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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationTue, 04 Jan 2011 13:48:53 +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/04/t1294149107z8io3nl8sx8ntho.htm/, Retrieved Thu, 16 May 2024 06:26:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117261, Retrieved Thu, 16 May 2024 06:26:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W102 - Natasha Van Linden
Estimated Impact194

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...] [2011-01-04 13:48:53] [85d6e4146de3ee96ae2a9c7dd566a647] [Current]
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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 time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117261&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117261&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117261&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 time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.78713528221648
betaFALSE
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.78713528221648 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117261&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]0.78713528221648[/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=117261&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117261&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
alpha0.78713528221648
betaFALSE
gammaFALSE






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
2100.7973.7
3101.499.9124005442011.48759945579903
4101.5101.0833425616660.416657438333573
5101.8101.4113083319770.388691668023284
6101.5101.717261257781-0.217261257781416
7102.2101.5462472563230.653752743677074
8101.8102.060839106717-0.260839106716986
998.5101.855523442838-3.35552344283822
1098.499.2142725506757-0.814272550675739
1197.598.5733298966985-1.07332989669847
1297.797.7284740655493-0.0284740655493323
1398.397.70606112392730.593938876072684
1499.698.17357136876411.42642863123586
1599.499.29636367197360.103636328026369
1696.799.3779394822826-2.67793948228255
1796.997.2700388321374-0.370038832137425
1896.196.9787682115719-0.878768211571895
1997.996.28705874735341.61294125264664
2099.297.5566617154541.64333828454602
2197.898.8501912598373-1.05019125983726
2294.998.023548666144-3.12354866614398
2393.395.5648933053018-2.26489330530184
2491.593.7821158742429-2.28211587424286
2589.191.98578195152-2.88578195152
2692.389.7142811606952.58571883930492
2791.891.74959168900380.0504083109961755
2892.191.78926984910580.310730150894145
2994.492.0338565141232.36614348587692
3092.893.8963315346435-1.0963315346435
3192.693.033370302719-0.433370302719069
3292.392.692249247184-0.392249247184054
3392.192.3834960253026-0.28349602530264
3489.892.1603463014188-2.36034630141879
3587.490.3024344493229-2.90243444932288
3687.788.0178258899403-0.317825889940295
3786.387.7676539183664-1.46765391836644
3889.186.6124117371372.48758826286304
3990.488.5704802264641.82951977353595
4087.190.0105597897269-2.9105597897269
4186.787.7195554882323-1.01955548823227
4284.486.9170273912672-2.51702739126721
4388.484.93578632529553.46421367470451
4488.987.66259113379221.23740886620779
4588.588.6365993109119-0.136599310911862
4687.288.5290771737667-1.32907717376668
4786.287.4829136375064-1.28291363750637
4883.486.4730870493884-3.07308704938842
4987.584.05415180749233.44584819250774
5085.786.766500496977-1.06650049697699
5187.485.9270203273051.47297967269502
5286.887.086454597671-0.286454597670925
5387.986.8609760770911.039023922909
5485.987.6788284658797-1.77882846587966
5587.786.27864981937481.42135018062523
568787.3974446949297-0.397444694929661
5786.887.0846019528208-0.284601952820765
5886.286.8605817143678-0.660581714367822
5986.186.3406145402019-0.240614540201875
6087.586.15121834619471.34878165380532
6185.787.2128919739111-1.51289197391114
6288.986.02204132306352.87795867693646
6389.888.28738413844131.5126158615587
