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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 computationWed, 22 Dec 2010 06:49:03 +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/2010/Dec/22/t1293000529fn6i2deyn9pasje.htm/, Retrieved Mon, 06 May 2024 08:53:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114048, Retrieved Mon, 06 May 2024 08:53:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W102
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] [] [2010-12-22 06:49:03] [5815de052410d7754c978b0de903e641] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100,4
97,7
97
96,5
98,4
106,3
103,1
102,4
95
98,1
106,1
99,1
101,2
95,5
99,8
97,1
97,5
96,8
97,7
100,9
94,3
99,5
100,8
97
99,2
101
102,3
97
91,2
97,6
95,7
100,5
94,4
102,9
105,1
98,8
100,7
99,6
107,7
102,9
101,6
102,7
110,5
109,8
94,3
102,5
105
102,3
107,7
100,3
99,5
95
97,7
96,3
97,8
106,4
96,1
106,2
114,7
111,9
121
117,7
115,4
114,3
109,5
108,1
108,2
99,1
101,2
98,1
95,5
97,9
98,2
98,7
95,6
95,8
94,4
96,5
103,3
104,3
104,5
102,3
103,8
103,1
102,2
106,3
102,1
94
102,6
102,6
106,7
107,9
109,3
105,9
109,1
108,5
111,7
109,8
109,1
108,5
108,5
106,2
117,1
109,8
115,2
115,9
119,2
121
118,6
117,6
114,6
110,6
102,5
101,6
107,4
105,8
102,8
104
100,4
100,6

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114048&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114048&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114048&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.632771665684048
beta-6.91178884959509e-18
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.632771665684048 \tabularnewline
beta & -6.91178884959509e-18 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114048&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]0.632771665684048[/C][/ROW]
[ROW][C]beta[/C][C]-6.91178884959509e-18[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114048&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114048&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
alpha0.632771665684048
beta-6.91178884959509e-18
gammaFALSE






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
39797.7-0.700000000000003
496.597.2570598340212-0.757059834021163
598.496.77801382182511.62198617817491
6106.397.80436071750538.49563928249466
7103.1103.180160537340-0.0801605373403191
8102.4103.129437220605-0.729437220605348
995102.667870015511-7.66787001551097
1098.197.81585913354730.284140866452660
11106.197.99565542290158.1043445770985
1299.1103.123855040230-4.02385504022959
13101.2100.5776735839520.622326416047642
1495.5100.971464106834-5.47146410683401
1599.897.50927665022222.29072334977782
1697.198.9587814798824-1.85878147988244
1797.597.7825972267146-0.282597226714557
1896.897.6037777088487-0.803777708848699
1997.797.09516994918080.604830050819203
20100.997.47788926789343.42211073210657
2194.399.6433039760038-5.34330397600377
2299.596.26221261885173.23778738114834
23100.898.31099273315172.48900726684830
249799.885966007295-2.88596600729500
2599.298.05980848975141.14019151024860
2610198.78128937089022.21871062910978
27102.3100.1852265913432.11477340865709
2897101.523395283683-4.52339528368319
2991.298.6611189154796-7.4611189154796
3097.693.93993427146483.66006572853517
3195.796.2559201590231-0.555920159023117
32100.595.90414963401074.59585036598928
3394.498.8122735253324-4.41227352533240
34102.996.02031185725426.8796881427458
35105.1100.3735835827264.72641641727374
3698.8103.364325971801-4.564325971801
37100.7100.4761498239000.223850176100484
3899.6100.617795872694-1.01779587269429
39107.799.97376348300327.72623651699683
40102.9104.862707033332-1.96270703333217
41101.6103.620761634601-2.02076163460079
42102.7102.3420809291240.357919070875980
43110.5102.5685619757827.9314380242177
44109.8107.5873512256362.21264877436367
4594.3108.987452676164-14.6874526761642
46102.599.69364878161222.80635121838785
47105101.4694283165663.53057168343412
48102.3103.703474041509-1.40347404150943
49107.7102.8153954345194.88460456548083
50100.3105.906234801626-5.60623480162639
5199.5102.358768267985-2.85876826798538
5295100.549820709248-5.54982070924757
