROW][C]36[/C][C]87.7[/C][C]88.0178258899405[/C][C]-0.317825889940465[/C][/ROW]
[ROW][C]37[/C][C]86.3[/C][C]87.7676539183665[/C][C]-1.46765391836649[/C][/ROW]
[ROW][C]38[/C][C]89.1[/C][C]86.612411737137[/C][C]2.48758826286294[/C][/ROW]
[ROW][C]39[/C][C]90.4[/C][C]88.570480226464[/C][C]1.82951977353606[/C][/ROW]
[ROW][C]40[/C][C]87.1[/C][C]90.0105597897268[/C][C]-2.91055978972679[/C][/ROW]
[ROW][C]41[/C][C]86.7[/C][C]87.7195554882324[/C][C]-1.0195554882324[/C][/ROW]
[ROW][C]42[/C][C]84.4[/C][C]86.9170273912673[/C][C]-2.51702739126728[/C][/ROW]
[ROW][C]43[/C][C]88.4[/C][C]84.9357863252956[/C][C]3.46421367470437[/C][/ROW]
[ROW][C]44[/C][C]88.9[/C][C]87.662591133792[/C][C]1.23740886620794[/C][/ROW]
[ROW][C]45[/C][C]88.5[/C][C]88.6365993109118[/C][C]-0.136599310911762[/C][/ROW]
[ROW][C]46[/C][C]87.2[/C][C]88.5290771737667[/C][C]-1.32907717376666[/C][/ROW]
[ROW][C]47[/C][C]86.2[/C][C]87.4829136375064[/C][C]-1.28291363750644[/C][/ROW]
[ROW][C]48[/C][C]83.4[/C][C]86.4730870493885[/C][C]-3.0730870493885[/C][/ROW]
[ROW][C]49[/C][C]87.5[/C][C]84.0541518074924[/C][C]3.44584819250755[/C][/ROW]
[ROW][C]50[/C][C]85.7[/C][C]86.7665004969768[/C][C]-1.06650049697684[/C][/ROW]
[ROW][C]51[/C][C]87.4[/C][C]85.927020327305[/C][C]1.47297967269499[/C][/ROW]
[ROW][C]52[/C][C]86.8[/C][C]87.0864545976708[/C][C]-0.28645459767084[/C][/ROW]
[ROW][C]53[/C][C]87.9[/C][C]86.860976077091[/C][C]1.039023922909[/C][/ROW]
[ROW][C]54[/C][C]85.9[/C][C]87.6788284658796[/C][C]-1.7788284658796[/C][/ROW]
[ROW][C]55[/C][C]87.7[/C][C]86.2786498193749[/C][C]1.42135018062514[/C][/ROW]
[ROW][C]56[/C][C]87[/C][C]87.3974446949296[/C][C]-0.39744469492959[/C][/ROW]
[ROW][C]57[/C][C]86.8[/C][C]87.0846019528208[/C][C]-0.284601952820765[/C][/ROW]
[ROW][C]58[/C][C]86.2[/C][C]86.8605817143678[/C][C]-0.660581714367837[/C][/ROW]
[ROW][C]59[/C][C]86.1[/C][C]86.3406145402019[/C][C]-0.240614540201904[/C][/ROW]
[ROW][C]60[/C][C]87.5[/C][C]86.1512183461947[/C][C]1.3487816538053[/C][/ROW]
[ROW][C]61[/C][C]85.7[/C][C]87.212891973911[/C][C]-1.51289197391107[/C][/ROW]
[ROW][C]62[/C][C]88.9[/C][C]86.0220413230636[/C][C]2.87795867693639[/C][/ROW]
[ROW][C]63[/C][C]89.8[/C][C]88.2873841384412[/C][C]1.51261586155884[/C][/ROW]
[ROW][C]64[/C][C]91.4[/C][C]89.4780174515143[/C][C]1.92198254848569[/C][/ROW]
[ROW][C]65[/C][C]95.2[/C][C]90.9908777272316[/C][C]4.20912227276837[/C][/ROW]
[ROW][C]66[/C][C]94.1[/C][C]94.3040263752906[/C][C]-0.20402637529061[/C][/ROW]
[ROW][C]67[/C][C]96.8[/C][C]94.1434300167966[/C][C]2.65656998320337[/C][/ROW]
[ROW][C]68[/C][C]96.1[/C][C]96.234509980253[/C][C]-0.1345099802531[/C][/ROW]
[ROW][C]69[/C][C]96.6[/C][C]96.1286324289857[/C][C]0.471367571014341[/C][/ROW]
[ROW][C]70[/C][C]94.2[/C][C]96.4996624750237[/C][C]-2.29966247502369[/C][/ROW]
[ROW][C]71[/C][C]93.9[/C][C]94.6895170037434[/C][C]-0.789517003743384[/C][/ROW]
[ROW][C]72[/C][C]96.5[/C][C]94.0680603141872[/C][C]2.43193968581282[/C][/ROW]
[ROW][C]73[/C][C]93.4[/C][C]95.9823258451128[/C][C]-2.58232584511276[/C][/ROW]
[ROW][C]74[/C][C]95[/C][C]93.9496860622451[/C][C]1.05031393775485[/C][/ROW]