6491.489.47801745151441.92198254848557
6595.290.99087772723184.20912227276823
6694.194.3040263752909-0.204026375290866
6796.894.14343001679672.65656998320331
6896.196.2345099802533-0.134509980253256
6996.696.12863242898570.471367571014326
7094.296.4996624750237-2.29966247502372
7193.994.6895170037433-0.78951700374327
7296.594.06806031418712.43193968581289
7393.495.982325845113-2.58232584511289
749593.9496860622451.05031393775495
7595.294.77642522005570.423574779944303
769495.109835874007-1.10983587400695
779794.23624490010652.76375509989349
7896.996.41169405063840.488305949361603
7996.396.7960568918971-0.49605689189714
8096.396.4055930102982-0.10559301029825
8197.396.3224770263370.977522973662957
8295.797.0919198480843-1.39191984808431
8396.495.99629062563980.403709374360247
8495.196.3140645179602-1.21406451796025
8594.695.3584315009866-0.758431500986589
8695.994.76144330741561.13855669258437
8796.295.65764145095250.542358549047506
8894.396.0845510005195-1.78455100051953
8998.394.67986794509593.6201320549041
9095.997.5294016117938-1.62940161179375
9192.196.2468421142505-4.1468421142505
9294.692.98271637634281.61728362365724
9394.794.25573737787430.44426262212572
9496.794.60543216231942.09456783768056
9597.596.25414040835371.24585959164631
9696.297.2348004496263-1.03480044962632
9797.196.4202725056720.67972749432802
9895.996.9553099987502-1.05530999875016
9994.596.124638265058-1.62463826505808
10099.494.8458281657924.55417183420811
101101.398.43057749777362.86942250222636
102101.4100.6892011888620.710798811138105
103100.9101.248696011666-0.34869601166622
104101.4100.9742250781160.425774921884425
105103.1101.3093675414141.79063245858622
106102.4102.718837527049-0.318837527049027
107101.1102.467869260214-1.36786926021409
108102101.391171104040.608828895959775
109103.9101.8704018088832.02959819111693
110101.7103.467970153834-1.76797015383396
111101.2102.076338467846-0.876338467845557
112101.9101.3865415406410.513458459359214
113101.1101.790702809955-0.690702809954956
114103.1101.2470262587131.85297374128665
115103.3102.7055672675010.594432732499257
116101.4103.173466244155-1.77346624415524
117102.8101.7775083915611.02249160843928
118103102.5823476123340.41765238766645
119102.6102.911096542368-0.311096542367778
120102.2102.666221477695-0.466221477694532

\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 & 99.912400544201 & 1.48759945579903 \tabularnewline
4 & 101.5 & 101.083342561666 & 0.416657438333573 \tabularnewline
5 & 101.8 & 101.411308331977 & 0.388691668023284 \tabularnewline
6 & 101.5 & 101.717261257781 & -0.217261257781416 \tabularnewline
7 & 102.2 & 101.546247256323 & 0.653752743677074 \tabularnewline
8 & 101.8 & 102.060839106717 & -0.260839106716986 \tabularnewline
9 & 98.5 & 101.855523442838 & -3.35552344283822 \tabularnewline
10 & 98.4 & 99.2142725506757 & -0.814272550675739 \tabularnewline
11 & 97.5 & 98.5733298966985 & -1.07332989669847 \tabularnewline
12 & 97.7 & 97.7284740655493 & -0.0284740655493323 \tabularnewline
13 & 98.3 & 97.7060611239273 & 0.593938876072684 \tabularnewline
14 & 99.6 & 98.1735713687641 & 1.42642863123586 \tabularnewline
15 & 99.4 & 99.2963636719736 & 0.103636328026369 \tabularnewline
16 & 96.7 & 99.3779394822826 & -2.67793948228255 \tabularnewline
17 & 96.9 & 97.2700388321374 & -0.370038832137425 \tabularnewline
18 & 96.1 & 96.9787682115719 & -0.878768211571895 \tabularnewline
19 & 97.9 & 96.2870587473534 & 1.61294125264664 \tabularnewline
20 & 99.2 & 97.556661715454 & 1.64333828454602 \tabularnewline
21 & 97.8 & 98.8501912598373 & -1.05019125983726 \tabularnewline
22 & 94.9 & 98.023548666144 & -3.12354866614398 \tabularnewline
23 & 93.3 & 95.5648933053018 & -2.26489330530184 \tabularnewline
24 & 91.5 & 93.7821158742429 & -2.28211587424286 \tabularnewline
25 & 89.1 & 91.98578195152 & -2.88578195152 \tabularnewline
26 & 92.3 & 89.714281160695 & 2.58571883930492 \tabularnewline