5397.797.03805141480920.661948585190842
5496.397.4569137236576-1.15691372365757
5597.896.7248514996861.07514850031397
56106.497.40517500708748.9948249929126
5796.1103.096845400389-6.99684540038922
58106.298.66943988185127.53056011814884
59114.7103.43456495134611.2654350486539
60111.9110.5630130517381.33698694826174
61121111.4090205099889.59097949001232
62117.7117.4779205774240.222079422575689
63115.4117.618446143562-2.21844614356168
64114.3116.214676282070-1.91467628206981
65109.5115.003123381819-5.50312338181875
66108.1111.520902833040-3.42090283304047
67108.2109.356252449234-1.15625244923416
6899.1108.624608660981-9.524608660981
69101.2102.597706173583-1.39770617358333
7098.1101.713277309988-3.61327730998813
7195.599.4268978079686-3.92689780796856
7297.996.94206814104930.957931858950744
7398.297.54822027904930.651779720950657
7498.797.96064801873440.739351981265628
7595.698.4284890034466-2.82848900344663
7695.896.6387013053667-0.838701305366698
7794.496.1079948833584-1.70799488335842
7896.595.02722411603591.47277588396412
79103.395.95915496531127.34084503468884
80104.3100.6042337054403.69576629456031
81104.5102.9428098996281.55719010037242
82102.3103.928155673227-1.62815567322694
83103.8102.8979048958860.902095104113798
84103.1103.468725117522-0.36872511752172
85102.2103.235406310728-1.03540631072795
86106.3102.5802305348293.71976946517114
87102.1104.933995255266-2.83399525526586
8894103.140723357051-9.14072335705059
89102.697.35673261285265.24326738714738
90102.6100.6745236510451.92547634895529
91106.7101.8929105276084.80708947239162
92107.9104.9347005401462.96529945985412
93109.3106.8110580186102.48894198139021
94105.9108.385989981965-2.48598998196502
95109.1106.8129259602032.28707403979683
96108.5108.2601216099080.239878390091846
97111.7108.4119098583683.28809014163183
98109.8110.492520134208-0.692520134207854
99109.1110.054313015365-0.954313015365415
100108.5109.450450779049-0.950450779048666
101108.5108.849032456439-0.349032456439346
102106.2108.628174607600-2.42817460760043
103117.1107.09169451657710.0083054834226
104109.8113.424666647997-3.6246666479975
105115.2111.1310802955954.0689197044053
106115.9113.7057773944862.19422260551411
107119.2115.0942192874594.10578071254136
108121117.6922409878673.30775901213312
109118.6119.785297167656-1.18529716765578
110117.6119.035274704548-1.43527470454765
111114.6118.127073539037-3.52707353903685
112110.6115.895241340750-5.29524134075037
113102.5112.544562657365-10.0445626573647
114101.6106.188648013596-4.58864801359627
115107.4103.2850815667954.11491843320485
116105.8105.888885357928-0.0888853579281772
117102.8105.832641221937-3.03264122193704
118104103.9136717845100.0863282154901697
119100.4103.968297833221-3.56829783322107
120100.6101.710380069637-1.11038006963700

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 97 & 97.7 & -0.700000000000003 \tabularnewline
4 & 96.5 & 97.2570598340212 & -0.757059834021163 \tabularnewline
5 & 98.4 & 96.7780138218251 & 1.62198617817491 \tabularnewline
6 & 106.3 & 97.8043607175053 & 8.49563928249466 \tabularnewline
7 & 103.1 & 103.180160537340 & -0.0801605373403191 \tabularnewline
8 & 102.4 & 103.129437220605 & -0.729437220605348 \tabularnewline
9 & 95 & 102.667870015511 & -7.66787001551097 \tabularnewline
10 & 98.1 & 97.8158591335473 & 0.284140866452660 \tabularnewline
11 & 106.1 & 97.9956554229015 & 8.1043445770985 \tabularnewline
12 & 99.1 & 103.123855040230 & -4.02385504022959 \tabularnewline
13 & 101.2 & 100.577673583952 & 0.622326416047642 \tabularnewline
14 & 95.5 & 100.971464106834 & -5.47146410683401 \tabularnewline
15 & 99.8 & 97.5092766502222 & 2.29072334977782 \tabularnewline
16 & 97.1 & 98.9587814798824 & -1.85878147988244 \tabularnewline
17 & 97.5 & 97.7825972267146 & -0.282597226714557 \tabularnewline
18 & 96.8 & 97.6037777088487 & -0.803777708848699 \tabularnewline
19 & 97.7 & 97.0951699491808 & 0.604830050819203 \tabularnewline
20 & 100.9 & 97.4778892678934 & 3.42211073210657 \tabularnewline
21 & 94.3 & 99.6433039760038 & -5.34330397600377 \tabularnewline
22 & 99.5 & 96.2622126188517 & 3.23778738114834 \tabularnewline