[ROW][C]75[/C][C]95.2[/C][C]94.7764252200557[/C][C]0.423574779944332[/C][/ROW]
[ROW][C]76[/C][C]94[/C][C]95.1098358740069[/C][C]-1.1098358740069[/C][/ROW]
[ROW][C]77[/C][C]97[/C][C]94.2362449001066[/C][C]2.76375509989343[/C][/ROW]
[ROW][C]78[/C][C]96.9[/C][C]96.4116940506383[/C][C]0.488305949361731[/C][/ROW]
[ROW][C]79[/C][C]96.3[/C][C]96.796056891897[/C][C]-0.496056891897084[/C][/ROW]
[ROW][C]80[/C][C]96.3[/C][C]96.4055930102983[/C][C]-0.105593010298279[/C][/ROW]
[ROW][C]81[/C][C]97.3[/C][C]96.322477026337[/C][C]0.977522973662943[/C][/ROW]
[ROW][C]82[/C][C]95.7[/C][C]97.0919198480843[/C][C]-1.39191984808427[/C][/ROW]
[ROW][C]83[/C][C]96.4[/C][C]95.9962906256398[/C][C]0.403709374360176[/C][/ROW]
[ROW][C]84[/C][C]95.1[/C][C]96.3140645179602[/C][C]-1.21406451796024[/C][/ROW]
[ROW][C]85[/C][C]94.6[/C][C]95.3584315009866[/C][C]-0.758431500986646[/C][/ROW]
[ROW][C]86[/C][C]95.9[/C][C]94.7614433074157[/C][C]1.13855669258432[/C][/ROW]
[ROW][C]87[/C][C]96.2[/C][C]95.6576414509524[/C][C]0.542358549047563[/C][/ROW]
[ROW][C]88[/C][C]94.3[/C][C]96.0845510005195[/C][C]-1.78455100051949[/C][/ROW]
[ROW][C]89[/C][C]98.3[/C][C]94.679867945096[/C][C]3.62013205490402[/C][/ROW]
[ROW][C]90[/C][C]95.9[/C][C]97.5294016117936[/C][C]-1.62940161179358[/C][/ROW]
[ROW][C]91[/C][C]92.1[/C][C]96.2468421142505[/C][C]-4.14684211425055[/C][/ROW]
[ROW][C]92[/C][C]94.6[/C][C]92.982716376343[/C][C]1.61728362365702[/C][/ROW]
[ROW][C]93[/C][C]94.7[/C][C]94.2557373778743[/C][C]0.444262622125748[/C][/ROW]
[ROW][C]94[/C][C]96.7[/C][C]94.6054321623194[/C][C]2.09456783768059[/C][/ROW]
[ROW][C]95[/C][C]97.5[/C][C]96.2541404083536[/C][C]1.24585959164642[/C][/ROW]
[ROW][C]96[/C][C]96.2[/C][C]97.2348004496262[/C][C]-1.03480044962622[/C][/ROW]
[ROW][C]97[/C][C]97.1[/C][C]96.420272505672[/C][C]0.679727494327992[/C][/ROW]
[ROW][C]98[/C][C]95.9[/C][C]96.9553099987501[/C][C]-1.05530999875013[/C][/ROW]
[ROW][C]99[/C][C]94.5[/C][C]96.1246382650581[/C][C]-1.62463826505814[/C][/ROW]
[ROW][C]100[/C][C]99.4[/C][C]94.845828165792[/C][C]4.55417183420801[/C][/ROW]
[ROW][C]101[/C][C]101.3[/C][C]98.4305774977734[/C][C]2.86942250222657[/C][/ROW]
[ROW][C]102[/C][C]101.4[/C][C]100.689201188862[/C][C]0.710798811138304[/C][/ROW]
[ROW][C]103[/C][C]100.9[/C][C]101.248696011666[/C][C]-0.348696011666149[/C][/ROW]
[ROW][C]104[/C][C]101.4[/C][C]100.974225078116[/C][C]0.425774921884425[/C][/ROW]
[ROW][C]105[/C][C]103.1[/C][C]101.309367541414[/C][C]1.79063245858623[/C][/ROW]
[ROW][C]106[/C][C]102.4[/C][C]102.718837527049[/C][C]-0.318837527048927[/C][/ROW]
[ROW][C]107[/C][C]101.1[/C][C]102.467869260214[/C][C]-1.36786926021409[/C][/ROW]
[ROW][C]108[/C][C]102[/C][C]101.39117110404[/C][C]0.608828895959704[/C][/ROW]
[ROW][C]109[/C][C]103.9[/C][C]101.870401808883[/C][C]2.02959819111695[/C][/ROW]
[ROW][C]110[/C][C]101.7[/C][C]103.467970153834[/C][C]-1.76797015383384[/C][/ROW]
[ROW][C]111[/C][C]101.2[/C][C]102.076338467846[/C][C]-0.876338467845628[/C][/ROW]
[ROW][C]112[/C][C]101.9[/C][C]101.386541540641[/C][C]0.513458459359157[/C][/ROW]
[ROW][C]113[/C][C]101.1[/C][C]101.790702809955[/C][C]-0.690702809954928[/C][/ROW]