27 & 91.8 & 91.7495916890038 & 0.0504083109961755 \tabularnewline
28 & 92.1 & 91.7892698491058 & 0.310730150894145 \tabularnewline
29 & 94.4 & 92.033856514123 & 2.36614348587692 \tabularnewline
30 & 92.8 & 93.8963315346435 & -1.0963315346435 \tabularnewline
31 & 92.6 & 93.033370302719 & -0.433370302719069 \tabularnewline
32 & 92.3 & 92.692249247184 & -0.392249247184054 \tabularnewline
33 & 92.1 & 92.3834960253026 & -0.28349602530264 \tabularnewline
34 & 89.8 & 92.1603463014188 & -2.36034630141879 \tabularnewline
35 & 87.4 & 90.3024344493229 & -2.90243444932288 \tabularnewline
36 & 87.7 & 88.0178258899403 & -0.317825889940295 \tabularnewline
37 & 86.3 & 87.7676539183664 & -1.46765391836644 \tabularnewline
38 & 89.1 & 86.612411737137 & 2.48758826286304 \tabularnewline
39 & 90.4 & 88.570480226464 & 1.82951977353595 \tabularnewline
40 & 87.1 & 90.0105597897269 & -2.9105597897269 \tabularnewline
41 & 86.7 & 87.7195554882323 & -1.01955548823227 \tabularnewline
42 & 84.4 & 86.9170273912672 & -2.51702739126721 \tabularnewline
43 & 88.4 & 84.9357863252955 & 3.46421367470451 \tabularnewline
44 & 88.9 & 87.6625911337922 & 1.23740886620779 \tabularnewline
45 & 88.5 & 88.6365993109119 & -0.136599310911862 \tabularnewline
46 & 87.2 & 88.5290771737667 & -1.32907717376668 \tabularnewline
47 & 86.2 & 87.4829136375064 & -1.28291363750637 \tabularnewline
48 & 83.4 & 86.4730870493884 & -3.07308704938842 \tabularnewline
49 & 87.5 & 84.0541518074923 & 3.44584819250774 \tabularnewline
50 & 85.7 & 86.766500496977 & -1.06650049697699 \tabularnewline
51 & 87.4 & 85.927020327305 & 1.47297967269502 \tabularnewline
52 & 86.8 & 87.086454597671 & -0.286454597670925 \tabularnewline
53 & 87.9 & 86.860976077091 & 1.039023922909 \tabularnewline
54 & 85.9 & 87.6788284658797 & -1.77882846587966 \tabularnewline
55 & 87.7 & 86.2786498193748 & 1.42135018062523 \tabularnewline
56 & 87 & 87.3974446949297 & -0.397444694929661 \tabularnewline
57 & 86.8 & 87.0846019528208 & -0.284601952820765 \tabularnewline
58 & 86.2 & 86.8605817143678 & -0.660581714367822 \tabularnewline
59 & 86.1 & 86.3406145402019 & -0.240614540201875 \tabularnewline
60 & 87.5 & 86.1512183461947 & 1.34878165380532 \tabularnewline
61 & 85.7 & 87.2128919739111 & -1.51289197391114 \tabularnewline
62 & 88.9 & 86.0220413230635 & 2.87795867693646 \tabularnewline
63 & 89.8 & 88.2873841384413 & 1.5126158615587 \tabularnewline
64 & 91.4 & 89.4780174515144 & 1.92198254848557 \tabularnewline
65 & 95.2 & 90.9908777272318 & 4.20912227276823 \tabularnewline
66 & 94.1 & 94.3040263752909 & -0.204026375290866 \tabularnewline
67 & 96.8 & 94.1434300167967 & 2.65656998320331 \tabularnewline
68 & 96.1 & 96.2345099802533 & -0.134509980253256 \tabularnewline
69 & 96.6 & 96.1286324289857 & 0.471367571014326 \tabularnewline
70 & 94.2 & 96.4996624750237 & -2.29966247502372 \tabularnewline
71 & 93.9 & 94.6895170037433 & -0.78951700374327 \tabularnewline
72 & 96.5 & 94.0680603141871 & 2.43193968581289 \tabularnewline
73 & 93.4 & 95.982325845113 & -2.58232584511289 \tabularnewline
74 & 95 & 93.949686062245 & 1.05031393775495 \tabularnewline
75 & 95.2 & 94.7764252200557 & 0.423574779944303 \tabularnewline
76 & 94 & 95.109835874007 & -1.10983587400695 \tabularnewline
77 & 97 & 94.2362449001065 & 2.76375509989349 \tabularnewline
78 & 96.9 & 96.4116940506384 & 0.488305949361603 \tabularnewline
79 & 96.3 & 96.7960568918971 & -0.49605689189714 \tabularnewline
80 & 96.3 & 96.4055930102982 & -0.10559301029825 \tabularnewline
81 & 97.3 & 96.322477026337 & 0.977522973662957 \tabularnewline
82 & 95.7 & 97.0919198480843 & -1.39191984808431 \tabularnewline
83 & 96.4 & 95.9962906256398 & 0.403709374360247 \tabularnewline
84 & 95.1 & 96.3140645179602 & -1.21406451796025 \tabularnewline
85 & 94.6 & 95.3584315009866 & -0.758431500986589 \tabularnewline
86 & 95.9 & 94.7614433074156 & 1.13855669258437 \tabularnewline