23 & 100.8 & 98.3109927331517 & 2.48900726684830 \tabularnewline
24 & 97 & 99.885966007295 & -2.88596600729500 \tabularnewline
25 & 99.2 & 98.0598084897514 & 1.14019151024860 \tabularnewline
26 & 101 & 98.7812893708902 & 2.21871062910978 \tabularnewline
27 & 102.3 & 100.185226591343 & 2.11477340865709 \tabularnewline
28 & 97 & 101.523395283683 & -4.52339528368319 \tabularnewline
29 & 91.2 & 98.6611189154796 & -7.4611189154796 \tabularnewline
30 & 97.6 & 93.9399342714648 & 3.66006572853517 \tabularnewline
31 & 95.7 & 96.2559201590231 & -0.555920159023117 \tabularnewline
32 & 100.5 & 95.9041496340107 & 4.59585036598928 \tabularnewline
33 & 94.4 & 98.8122735253324 & -4.41227352533240 \tabularnewline
34 & 102.9 & 96.0203118572542 & 6.8796881427458 \tabularnewline
35 & 105.1 & 100.373583582726 & 4.72641641727374 \tabularnewline
36 & 98.8 & 103.364325971801 & -4.564325971801 \tabularnewline
37 & 100.7 & 100.476149823900 & 0.223850176100484 \tabularnewline
38 & 99.6 & 100.617795872694 & -1.01779587269429 \tabularnewline
39 & 107.7 & 99.9737634830032 & 7.72623651699683 \tabularnewline
40 & 102.9 & 104.862707033332 & -1.96270703333217 \tabularnewline
41 & 101.6 & 103.620761634601 & -2.02076163460079 \tabularnewline
42 & 102.7 & 102.342080929124 & 0.357919070875980 \tabularnewline
43 & 110.5 & 102.568561975782 & 7.9314380242177 \tabularnewline
44 & 109.8 & 107.587351225636 & 2.21264877436367 \tabularnewline
45 & 94.3 & 108.987452676164 & -14.6874526761642 \tabularnewline
46 & 102.5 & 99.6936487816122 & 2.80635121838785 \tabularnewline
47 & 105 & 101.469428316566 & 3.53057168343412 \tabularnewline
48 & 102.3 & 103.703474041509 & -1.40347404150943 \tabularnewline
49 & 107.7 & 102.815395434519 & 4.88460456548083 \tabularnewline
50 & 100.3 & 105.906234801626 & -5.60623480162639 \tabularnewline
51 & 99.5 & 102.358768267985 & -2.85876826798538 \tabularnewline
52 & 95 & 100.549820709248 & -5.54982070924757 \tabularnewline
53 & 97.7 & 97.0380514148092 & 0.661948585190842 \tabularnewline
54 & 96.3 & 97.4569137236576 & -1.15691372365757 \tabularnewline
55 & 97.8 & 96.724851499686 & 1.07514850031397 \tabularnewline
56 & 106.4 & 97.4051750070874 & 8.9948249929126 \tabularnewline
57 & 96.1 & 103.096845400389 & -6.99684540038922 \tabularnewline
58 & 106.2 & 98.6694398818512 & 7.53056011814884 \tabularnewline
59 & 114.7 & 103.434564951346 & 11.2654350486539 \tabularnewline
60 & 111.9 & 110.563013051738 & 1.33698694826174 \tabularnewline
61 & 121 & 111.409020509988 & 9.59097949001232 \tabularnewline
62 & 117.7 & 117.477920577424 & 0.222079422575689 \tabularnewline
63 & 115.4 & 117.618446143562 & -2.21844614356168 \tabularnewline
64 & 114.3 & 116.214676282070 & -1.91467628206981 \tabularnewline
65 & 109.5 & 115.003123381819 & -5.50312338181875 \tabularnewline
66 & 108.1 & 111.520902833040 & -3.42090283304047 \tabularnewline
67 & 108.2 & 109.356252449234 & -1.15625244923416 \tabularnewline
68 & 99.1 & 108.624608660981 & -9.524608660981 \tabularnewline
69 & 101.2 & 102.597706173583 & -1.39770617358333 \tabularnewline
70 & 98.1 & 101.713277309988 & -3.61327730998813 \tabularnewline
71 & 95.5 & 99.4268978079686 & -3.92689780796856 \tabularnewline
72 & 97.9 & 96.9420681410493 & 0.957931858950744 \tabularnewline
73 & 98.2 & 97.5482202790493 & 0.651779720950657 \tabularnewline
74 & 98.7 & 97.9606480187344 & 0.739351981265628 \tabularnewline
75 & 95.6 & 98.4284890034466 & -2.82848900344663 \tabularnewline
76 & 95.8 & 96.6387013053667 & -0.838701305366698 \tabularnewline
77 & 94.4 & 96.1079948833584 & -1.70799488335842 \tabularnewline
78 & 96.5 & 95.0272241160359 & 1.47277588396412 \tabularnewline
79 & 103.3 & 95.9591549653112 & 7.34084503468884 \tabularnewline
80 & 104.3 & 100.604233705440 & 3.69576629456031 \tabularnewline
81 & 104.5 & 102.942809899628 & 1.55719010037242 \tabularnewline
82 & 102.3 & 103.928155673227 & -1.62815567322694 \tabularnewline
83 & 103.8 & 102.897904895886 & 0.902095104113798 \tabularnewline
84 & 103.1 & 103.468725117522 & -0.36872511752172 \tabularnewline
85 & 102.2 & 103.235406310728 & -1.03540631072795 \tabularnewline