[ROW][C]114[/C][C]103.1[/C][C]101.247026258713[/C][C]1.85297374128662[/C][/ROW]
[ROW][C]115[/C][C]103.3[/C][C]102.705567267501[/C][C]0.594432732499357[/C][/ROW]
[ROW][C]116[/C][C]101.4[/C][C]103.173466244155[/C][C]-1.7734662441552[/C][/ROW]
[ROW][C]117[/C][C]102.8[/C][C]101.777508391561[/C][C]1.0224916084392[/C][/ROW]
[ROW][C]118[/C][C]103[/C][C]102.582347612334[/C][C]0.417652387666479[/C][/ROW]
[ROW][C]119[/C][C]102.6[/C][C]102.911096542368[/C][C]-0.31109654236775[/C][/ROW]
[ROW][C]120[/C][C]102.2[/C][C]102.666221477695[/C][C]-0.466221477694546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117280&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117280&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
2100.7973.7
3101.499.91240054420081.48759945579923
4101.5101.0833425616660.416657438333687
5101.8101.4113083319770.388691668023327
6101.5101.717261257781-0.217261257781388
7102.2101.5462472563230.653752743677074
8101.8102.060839106717-0.260839106716944
998.5101.855523442838-3.35552344283822
1098.499.214272550676-0.81427255067591
1197.598.5733298966986-1.07332989669855
1297.797.7284740655494-0.0284740655494033
1398.397.70606112392730.59393887607267
1499.698.1735713687641.42642863123589
1599.499.29636367197360.103636328026454
1696.799.3779394822825-2.67793948228253
1796.997.2700388321376-0.370038832137567
1896.196.978768211572-0.878768211571938
1997.996.28705874735341.61294125264656
2099.297.5566617154541.64333828454609
2197.898.8501912598371-1.05019125983715
2294.998.023548666144-3.12354866614399
2393.395.564893305302-2.26489330530201
2491.593.782115874243-2.28211587424302
2589.191.9857819515202-2.88578195152016
2692.389.71428116069532.58571883930473
2791.891.74959168900370.050408310996275
2892.191.78926984910580.310730150894173
2994.492.0338565141232.36614348587696
3092.893.8963315346434-1.09633153464337
3192.693.033370302719-0.433370302719084
3292.392.692249247184-0.392249247184083
3392.192.3834960253027-0.283496025302668
3489.892.1603463014188-2.3603463014188
3587.490.302434449323-2.90243444932301
3687.788.0178258899405-0.317825889940465
3786.387.7676539183665-1.46765391836649
3889.186.6124117371372.48758826286294
3990.488.5704802264641.82951977353606
4087.190.0105597897268-2.91055978972679
4186.787.7195554882324-1.0195554882324
4284.486.9170273912673-2.51702739126728
4388.484.93578632529563.46421367470437
4488.987.6625911337921.23740886620794
4588.588.6365993109118-0.136599310911762
4687.288.5290771737667-1.32907717376666
4786.287.4829136375064-1.28291363750644
4883.486.4730870493885-3.0730870493885
4987.584.05415180749243.44584819250755
5085.786.7665004969768-1.06650049697684
5187.485.9270203273051.47297967269499
5286.887.0864545976708-0.28645459767084
5387.986.8609760770911.039023922909
5485.987.6788284658796-1.7788284658796
5587.786.27864981937491.42135018062514
568787.3974446949296-0.39744469492959
5786.887.0846019528208-0.284601952820765
5886.286.8605817143678-0.660581714367837
5986.186.3406145402019-0.240614540201904
6087.586.15121834619471.3487816538053