87 & 96.2 & 95.6576414509525 & 0.542358549047506 \tabularnewline
88 & 94.3 & 96.0845510005195 & -1.78455100051953 \tabularnewline
89 & 98.3 & 94.6798679450959 & 3.6201320549041 \tabularnewline
90 & 95.9 & 97.5294016117938 & -1.62940161179375 \tabularnewline
91 & 92.1 & 96.2468421142505 & -4.1468421142505 \tabularnewline
92 & 94.6 & 92.9827163763428 & 1.61728362365724 \tabularnewline
93 & 94.7 & 94.2557373778743 & 0.44426262212572 \tabularnewline
94 & 96.7 & 94.6054321623194 & 2.09456783768056 \tabularnewline
95 & 97.5 & 96.2541404083537 & 1.24585959164631 \tabularnewline
96 & 96.2 & 97.2348004496263 & -1.03480044962632 \tabularnewline
97 & 97.1 & 96.420272505672 & 0.67972749432802 \tabularnewline
98 & 95.9 & 96.9553099987502 & -1.05530999875016 \tabularnewline
99 & 94.5 & 96.124638265058 & -1.62463826505808 \tabularnewline
100 & 99.4 & 94.845828165792 & 4.55417183420811 \tabularnewline
101 & 101.3 & 98.4305774977736 & 2.86942250222636 \tabularnewline
102 & 101.4 & 100.689201188862 & 0.710798811138105 \tabularnewline
103 & 100.9 & 101.248696011666 & -0.34869601166622 \tabularnewline
104 & 101.4 & 100.974225078116 & 0.425774921884425 \tabularnewline
105 & 103.1 & 101.309367541414 & 1.79063245858622 \tabularnewline
106 & 102.4 & 102.718837527049 & -0.318837527049027 \tabularnewline
107 & 101.1 & 102.467869260214 & -1.36786926021409 \tabularnewline
108 & 102 & 101.39117110404 & 0.608828895959775 \tabularnewline
109 & 103.9 & 101.870401808883 & 2.02959819111693 \tabularnewline
110 & 101.7 & 103.467970153834 & -1.76797015383396 \tabularnewline
111 & 101.2 & 102.076338467846 & -0.876338467845557 \tabularnewline
112 & 101.9 & 101.386541540641 & 0.513458459359214 \tabularnewline
113 & 101.1 & 101.790702809955 & -0.690702809954956 \tabularnewline
114 & 103.1 & 101.247026258713 & 1.85297374128665 \tabularnewline
115 & 103.3 & 102.705567267501 & 0.594432732499257 \tabularnewline
116 & 101.4 & 103.173466244155 & -1.77346624415524 \tabularnewline
117 & 102.8 & 101.777508391561 & 1.02249160843928 \tabularnewline
118 & 103 & 102.582347612334 & 0.41765238766645 \tabularnewline
119 & 102.6 & 102.911096542368 & -0.311096542367778 \tabularnewline
120 & 102.2 & 102.666221477695 & -0.466221477694532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117261&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]99.912400544201[/C][C]1.48759945579903[/C][/ROW]
[ROW][C]4[/C][C]101.5[/C][C]101.083342561666[/C][C]0.416657438333573[/C][/ROW]
[ROW][C]5[/C][C]101.8[/C][C]101.411308331977[/C][C]0.388691668023284[/C][/ROW]
[ROW][C]6[/C][C]101.5[/C][C]101.717261257781[/C][C]-0.217261257781416[/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]-0.260839106716986[/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]99.2142725506757[/C][C]-0.814272550675739[/C][/ROW]
[ROW][C]11[/C][C]97.5[/C][C]98.5733298966985[/C][C]-1.07332989669847[/C][/ROW]
[ROW][C]12[/C][C]97.7[/C][C]97.7284740655493[/C][C]-0.0284740655493323[/C][/ROW]
[ROW][C]13[/C][C]98.3[/C][C]97.7060611239273[/C][C]0.593938876072684[/C][/ROW]
[ROW][C]14[/C][C]99.6[/C][C]98.1735713687641[/C][C]1.42642863123586[/C][/ROW]
[ROW][C]15[/C][C]99.4[/C][C]99.2963636719736[/C][C]0.103636328026369[/C][/ROW]
[ROW][C]16[/C][C]96.7[/C][C]99.3779394822826[/C][C]-2.67793948228255[/C][/ROW]
[ROW][C]17[/C][C]96.9[/C][C]97.2700388321374[/C][C]-0.370038832137425[/C][/ROW]
[ROW][C]18[/C][C]96.1[/C][C]96.9787682115719[/C][C]-0.878768211571895[/C][/ROW]
[ROW][C]19[/C][C]97.9[/C][C]96.2870587473534[/C][C]1.61294125264664[/C][/ROW]
[ROW][C]20[/C][C]99.2[/C][C]97.556661715454[/C][C]1.64333828454602[/C][/ROW]
[ROW][C]21[/C][C]97.8[/C][C]98.8501912598373[/C][C]-1.05019125983726[/C][/ROW]
[ROW][C]22[/C][C]94.9[/C][C]98.023548666144[/C][C]-3.12354866614398[/C][/ROW]
[ROW][C]23[/C][C]93.3[/C][C]95.5648933053018[/C][C]-2.26489330530184[/C][/ROW]
[ROW][C]24[/C][C]91.5[/C][C]93.7821158742429[/C][C]-2.28211587424286[/C][/ROW]