86 & 106.3 & 102.580230534829 & 3.71976946517114 \tabularnewline
87 & 102.1 & 104.933995255266 & -2.83399525526586 \tabularnewline
88 & 94 & 103.140723357051 & -9.14072335705059 \tabularnewline
89 & 102.6 & 97.3567326128526 & 5.24326738714738 \tabularnewline
90 & 102.6 & 100.674523651045 & 1.92547634895529 \tabularnewline
91 & 106.7 & 101.892910527608 & 4.80708947239162 \tabularnewline
92 & 107.9 & 104.934700540146 & 2.96529945985412 \tabularnewline
93 & 109.3 & 106.811058018610 & 2.48894198139021 \tabularnewline
94 & 105.9 & 108.385989981965 & -2.48598998196502 \tabularnewline
95 & 109.1 & 106.812925960203 & 2.28707403979683 \tabularnewline
96 & 108.5 & 108.260121609908 & 0.239878390091846 \tabularnewline
97 & 111.7 & 108.411909858368 & 3.28809014163183 \tabularnewline
98 & 109.8 & 110.492520134208 & -0.692520134207854 \tabularnewline
99 & 109.1 & 110.054313015365 & -0.954313015365415 \tabularnewline
100 & 108.5 & 109.450450779049 & -0.950450779048666 \tabularnewline
101 & 108.5 & 108.849032456439 & -0.349032456439346 \tabularnewline
102 & 106.2 & 108.628174607600 & -2.42817460760043 \tabularnewline
103 & 117.1 & 107.091694516577 & 10.0083054834226 \tabularnewline
104 & 109.8 & 113.424666647997 & -3.6246666479975 \tabularnewline
105 & 115.2 & 111.131080295595 & 4.0689197044053 \tabularnewline
106 & 115.9 & 113.705777394486 & 2.19422260551411 \tabularnewline
107 & 119.2 & 115.094219287459 & 4.10578071254136 \tabularnewline
108 & 121 & 117.692240987867 & 3.30775901213312 \tabularnewline
109 & 118.6 & 119.785297167656 & -1.18529716765578 \tabularnewline
110 & 117.6 & 119.035274704548 & -1.43527470454765 \tabularnewline
111 & 114.6 & 118.127073539037 & -3.52707353903685 \tabularnewline
112 & 110.6 & 115.895241340750 & -5.29524134075037 \tabularnewline
113 & 102.5 & 112.544562657365 & -10.0445626573647 \tabularnewline
114 & 101.6 & 106.188648013596 & -4.58864801359627 \tabularnewline
115 & 107.4 & 103.285081566795 & 4.11491843320485 \tabularnewline
116 & 105.8 & 105.888885357928 & -0.0888853579281772 \tabularnewline
117 & 102.8 & 105.832641221937 & -3.03264122193704 \tabularnewline
118 & 104 & 103.913671784510 & 0.0863282154901697 \tabularnewline
119 & 100.4 & 103.968297833221 & -3.56829783322107 \tabularnewline
120 & 100.6 & 101.710380069637 & -1.11038006963700 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114048&T=2

[TABLE]
[ROW][C]Interpolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Residuals[/C][/ROW]
[ROW][C]3[/C][C]97[/C][C]97.7[/C][C]-0.700000000000003[/C][/ROW]
[ROW][C]4[/C][C]96.5[/C][C]97.2570598340212[/C][C]-0.757059834021163[/C][/ROW]
[ROW][C]5[/C][C]98.4[/C][C]96.7780138218251[/C][C]1.62198617817491[/C][/ROW]
[ROW][C]6[/C][C]106.3[/C][C]97.8043607175053[/C][C]8.49563928249466[/C][/ROW]
[ROW][C]7[/C][C]103.1[/C][C]103.180160537340[/C][C]-0.0801605373403191[/C][/ROW]
[ROW][C]8[/C][C]102.4[/C][C]103.129437220605[/C][C]-0.729437220605348[/C][/ROW]
[ROW][C]9[/C][C]95[/C][C]102.667870015511[/C][C]-7.66787001551097[/C][/ROW]
[ROW][C]10[/C][C]98.1[/C][C]97.8158591335473[/C][C]0.284140866452660[/C][/ROW]
[ROW][C]11[/C][C]106.1[/C][C]97.9956554229015[/C][C]8.1043445770985[/C][/ROW]
[ROW][C]12[/C][C]99.1[/C][C]103.123855040230[/C][C]-4.02385504022959[/C][/ROW]
[ROW][C]13[/C][C]101.2[/C][C]100.577673583952[/C][C]0.622326416047642[/C][/ROW]
[ROW][C]14[/C][C]95.5[/C][C]100.971464106834[/C][C]-5.47146410683401[/C][/ROW]
[ROW][C]15[/C][C]99.8[/C][C]97.5092766502222[/C][C]2.29072334977782[/C][/ROW]
[ROW][C]16[/C][C]97.1[/C][C]98.9587814798824[/C][C]-1.85878147988244[/C][/ROW]
[ROW][C]17[/C][C]97.5[/C][C]97.7825972267146[/C][C]-0.282597226714557[/C][/ROW]
[ROW][C]18[/C][C]96.8[/C][C]97.6037777088487[/C][C]-0.803777708848699[/C][/ROW]
[ROW][C]19[/C][C]97.7[/C][C]97.0951699491808[/C][C]0.604830050819203[/C][/ROW]
[ROW][C]20[/C][C]100.9[/C][C]97.4778892678934[/C][C]3.42211073210657[/C][/ROW]
[ROW][C]21[/C][C]94.3[/C][C]99.6433039760038[/C][C]-5.34330397600377[/C][/ROW]
[ROW][C]22[/C][C]99.5[/C][C]96.2622126188517[/C][C]3.23778738114834[/C][/ROW]
[ROW][C]23[/C][C]100.8[/C][C]98.3109927331517[/C][C]2.48900726684830[/C][/ROW]