6185.787.212891973911-1.51289197391107
6288.986.02204132306362.87795867693639
6389.888.28738413844121.51261586155884
6491.489.47801745151431.92198254848569
6595.290.99087772723164.20912227276837
6694.194.3040263752906-0.20402637529061
6796.894.14343001679662.65656998320337
6896.196.234509980253-0.1345099802531
6996.696.12863242898570.471367571014341
7094.296.4996624750237-2.29966247502369
7193.994.6895170037434-0.789517003743384
7296.594.06806031418722.43193968581282
7393.495.9823258451128-2.58232584511276
749593.94968606224511.05031393775485
7595.294.77642522005570.423574779944332
769495.1098358740069-1.1098358740069
779794.23624490010662.76375509989343
7896.996.41169405063830.488305949361731
7996.396.796056891897-0.496056891897084
8096.396.4055930102983-0.105593010298279
8197.396.3224770263370.977522973662943
8295.797.0919198480843-1.39191984808427
8396.495.99629062563980.403709374360176
8495.196.3140645179602-1.21406451796024
8594.695.3584315009866-0.758431500986646
8695.994.76144330741571.13855669258432
8796.295.65764145095240.542358549047563
8894.396.0845510005195-1.78455100051949
8998.394.6798679450963.62013205490402
9095.997.5294016117936-1.62940161179358
9192.196.2468421142505-4.14684211425055
9294.692.9827163763431.61728362365702
9394.794.25573737787430.444262622125748
9496.794.60543216231942.09456783768059
9597.596.25414040835361.24585959164642
9696.297.2348004496262-1.03480044962622
9797.196.4202725056720.679727494327992
9895.996.9553099987501-1.05530999875013
9994.596.1246382650581-1.62463826505814
10099.494.8458281657924.55417183420801
101101.398.43057749777342.86942250222657
102101.4100.6892011888620.710798811138304
103100.9101.248696011666-0.348696011666149
104101.4100.9742250781160.425774921884425
105103.1101.3093675414141.79063245858623
106102.4102.718837527049-0.318837527048927
107101.1102.467869260214-1.36786926021409
108102101.391171104040.608828895959704
109103.9101.8704018088832.02959819111695
110101.7103.467970153834-1.76797015383384
111101.2102.076338467846-0.876338467845628
112101.9101.3865415406410.513458459359157
113101.1101.790702809955-0.690702809954928
114103.1101.2470262587131.85297374128662
115103.3102.7055672675010.594432732499357
116101.4103.173466244155-1.7734662441552
117102.8101.7775083915611.0224916084392
118103102.5823476123340.417652387666479
119102.6102.911096542368-0.31109654236775
120102.2102.666221477695-0.466221477694546






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
121102.29924210327498.8707257081588105.727758498389
122102.29924210327497.9360162399013106.662467966647
123102.29924210327497.1688663667382107.42961783981
124102.29924210327496.502366180739108.096118025809
125102.29924210327495.9049645268393108.693519679709
126102.29924210327495.358795360612109.239688845936
127102.29924210327494.8525773296349109.745906876913
128102.29924210327494.3786467017787110.219837504769
129102.29924210327493.9315156033567110.666968603192
130102.29924210327493.507094354251111.091389852297
131102.29924210327493.1022383898965111.496245816652
132102.29924210327492.7144681066108111.884016099937