[ROW][C]25[/C][C]89.1[/C][C]91.98578195152[/C][C]-2.88578195152[/C][/ROW]
[ROW][C]26[/C][C]92.3[/C][C]89.714281160695[/C][C]2.58571883930492[/C][/ROW]
[ROW][C]27[/C][C]91.8[/C][C]91.7495916890038[/C][C]0.0504083109961755[/C][/ROW]
[ROW][C]28[/C][C]92.1[/C][C]91.7892698491058[/C][C]0.310730150894145[/C][/ROW]
[ROW][C]29[/C][C]94.4[/C][C]92.033856514123[/C][C]2.36614348587692[/C][/ROW]
[ROW][C]30[/C][C]92.8[/C][C]93.8963315346435[/C][C]-1.0963315346435[/C][/ROW]
[ROW][C]31[/C][C]92.6[/C][C]93.033370302719[/C][C]-0.433370302719069[/C][/ROW]
[ROW][C]32[/C][C]92.3[/C][C]92.692249247184[/C][C]-0.392249247184054[/C][/ROW]
[ROW][C]33[/C][C]92.1[/C][C]92.3834960253026[/C][C]-0.28349602530264[/C][/ROW]
[ROW][C]34[/C][C]89.8[/C][C]92.1603463014188[/C][C]-2.36034630141879[/C][/ROW]
[ROW][C]35[/C][C]87.4[/C][C]90.3024344493229[/C][C]-2.90243444932288[/C][/ROW]
[ROW][C]36[/C][C]87.7[/C][C]88.0178258899403[/C][C]-0.317825889940295[/C][/ROW]
[ROW][C]37[/C][C]86.3[/C][C]87.7676539183664[/C][C]-1.46765391836644[/C][/ROW]
[ROW][C]38[/C][C]89.1[/C][C]86.612411737137[/C][C]2.48758826286304[/C][/ROW]
[ROW][C]39[/C][C]90.4[/C][C]88.570480226464[/C][C]1.82951977353595[/C][/ROW]
[ROW][C]40[/C][C]87.1[/C][C]90.0105597897269[/C][C]-2.9105597897269[/C][/ROW]
[ROW][C]41[/C][C]86.7[/C][C]87.7195554882323[/C][C]-1.01955548823227[/C][/ROW]
[ROW][C]42[/C][C]84.4[/C][C]86.9170273912672[/C][C]-2.51702739126721[/C][/ROW]
[ROW][C]43[/C][C]88.4[/C][C]84.9357863252955[/C][C]3.46421367470451[/C][/ROW]
[ROW][C]44[/C][C]88.9[/C][C]87.6625911337922[/C][C]1.23740886620779[/C][/ROW]
[ROW][C]45[/C][C]88.5[/C][C]88.6365993109119[/C][C]-0.136599310911862[/C][/ROW]
[ROW][C]46[/C][C]87.2[/C][C]88.5290771737667[/C][C]-1.32907717376668[/C][/ROW]
[ROW][C]47[/C][C]86.2[/C][C]87.4829136375064[/C][C]-1.28291363750637[/C][/ROW]
[ROW][C]48[/C][C]83.4[/C][C]86.4730870493884[/C][C]-3.07308704938842[/C][/ROW]
[ROW][C]49[/C][C]87.5[/C][C]84.0541518074923[/C][C]3.44584819250774[/C][/ROW]
[ROW][C]50[/C][C]85.7[/C][C]86.766500496977[/C][C]-1.06650049697699[/C][/ROW]
[ROW][C]51[/C][C]87.4[/C][C]85.927020327305[/C][C]1.47297967269502[/C][/ROW]
[ROW][C]52[/C][C]86.8[/C][C]87.086454597671[/C][C]-0.286454597670925[/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]87.6788284658797[/C][C]-1.77882846587966[/C][/ROW]
[ROW][C]55[/C][C]87.7[/C][C]86.2786498193748[/C][C]1.42135018062523[/C][/ROW]
[ROW][C]56[/C][C]87[/C][C]87.3974446949297[/C][C]-0.397444694929661[/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]-0.660581714367822[/C][/ROW]
[ROW][C]59[/C][C]86.1[/C][C]86.3406145402019[/C][C]-0.240614540201875[/C][/ROW]
[ROW][C]60[/C][C]87.5[/C][C]86.1512183461947[/C][C]1.34878165380532[/C][/ROW]
[ROW][C]61[/C][C]85.7[/C][C]87.2128919739111[/C][C]-1.51289197391114[/C][/ROW]
[ROW][C]62[/C][C]88.9[/C][C]86.0220413230635[/C][C]2.87795867693646[/C][/ROW]
[ROW][C]63[/C][C]89.8[/C][C]88.2873841384413[/C][C]1.5126158615587[/C][/ROW]
[ROW][C]64[/C][C]91.4[/C][C]89.4780174515144[/C][C]1.92198254848557[/C][/ROW]
[ROW][C]65[/C][C]95.2[/C][C]90.9908777272318[/C][C]4.20912227276823[/C][/ROW]
[ROW][C]66[/C][C]94.1[/C][C]94.3040263752909[/C][C]-0.204026375290866[/C][/ROW]
[ROW][C]67[/C][C]96.8[/C][C]94.1434300167967[/C][C]2.65656998320331[/C][/ROW]
[ROW][C]68[/C][C]96.1[/C][C]96.2345099802533[/C][C]-0.134509980253256[/C][/ROW]
[ROW][C]69[/C][C]96.6[/C][C]96.1286324289857[/C][C]0.471367571014326[/C][/ROW]
[ROW][C]70[/C][C]94.2[/C][C]96.4996624750237[/C][C]-2.29966247502372[/C][/ROW]
[ROW][C]71[/C][C]93.9[/C][C]94.6895170037433[/C][C]-0.78951700374327[/C][/ROW]
[ROW][C]72[/C][C]96.5[/C][C]94.0680603141871[/C][C]2.43193968581289[/C][/ROW]
[ROW][C]73[/C][C]93.4[/C][C]95.982325845113[/C][C]-2.58232584511289[/C][/ROW]
[ROW][C]74[/C][C]95[/C][C]93.949686062245[/C][C]1.05031393775495[/C][/ROW]
[ROW][C]75[/C][C]95.2[/C][C]94.7764252200557[/C][C]0.423574779944303[/C][/ROW]
[ROW][C]76[/C][C]94[/C][C]95.109835874007[/C][C]-1.10983587400695[/C][/ROW]