[ROW][C]24[/C][C]97[/C][C]99.885966007295[/C][C]-2.88596600729500[/C][/ROW]
[ROW][C]25[/C][C]99.2[/C][C]98.0598084897514[/C][C]1.14019151024860[/C][/ROW]
[ROW][C]26[/C][C]101[/C][C]98.7812893708902[/C][C]2.21871062910978[/C][/ROW]
[ROW][C]27[/C][C]102.3[/C][C]100.185226591343[/C][C]2.11477340865709[/C][/ROW]
[ROW][C]28[/C][C]97[/C][C]101.523395283683[/C][C]-4.52339528368319[/C][/ROW]
[ROW][C]29[/C][C]91.2[/C][C]98.6611189154796[/C][C]-7.4611189154796[/C][/ROW]
[ROW][C]30[/C][C]97.6[/C][C]93.9399342714648[/C][C]3.66006572853517[/C][/ROW]
[ROW][C]31[/C][C]95.7[/C][C]96.2559201590231[/C][C]-0.555920159023117[/C][/ROW]
[ROW][C]32[/C][C]100.5[/C][C]95.9041496340107[/C][C]4.59585036598928[/C][/ROW]
[ROW][C]33[/C][C]94.4[/C][C]98.8122735253324[/C][C]-4.41227352533240[/C][/ROW]
[ROW][C]34[/C][C]102.9[/C][C]96.0203118572542[/C][C]6.8796881427458[/C][/ROW]
[ROW][C]35[/C][C]105.1[/C][C]100.373583582726[/C][C]4.72641641727374[/C][/ROW]
[ROW][C]36[/C][C]98.8[/C][C]103.364325971801[/C][C]-4.564325971801[/C][/ROW]
[ROW][C]37[/C][C]100.7[/C][C]100.476149823900[/C][C]0.223850176100484[/C][/ROW]
[ROW][C]38[/C][C]99.6[/C][C]100.617795872694[/C][C]-1.01779587269429[/C][/ROW]
[ROW][C]39[/C][C]107.7[/C][C]99.9737634830032[/C][C]7.72623651699683[/C][/ROW]
[ROW][C]40[/C][C]102.9[/C][C]104.862707033332[/C][C]-1.96270703333217[/C][/ROW]
[ROW][C]41[/C][C]101.6[/C][C]103.620761634601[/C][C]-2.02076163460079[/C][/ROW]
[ROW][C]42[/C][C]102.7[/C][C]102.342080929124[/C][C]0.357919070875980[/C][/ROW]
[ROW][C]43[/C][C]110.5[/C][C]102.568561975782[/C][C]7.9314380242177[/C][/ROW]
[ROW][C]44[/C][C]109.8[/C][C]107.587351225636[/C][C]2.21264877436367[/C][/ROW]
[ROW][C]45[/C][C]94.3[/C][C]108.987452676164[/C][C]-14.6874526761642[/C][/ROW]
[ROW][C]46[/C][C]102.5[/C][C]99.6936487816122[/C][C]2.80635121838785[/C][/ROW]
[ROW][C]47[/C][C]105[/C][C]101.469428316566[/C][C]3.53057168343412[/C][/ROW]
[ROW][C]48[/C][C]102.3[/C][C]103.703474041509[/C][C]-1.40347404150943[/C][/ROW]
[ROW][C]49[/C][C]107.7[/C][C]102.815395434519[/C][C]4.88460456548083[/C][/ROW]
[ROW][C]50[/C][C]100.3[/C][C]105.906234801626[/C][C]-5.60623480162639[/C][/ROW]
[ROW][C]51[/C][C]99.5[/C][C]102.358768267985[/C][C]-2.85876826798538[/C][/ROW]
[ROW][C]52[/C][C]95[/C][C]100.549820709248[/C][C]-5.54982070924757[/C][/ROW]
[ROW][C]53[/C][C]97.7[/C][C]97.0380514148092[/C][C]0.661948585190842[/C][/ROW]
[ROW][C]54[/C][C]96.3[/C][C]97.4569137236576[/C][C]-1.15691372365757[/C][/ROW]
[ROW][C]55[/C][C]97.8[/C][C]96.724851499686[/C][C]1.07514850031397[/C][/ROW]
[ROW][C]56[/C][C]106.4[/C][C]97.4051750070874[/C][C]8.9948249929126[/C][/ROW]
[ROW][C]57[/C][C]96.1[/C][C]103.096845400389[/C][C]-6.99684540038922[/C][/ROW]
[ROW][C]58[/C][C]106.2[/C][C]98.6694398818512[/C][C]7.53056011814884[/C][/ROW]
[ROW][C]59[/C][C]114.7[/C][C]103.434564951346[/C][C]11.2654350486539[/C][/ROW]
[ROW][C]60[/C][C]111.9[/C][C]110.563013051738[/C][C]1.33698694826174[/C][/ROW]
[ROW][C]61[/C][C]121[/C][C]111.409020509988[/C][C]9.59097949001232[/C][/ROW]
[ROW][C]62[/C][C]117.7[/C][C]117.477920577424[/C][C]0.222079422575689[/C][/ROW]
[ROW][C]63[/C][C]115.4[/C][C]117.618446143562[/C][C]-2.21844614356168[/C][/ROW]
[ROW][C]64[/C][C]114.3[/C][C]116.214676282070[/C][C]-1.91467628206981[/C][/ROW]
[ROW][C]65[/C][C]109.5[/C][C]115.003123381819[/C][C]-5.50312338181875[/C][/ROW]
[ROW][C]66[/C][C]108.1[/C][C]111.520902833040[/C][C]-3.42090283304047[/C][/ROW]
[ROW][C]67[/C][C]108.2[/C][C]109.356252449234[/C][C]-1.15625244923416[/C][/ROW]
[ROW][C]68[/C][C]99.1[/C][C]108.624608660981[/C][C]-9.524608660981[/C][/ROW]
[ROW][C]69[/C][C]101.2[/C][C]102.597706173583[/C][C]-1.39770617358333[/C][/ROW]
[ROW][C]70[/C][C]98.1[/C][C]101.713277309988[/C][C]-3.61327730998813[/C][/ROW]
[ROW][C]71[/C][C]95.5[/C][C]99.4268978079686[/C][C]-3.92689780796856[/C][/ROW]
[ROW][C]72[/C][C]97.9[/C][C]96.9420681410493[/C][C]0.957931858950744[/C][/ROW]
[ROW][C]73[/C][C]98.2[/C][C]97.5482202790493[/C][C]0.651779720950657[/C][/ROW]
[ROW][C]74[/C][C]98.7[/C][C]97.9606480187344[/C][C]0.739351981265628[/C][/ROW]