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
121 & 102.299242103274 & 98.8707257081588 & 105.727758498389 \tabularnewline
122 & 102.299242103274 & 97.9360162399013 & 106.662467966647 \tabularnewline
123 & 102.299242103274 & 97.1688663667382 & 107.42961783981 \tabularnewline
124 & 102.299242103274 & 96.502366180739 & 108.096118025809 \tabularnewline
125 & 102.299242103274 & 95.9049645268393 & 108.693519679709 \tabularnewline
126 & 102.299242103274 & 95.358795360612 & 109.239688845936 \tabularnewline
127 & 102.299242103274 & 94.8525773296349 & 109.745906876913 \tabularnewline
128 & 102.299242103274 & 94.3786467017787 & 110.219837504769 \tabularnewline
129 & 102.299242103274 & 93.9315156033567 & 110.666968603192 \tabularnewline
130 & 102.299242103274 & 93.507094354251 & 111.091389852297 \tabularnewline
131 & 102.299242103274 & 93.1022383898965 & 111.496245816652 \tabularnewline
132 & 102.299242103274 & 92.7144681066108 & 111.884016099937 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117280&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]121[/C][C]102.299242103274[/C][C]98.8707257081588[/C][C]105.727758498389[/C][/ROW]
[ROW][C]122[/C][C]102.299242103274[/C][C]97.9360162399013[/C][C]106.662467966647[/C][/ROW]
[ROW][C]123[/C][C]102.299242103274[/C][C]97.1688663667382[/C][C]107.42961783981[/C][/ROW]
[ROW][C]124[/C][C]102.299242103274[/C][C]96.502366180739[/C][C]108.096118025809[/C][/ROW]
[ROW][C]125[/C][C]102.299242103274[/C][C]95.9049645268393[/C][C]108.693519679709[/C][/ROW]
[ROW][C]126[/C][C]102.299242103274[/C][C]95.358795360612[/C][C]109.239688845936[/C][/ROW]
[ROW][C]127[/C][C]102.299242103274[/C][C]94.8525773296349[/C][C]109.745906876913[/C][/ROW]
[ROW][C]128[/C][C]102.299242103274[/C][C]94.3786467017787[/C][C]110.219837504769[/C][/ROW]
[ROW][C]129[/C][C]102.299242103274[/C][C]93.9315156033567[/C][C]110.666968603192[/C][/ROW]
[ROW][C]130[/C][C]102.299242103274[/C][C]93.507094354251[/C][C]111.091389852297[/C][/ROW]
[ROW][C]131[/C][C]102.299242103274[/C][C]93.1022383898965[/C][C]111.496245816652[/C][/ROW]
[ROW][C]132[/C][C]102.299242103274[/C][C]92.7144681066108[/C][C]111.884016099937[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117280&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117280&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
121102.29924210327498.8707257081588105.727758498389
122102.29924210327497.9360162399013106.662467966647
123102.29924210327497.1688663667382107.42961783981
124102.29924210327496.502366180739108.096118025809
125102.29924210327495.9049645268393108.693519679709
126102.29924210327495.358795360612109.239688845936
127102.29924210327494.8525773296349109.745906876913
128102.29924210327494.3786467017787110.219837504769
129102.29924210327493.9315156033567110.666968603192
130102.29924210327493.507094354251111.091389852297
131102.29924210327493.1022383898965111.496245816652
132102.29924210327492.7144681066108111.884016099937


Figures (Output of Computation)

Input Parameters & R Code

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')