[ROW][C]77[/C][C]97[/C][C]94.2362449001065[/C][C]2.76375509989349[/C][/ROW]
[ROW][C]78[/C][C]96.9[/C][C]96.4116940506384[/C][C]0.488305949361603[/C][/ROW]
[ROW][C]79[/C][C]96.3[/C][C]96.7960568918971[/C][C]-0.49605689189714[/C][/ROW]
[ROW][C]80[/C][C]96.3[/C][C]96.4055930102982[/C][C]-0.10559301029825[/C][/ROW]
[ROW][C]81[/C][C]97.3[/C][C]96.322477026337[/C][C]0.977522973662957[/C][/ROW]
[ROW][C]82[/C][C]95.7[/C][C]97.0919198480843[/C][C]-1.39191984808431[/C][/ROW]
[ROW][C]83[/C][C]96.4[/C][C]95.9962906256398[/C][C]0.403709374360247[/C][/ROW]
[ROW][C]84[/C][C]95.1[/C][C]96.3140645179602[/C][C]-1.21406451796025[/C][/ROW]
[ROW][C]85[/C][C]94.6[/C][C]95.3584315009866[/C][C]-0.758431500986589[/C][/ROW]
[ROW][C]86[/C][C]95.9[/C][C]94.7614433074156[/C][C]1.13855669258437[/C][/ROW]
[ROW][C]87[/C][C]96.2[/C][C]95.6576414509525[/C][C]0.542358549047506[/C][/ROW]
[ROW][C]88[/C][C]94.3[/C][C]96.0845510005195[/C][C]-1.78455100051953[/C][/ROW]
[ROW][C]89[/C][C]98.3[/C][C]94.6798679450959[/C][C]3.6201320549041[/C][/ROW]
[ROW][C]90[/C][C]95.9[/C][C]97.5294016117938[/C][C]-1.62940161179375[/C][/ROW]
[ROW][C]91[/C][C]92.1[/C][C]96.2468421142505[/C][C]-4.1468421142505[/C][/ROW]
[ROW][C]92[/C][C]94.6[/C][C]92.9827163763428[/C][C]1.61728362365724[/C][/ROW]
[ROW][C]93[/C][C]94.7[/C][C]94.2557373778743[/C][C]0.44426262212572[/C][/ROW]
[ROW][C]94[/C][C]96.7[/C][C]94.6054321623194[/C][C]2.09456783768056[/C][/ROW]
[ROW][C]95[/C][C]97.5[/C][C]96.2541404083537[/C][C]1.24585959164631[/C][/ROW]
[ROW][C]96[/C][C]96.2[/C][C]97.2348004496263[/C][C]-1.03480044962632[/C][/ROW]
[ROW][C]97[/C][C]97.1[/C][C]96.420272505672[/C][C]0.67972749432802[/C][/ROW]
[ROW][C]98[/C][C]95.9[/C][C]96.9553099987502[/C][C]-1.05530999875016[/C][/ROW]
[ROW][C]99[/C][C]94.5[/C][C]96.124638265058[/C][C]-1.62463826505808[/C][/ROW]
[ROW][C]100[/C][C]99.4[/C][C]94.845828165792[/C][C]4.55417183420811[/C][/ROW]
[ROW][C]101[/C][C]101.3[/C][C]98.4305774977736[/C][C]2.86942250222636[/C][/ROW]
[ROW][C]102[/C][C]101.4[/C][C]100.689201188862[/C][C]0.710798811138105[/C][/ROW]
[ROW][C]103[/C][C]100.9[/C][C]101.248696011666[/C][C]-0.34869601166622[/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]1.79063245858622[/C][/ROW]
[ROW][C]106[/C][C]102.4[/C][C]102.718837527049[/C][C]-0.318837527049027[/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]0.608828895959775[/C][/ROW]
[ROW][C]109[/C][C]103.9[/C][C]101.870401808883[/C][C]2.02959819111693[/C][/ROW]
[ROW][C]110[/C][C]101.7[/C][C]103.467970153834[/C][C]-1.76797015383396[/C][/ROW]
[ROW][C]111[/C][C]101.2[/C][C]102.076338467846[/C][C]-0.876338467845557[/C][/ROW]
[ROW][C]112[/C][C]101.9[/C][C]101.386541540641[/C][C]0.513458459359214[/C][/ROW]
[ROW][C]113[/C][C]101.1[/C][C]101.790702809955[/C][C]-0.690702809954956[/C][/ROW]
[ROW][C]114[/C][C]103.1[/C][C]101.247026258713[/C][C]1.85297374128665[/C][/ROW]
[ROW][C]115[/C][C]103.3[/C][C]102.705567267501[/C][C]0.594432732499257[/C][/ROW]
[ROW][C]116[/C][C]101.4[/C][C]103.173466244155[/C][C]-1.77346624415524[/C][/ROW]
[ROW][C]117[/C][C]102.8[/C][C]101.777508391561[/C][C]1.02249160843928[/C][/ROW]
[ROW][C]118[/C][C]103[/C][C]102.582347612334[/C][C]0.41765238766645[/C][/ROW]
[ROW][C]119[/C][C]102.6[/C][C]102.911096542368[/C][C]-0.311096542367778[/C][/ROW]
[ROW][C]120[/C][C]102.2[/C][C]102.666221477695[/C][C]-0.466221477694532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117261&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117261&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
3101.499.9124005442011.48759945579903
4101.5101.0833425616660.416657438333573
5101.8101.4113083319770.388691668023284
6101.5101.717261257781-0.217261257781416
7102.2101.5462472563230.653752743677074
8101.8102.060839106717-0.260839106716986
998.5101.855523442838-3.35552344283822
1098.499.2142725506757-0.814272550675739
1197.598.5733298966985-1.07332989669847
1297.797.7284740655493-0.0284740655493323