[ROW][C]75[/C][C]95.6[/C][C]98.4284890034466[/C][C]-2.82848900344663[/C][/ROW]
[ROW][C]76[/C][C]95.8[/C][C]96.6387013053667[/C][C]-0.838701305366698[/C][/ROW]
[ROW][C]77[/C][C]94.4[/C][C]96.1079948833584[/C][C]-1.70799488335842[/C][/ROW]
[ROW][C]78[/C][C]96.5[/C][C]95.0272241160359[/C][C]1.47277588396412[/C][/ROW]
[ROW][C]79[/C][C]103.3[/C][C]95.9591549653112[/C][C]7.34084503468884[/C][/ROW]
[ROW][C]80[/C][C]104.3[/C][C]100.604233705440[/C][C]3.69576629456031[/C][/ROW]
[ROW][C]81[/C][C]104.5[/C][C]102.942809899628[/C][C]1.55719010037242[/C][/ROW]
[ROW][C]82[/C][C]102.3[/C][C]103.928155673227[/C][C]-1.62815567322694[/C][/ROW]
[ROW][C]83[/C][C]103.8[/C][C]102.897904895886[/C][C]0.902095104113798[/C][/ROW]
[ROW][C]84[/C][C]103.1[/C][C]103.468725117522[/C][C]-0.36872511752172[/C][/ROW]
[ROW][C]85[/C][C]102.2[/C][C]103.235406310728[/C][C]-1.03540631072795[/C][/ROW]
[ROW][C]86[/C][C]106.3[/C][C]102.580230534829[/C][C]3.71976946517114[/C][/ROW]
[ROW][C]87[/C][C]102.1[/C][C]104.933995255266[/C][C]-2.83399525526586[/C][/ROW]
[ROW][C]88[/C][C]94[/C][C]103.140723357051[/C][C]-9.14072335705059[/C][/ROW]
[ROW][C]89[/C][C]102.6[/C][C]97.3567326128526[/C][C]5.24326738714738[/C][/ROW]
[ROW][C]90[/C][C]102.6[/C][C]100.674523651045[/C][C]1.92547634895529[/C][/ROW]
[ROW][C]91[/C][C]106.7[/C][C]101.892910527608[/C][C]4.80708947239162[/C][/ROW]
[ROW][C]92[/C][C]107.9[/C][C]104.934700540146[/C][C]2.96529945985412[/C][/ROW]
[ROW][C]93[/C][C]109.3[/C][C]106.811058018610[/C][C]2.48894198139021[/C][/ROW]
[ROW][C]94[/C][C]105.9[/C][C]108.385989981965[/C][C]-2.48598998196502[/C][/ROW]
[ROW][C]95[/C][C]109.1[/C][C]106.812925960203[/C][C]2.28707403979683[/C][/ROW]
[ROW][C]96[/C][C]108.5[/C][C]108.260121609908[/C][C]0.239878390091846[/C][/ROW]
[ROW][C]97[/C][C]111.7[/C][C]108.411909858368[/C][C]3.28809014163183[/C][/ROW]
[ROW][C]98[/C][C]109.8[/C][C]110.492520134208[/C][C]-0.692520134207854[/C][/ROW]
[ROW][C]99[/C][C]109.1[/C][C]110.054313015365[/C][C]-0.954313015365415[/C][/ROW]
[ROW][C]100[/C][C]108.5[/C][C]109.450450779049[/C][C]-0.950450779048666[/C][/ROW]
[ROW][C]101[/C][C]108.5[/C][C]108.849032456439[/C][C]-0.349032456439346[/C][/ROW]
[ROW][C]102[/C][C]106.2[/C][C]108.628174607600[/C][C]-2.42817460760043[/C][/ROW]
[ROW][C]103[/C][C]117.1[/C][C]107.091694516577[/C][C]10.0083054834226[/C][/ROW]
[ROW][C]104[/C][C]109.8[/C][C]113.424666647997[/C][C]-3.6246666479975[/C][/ROW]
[ROW][C]105[/C][C]115.2[/C][C]111.131080295595[/C][C]4.0689197044053[/C][/ROW]
[ROW][C]106[/C][C]115.9[/C][C]113.705777394486[/C][C]2.19422260551411[/C][/ROW]
[ROW][C]107[/C][C]119.2[/C][C]115.094219287459[/C][C]4.10578071254136[/C][/ROW]
[ROW][C]108[/C][C]121[/C][C]117.692240987867[/C][C]3.30775901213312[/C][/ROW]
[ROW][C]109[/C][C]118.6[/C][C]119.785297167656[/C][C]-1.18529716765578[/C][/ROW]
[ROW][C]110[/C][C]117.6[/C][C]119.035274704548[/C][C]-1.43527470454765[/C][/ROW]
[ROW][C]111[/C][C]114.6[/C][C]118.127073539037[/C][C]-3.52707353903685[/C][/ROW]
[ROW][C]112[/C][C]110.6[/C][C]115.895241340750[/C][C]-5.29524134075037[/C][/ROW]
[ROW][C]113[/C][C]102.5[/C][C]112.544562657365[/C][C]-10.0445626573647[/C][/ROW]
[ROW][C]114[/C][C]101.6[/C][C]106.188648013596[/C][C]-4.58864801359627[/C][/ROW]
[ROW][C]115[/C][C]107.4[/C][C]103.285081566795[/C][C]4.11491843320485[/C][/ROW]
[ROW][C]116[/C][C]105.8[/C][C]105.888885357928[/C][C]-0.0888853579281772[/C][/ROW]
[ROW][C]117[/C][C]102.8[/C][C]105.832641221937[/C][C]-3.03264122193704[/C][/ROW]
[ROW][C]118[/C][C]104[/C][C]103.913671784510[/C][C]0.0863282154901697[/C][/ROW]
[ROW][C]119[/C][C]100.4[/C][C]103.968297833221[/C][C]-3.56829783322107[/C][/ROW]
[ROW][C]120[/C][C]100.6[/C][C]101.710380069637[/C][C]-1.11038006963700[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114048&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114048&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
39797.7-0.700000000000003
496.597.2570598340212-0.757059834021163
598.496.77801382182511.62198617817491
6106.397.80436071750538.49563928249466
7103.1103.180160537340-0.0801605373403191
8102.4103.129437220605-0.729437220605348
995102.667870015511-7.66787001551097