1398.397.70606112392730.593938876072684
1499.698.17357136876411.42642863123586
1599.499.29636367197360.103636328026369
1696.799.3779394822826-2.67793948228255
1796.997.2700388321374-0.370038832137425
1896.196.9787682115719-0.878768211571895
1997.996.28705874735341.61294125264664
2099.297.5566617154541.64333828454602
2197.898.8501912598373-1.05019125983726
2294.998.023548666144-3.12354866614398
2393.395.5648933053018-2.26489330530184
2491.593.7821158742429-2.28211587424286
2589.191.98578195152-2.88578195152
2692.389.7142811606952.58571883930492
2791.891.74959168900380.0504083109961755
2892.191.78926984910580.310730150894145
2994.492.0338565141232.36614348587692
3092.893.8963315346435-1.0963315346435
3192.693.033370302719-0.433370302719069
3292.392.692249247184-0.392249247184054
3392.192.3834960253026-0.28349602530264
3489.892.1603463014188-2.36034630141879
3587.490.3024344493229-2.90243444932288
3687.788.0178258899403-0.317825889940295
3786.387.7676539183664-1.46765391836644
3889.186.6124117371372.48758826286304
3990.488.5704802264641.82951977353595
4087.190.0105597897269-2.9105597897269
4186.787.7195554882323-1.01955548823227
4284.486.9170273912672-2.51702739126721
4388.484.93578632529553.46421367470451
4488.987.66259113379221.23740886620779
4588.588.6365993109119-0.136599310911862
4687.288.5290771737667-1.32907717376668
4786.287.4829136375064-1.28291363750637
4883.486.4730870493884-3.07308704938842
4987.584.05415180749233.44584819250774
5085.786.766500496977-1.06650049697699
5187.485.9270203273051.47297967269502
5286.887.086454597671-0.286454597670925
5387.986.8609760770911.039023922909
5485.987.6788284658797-1.77882846587966
5587.786.27864981937481.42135018062523
568787.3974446949297-0.397444694929661
5786.887.0846019528208-0.284601952820765
5886.286.8605817143678-0.660581714367822
5986.186.3406145402019-0.240614540201875
6087.586.15121834619471.34878165380532
6185.787.2128919739111-1.51289197391114
6288.986.02204132306352.87795867693646
6389.888.28738413844131.5126158615587
6491.489.47801745151441.92198254848557
6595.290.99087772723184.20912227276823
6694.194.3040263752909-0.204026375290866
6796.894.14343001679672.65656998320331
6896.196.2345099802533-0.134509980253256
6996.696.12863242898570.471367571014326
7094.296.4996624750237-2.29966247502372
7193.994.6895170037433-0.78951700374327
7296.594.06806031418712.43193968581289
7393.495.982325845113-2.58232584511289
749593.9496860622451.05031393775495
7595.294.77642522005570.423574779944303
769495.109835874007-1.10983587400695
779794.23624490010652.76375509989349
7896.996.41169405063840.488305949361603
7996.396.7960568918971-0.49605689189714
8096.396.4055930102982-0.10559301029825
8197.396.3224770263370.977522973662957
8295.797.0919198480843-1.39191984808431
8396.495.99629062563980.403709374360247
8495.196.3140645179602-1.21406451796025
8594.695.3584315009866-0.758431500986589
8695.994.76144330741561.13855669258437
8796.295.65764145095250.542358549047506
8894.396.0845510005195-1.78455100051953
8998.394.67986794509593.6201320549041
9095.997.5294016117938-1.62940161179375
9192.196.2468421142505-4.1468421142505
9294.692.98271637634281.61728362365724
9394.794.25573737787430.44426262212572
9496.794.60543216231942.09456783768056
9597.596.25414040835371.24585959164631
9696.297.2348004496263-1.03480044962632
9797.196.4202725056720.67972749432802
9895.996.9553099987502-1.05530999875016
9994.596.124638265058-1.62463826505808
10099.494.8458281657924.55417183420811
101101.398.43057749777362.86942250222636
102101.4100.6892011888620.710798811138105
103100.9101.248696011666-0.34869601166622
104101.4100.9742250781160.425774921884425
105103.1101.3093675414141.79063245858622
106102.4102.718837527049-0.318837527049027
107101.1102.467869260214-1.36786926021409
108102101.391171104040.608828895959775
109103.9101.8704018088832.02959819111693
110101.7103.467970153834-1.76797015383396
111101.2102.076338467846-0.876338467845557