1098.197.81585913354730.284140866452660
11106.197.99565542290158.1043445770985
1299.1103.123855040230-4.02385504022959
13101.2100.5776735839520.622326416047642
1495.5100.971464106834-5.47146410683401
1599.897.50927665022222.29072334977782
1697.198.9587814798824-1.85878147988244
1797.597.7825972267146-0.282597226714557
1896.897.6037777088487-0.803777708848699
1997.797.09516994918080.604830050819203
20100.997.47788926789343.42211073210657
2194.399.6433039760038-5.34330397600377
2299.596.26221261885173.23778738114834
23100.898.31099273315172.48900726684830
249799.885966007295-2.88596600729500
2599.298.05980848975141.14019151024860
2610198.78128937089022.21871062910978
27102.3100.1852265913432.11477340865709
2897101.523395283683-4.52339528368319
2991.298.6611189154796-7.4611189154796
3097.693.93993427146483.66006572853517
3195.796.2559201590231-0.555920159023117
32100.595.90414963401074.59585036598928
3394.498.8122735253324-4.41227352533240
34102.996.02031185725426.8796881427458
35105.1100.3735835827264.72641641727374
3698.8103.364325971801-4.564325971801
37100.7100.4761498239000.223850176100484
3899.6100.617795872694-1.01779587269429
39107.799.97376348300327.72623651699683
40102.9104.862707033332-1.96270703333217
41101.6103.620761634601-2.02076163460079
42102.7102.3420809291240.357919070875980
43110.5102.5685619757827.9314380242177
44109.8107.5873512256362.21264877436367
4594.3108.987452676164-14.6874526761642
46102.599.69364878161222.80635121838785
47105101.4694283165663.53057168343412
48102.3103.703474041509-1.40347404150943
49107.7102.8153954345194.88460456548083
50100.3105.906234801626-5.60623480162639
5199.5102.358768267985-2.85876826798538
5295100.549820709248-5.54982070924757
5397.797.03805141480920.661948585190842
5496.397.4569137236576-1.15691372365757
5597.896.7248514996861.07514850031397
56106.497.40517500708748.9948249929126
5796.1103.096845400389-6.99684540038922
58106.298.66943988185127.53056011814884
59114.7103.43456495134611.2654350486539
60111.9110.5630130517381.33698694826174
61121111.4090205099889.59097949001232
62117.7117.4779205774240.222079422575689
63115.4117.618446143562-2.21844614356168
64114.3116.214676282070-1.91467628206981
65109.5115.003123381819-5.50312338181875
66108.1111.520902833040-3.42090283304047
67108.2109.356252449234-1.15625244923416
6899.1108.624608660981-9.524608660981
69101.2102.597706173583-1.39770617358333
7098.1101.713277309988-3.61327730998813
7195.599.4268978079686-3.92689780796856
7297.996.94206814104930.957931858950744
7398.297.54822027904930.651779720950657
7498.797.96064801873440.739351981265628
7595.698.4284890034466-2.82848900344663
7695.896.6387013053667-0.838701305366698
7794.496.1079948833584-1.70799488335842
7896.595.02722411603591.47277588396412
79103.395.95915496531127.34084503468884
80104.3100.6042337054403.69576629456031
81104.5102.9428098996281.55719010037242
82102.3103.928155673227-1.62815567322694
83103.8102.8979048958860.902095104113798
84103.1103.468725117522-0.36872511752172
85102.2103.235406310728-1.03540631072795
86106.3102.5802305348293.71976946517114
87102.1104.933995255266-2.83399525526586
8894103.140723357051-9.14072335705059
89102.697.35673261285265.24326738714738
90102.6100.6745236510451.92547634895529
91106.7101.8929105276084.80708947239162
92107.9104.9347005401462.96529945985412
93109.3106.8110580186102.48894198139021
94105.9108.385989981965-2.48598998196502
95109.1106.8129259602032.28707403979683
96108.5108.2601216099080.239878390091846
97111.7108.4119098583683.28809014163183
98109.8110.492520134208-0.692520134207854
99109.1110.054313015365-0.954313015365415
100108.5109.450450779049-0.950450779048666
101108.5108.849032456439-0.349032456439346
102106.2108.628174607600-2.42817460760043
103117.1107.09169451657710.0083054834226
104109.8113.424666647997-3.6246666479975
105115.2111.1310802955954.0689197044053
106115.9113.7057773944862.19422260551411
107119.2115.0942192874594.10578071254136
108121117.6922409878673.30775901213312
109118.6119.785297167656-1.18529716765578
110117.6119.035274704548-1.43527470454765
111114.6118.127073539037-3.52707353903685
112110.6115.895241340750-5.29524134075037
113102.5112.544562657365-10.0445626573647
114101.6106.188648013596-4.58864801359627
115107.4103.2850815667954.11491843320485
116105.8105.888885357928-0.0888853579281772
117102.8105.832641221937-3.03264122193704
118104103.9136717845100.0863282154901697
119100.4103.968297833221-3.56829783322107
120100.6101.710380069637-1.11038006963700






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
121101.00776302343092.3740976712982109.641428375563
122101.00776302343090.790813232796111.224712814065
123101.00776302343089.4219117972113112.593614249650
124101.00776302343088.1984759922245113.817050054636
125101.00776302343087.0821133129301114.933412733931
126101.00776302343086.0488323975035115.966693649357
127101.00776302343085.082453259315116.933072787546
128101.00776302343084.1714517678505117.844074279010
129101.00776302343083.3072753943704118.708250652490
130101.00776302343082.4833697022379119.532156344623
131101.00776302343081.694580128579120.320945918282
132101.00776302343080.936766059111121.078759987750

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
121 & 101.007763023430 & 92.3740976712982 & 109.641428375563 \tabularnewline
122 & 101.007763023430 & 90.790813232796 & 111.224712814065 \tabularnewline
123 & 101.007763023430 & 89.4219117972113 & 112.593614249650 \tabularnewline
124 & 101.007763023430 & 88.1984759922245 & 113.817050054636 \tabularnewline
125 & 101.007763023430 & 87.0821133129301 & 114.933412733931 \tabularnewline
126 & 101.007763023430 & 86.0488323975035 & 115.966693649357 \tabularnewline
127 & 101.007763023430 & 85.082453259315 & 116.933072787546 \tabularnewline
128 & 101.007763023430 & 84.1714517678505 & 117.844074279010 \tabularnewline
129 & 101.007763023430 & 83.3072753943704 & 118.708250652490 \tabularnewline
130 & 101.007763023430 & 82.4833697022379 & 119.532156344623 \tabularnewline
131 & 101.007763023430 & 81.694580128579 & 120.320945918282 \tabularnewline
132 & 101.007763023430 & 80.936766059111 & 121.078759987750 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114048&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]101.007763023430[/C][C]92.3740976712982[/C][C]109.641428375563[/C][/ROW]
[ROW][C]122[/C][C]101.007763023430[/C][C]90.790813232796[/C][C]111.224712814065[/C][/ROW]
[ROW][C]123[/C][C]101.007763023430[/C][C]89.4219117972113[/C][C]112.593614249650[/C][/ROW]
[ROW][C]124[/C][C]101.007763023430[/C][C]88.1984759922245[/C][C]113.817050054636[/C][/ROW]
[ROW][C]125[/C][C]101.007763023430[/C][C]87.0821133129301[/C][C]114.933412733931[/C][/ROW]
[ROW][C]126[/C][C]101.007763023430[/C][C]86.0488323975035[/C][C]115.966693649357[/C][/ROW]
[ROW][C]127[/C][C]101.007763023430[/C][C]85.082453259315[/C][C]116.933072787546[/C][/ROW]
[ROW][C]128[/C][C]101.007763023430[/C][C]84.1714517678505[/C][C]117.844074279010[/C][/ROW]
[ROW][C]129[/C][C]101.007763023430[/C][C]83.3072753943704[/C][C]118.708250652490[/C][/ROW]
[ROW][C]130[/C][C]101.007763023430[/C][C]82.4833697022379[/C][C]119.532156344623[/C][/ROW]
[ROW][C]131[/C][C]101.007763023430[/C][C]81.694580128579[/C][C]120.320945918282[/C][/ROW]
[ROW][C]132[/C][C]101.007763023430[/C][C]80.936766059111[/C][C]121.078759987750[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114048&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114048&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
121101.00776302343092.3740976712982109.641428375563
122101.00776302343090.790813232796111.224712814065
123101.00776302343089.4219117972113112.593614249650
124101.00776302343088.1984759922245113.817050054636
125101.00776302343087.0821133129301114.933412733931
126101.00776302343086.0488323975035115.966693649357
127101.00776302343085.082453259315116.933072787546
128101.00776302343084.1714517678505117.844074279010
129101.00776302343083.3072753943704118.708250652490
130101.00776302343082.4833697022379119.532156344623
131101.00776302343081.694580128579120.320945918282
132101.00776302343080.936766059111121.078759987750


Figures (Output of Computation)

Input Parameters & R Code

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