112101.9101.3865415406410.513458459359214
113101.1101.790702809955-0.690702809954956
114103.1101.2470262587131.85297374128665
115103.3102.7055672675010.594432732499257
116101.4103.173466244155-1.77346624415524
117102.8101.7775083915611.02249160843928
118103102.5823476123340.41765238766645
119102.6102.911096542368-0.311096542367778
120102.2102.666221477695-0.466221477694532






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
121102.29924210327498.8707257081588105.727758498389
122102.29924210327497.9360162399012106.662467966647
123102.29924210327497.168866366738107.42961783981
124102.29924210327496.5023661807388108.096118025809
125102.29924210327495.904964526839108.693519679709
126102.29924210327495.3587953606117109.239688845936
127102.29924210327494.8525773296345109.745906876914
128102.29924210327494.3786467017783110.21983750477
129102.29924210327493.9315156033562110.666968603192
130102.29924210327493.5070943542505111.091389852298
131102.29924210327493.1022383898959111.496245816652
132102.29924210327492.7144681066102111.884016099938

\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 & 97.9360162399012 & 106.662467966647 \tabularnewline
123 & 102.299242103274 & 97.168866366738 & 107.42961783981 \tabularnewline
124 & 102.299242103274 & 96.5023661807388 & 108.096118025809 \tabularnewline
125 & 102.299242103274 & 95.904964526839 & 108.693519679709 \tabularnewline
126 & 102.299242103274 & 95.3587953606117 & 109.239688845936 \tabularnewline
127 & 102.299242103274 & 94.8525773296345 & 109.745906876914 \tabularnewline
128 & 102.299242103274 & 94.3786467017783 & 110.21983750477 \tabularnewline
129 & 102.299242103274 & 93.9315156033562 & 110.666968603192 \tabularnewline
130 & 102.299242103274 & 93.5070943542505 & 111.091389852298 \tabularnewline
131 & 102.299242103274 & 93.1022383898959 & 111.496245816652 \tabularnewline
132 & 102.299242103274 & 92.7144681066102 & 111.884016099938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117261&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]97.9360162399012[/C][C]106.662467966647[/C][/ROW]
[ROW][C]123[/C][C]102.299242103274[/C][C]97.168866366738[/C][C]107.42961783981[/C][/ROW]
[ROW][C]124[/C][C]102.299242103274[/C][C]96.5023661807388[/C][C]108.096118025809[/C][/ROW]
[ROW][C]125[/C][C]102.299242103274[/C][C]95.904964526839[/C][C]108.693519679709[/C][/ROW]
[ROW][C]126[/C][C]102.299242103274[/C][C]95.3587953606117[/C][C]109.239688845936[/C][/ROW]
[ROW][C]127[/C][C]102.299242103274[/C][C]94.8525773296345[/C][C]109.745906876914[/C][/ROW]
[ROW][C]128[/C][C]102.299242103274[/C][C]94.3786467017783[/C][C]110.21983750477[/C][/ROW]
[ROW][C]129[/C][C]102.299242103274[/C][C]93.9315156033562[/C][C]110.666968603192[/C][/ROW]
[ROW][C]130[/C][C]102.299242103274[/C][C]93.5070943542505[/C][C]111.091389852298[/C][/ROW]
[ROW][C]131[/C][C]102.299242103274[/C][C]93.1022383898959[/C][C]111.496245816652[/C][/ROW]
[ROW][C]132[/C][C]102.299242103274[/C][C]92.7144681066102[/C][C]111.884016099938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117261&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117261&T=3

As an alternative you can also use a QR Code:  
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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
122102.29924210327497.9360162399012106.662467966647
123102.29924210327497.168866366738107.42961783981
124102.29924210327496.5023661807388108.096118025809
125102.29924210327495.904964526839108.693519679709
126102.29924210327495.3587953606117109.239688845936
127102.29924210327494.8525773296345109.745906876914
128102.29924210327494.3786467017783110.21983750477
129102.29924210327493.9315156033562110.666968603192
130102.29924210327493.5070943542505111.091389852298
131102.29924210327493.1022383898959111.496245816652
132102.29924210327492.7144681066102111.884016099938


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')