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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationFri, 21 Dec 2012 07:33:31 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356093235qty0w5tuzaa210x.htm/, Retrieved Thu, 28 Mar 2024 19:13:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203555, Retrieved Thu, 28 Mar 2024 19:13:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact66
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD  [Classical Decomposition] [Classical Decompo...] [2011-12-19 14:36:02] [920204e71d7e82687d0571379c55a021]
- RMPD      [Exponential Smoothing] [] [2012-12-21 12:33:31] [7f1e2e1b7f66b13ad70fccbed4479dd6] [Current]
-    D        [Exponential Smoothing] [] [2012-12-21 12:39:14] [63daa42bab46576bcb233b0e49169cb8]
-   PD        [Exponential Smoothing] [] [2012-12-21 12:58:26] [63daa42bab46576bcb233b0e49169cb8]
- RMPD        [Multiple Regression] [] [2012-12-21 15:00:33] [63daa42bab46576bcb233b0e49169cb8]
- RMPD        [(Partial) Autocorrelation Function] [] [2012-12-21 15:37:27] [63daa42bab46576bcb233b0e49169cb8]
- RMPD        [(Partial) Autocorrelation Function] [] [2012-12-21 15:45:14] [63daa42bab46576bcb233b0e49169cb8]
- RMPD        [(Partial) Autocorrelation Function] [] [2012-12-21 15:51:12] [63daa42bab46576bcb233b0e49169cb8]
- RMPD        [(Partial) Autocorrelation Function] [] [2012-12-21 15:56:04] [63daa42bab46576bcb233b0e49169cb8]
- RMPD        [Standard Deviation-Mean Plot] [] [2012-12-21 16:07:05] [63daa42bab46576bcb233b0e49169cb8]
- RMPD        [ARIMA Backward Selection] [] [2012-12-21 17:18:49] [63daa42bab46576bcb233b0e49169cb8]
- RMPD        [Central Tendency] [] [2012-12-21 17:45:37] [63daa42bab46576bcb233b0e49169cb8]
- RM            [Skewness and Kurtosis Test] [] [2012-12-21 17:51:01] [63daa42bab46576bcb233b0e49169cb8]
- RM D          [ARIMA Forecasting] [] [2012-12-21 18:05:13] [63daa42bab46576bcb233b0e49169cb8]
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Dataseries X:
127 
150 
130 
119 
129 
129 
131 
126 
131 
119 
127 
137 
124 
152 
134 
119 
131 
133 
131 
134 
136 
122 
131 
138 
131 
166 
134 
121 
140 
137 
134 
141 
139 
129 
136 
140 
133 
171 
136 
121 
143 
143 
143 
143 
147 
133 
140 
144 
143 
172 
145 
127 
153 
149 
145 
154 
149 
141 
149 
150 
145 
175 
152 
139 
153 
157 
161 
159 
155 
146 
162 
156 
141 
186 
159 
140 
159 
158 
157 
153 
161 
142 
152 
162 
146 
190 
154 
132 
155 
155 
150 
163 
160 
149 
171 
176 
156 
198 
157 
139 
160 
167 
162 
159 
169 
153 
161 
169 
156 
205 
167 
150 
174 
163 
163 
168 
168 
156 
170 
177 




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203555&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203555&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203555&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.120576301406814
betaFALSE
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.120576301406814 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203555&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]0.120576301406814[/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=203555&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203555&T=1

As an alternative you can also use a QR Code:  

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

Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.120576301406814
betaFALSE
gammaFALSE







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
215012723
3130129.7732549323570.226745067643265
4119129.800595013975-10.8005950139754
5129128.4982992141970.501700785802626
6129128.5587924393620.44120756063765
7131128.6119916151772.38800838482325
8126128.899928833947-2.8999288339472
9131128.5502661408072.44973385919312
10119128.845645988979-9.84564598897944
11127127.658494410667-0.658494410667473
12137127.5790955901329.42090440986787
13124128.715033399781-4.71503339978113
14152128.14651211142623.8534878885741
15134131.0226774566822.97732254331757
16119131.381671997051-12.3816719970508
17131129.8887357824141.1112642175859
18133130.0227279116562.97727208834365
19131130.3817163683510.618283631649433
20134130.4562667218753.54373327812476
21136130.8835569737245.11644302627624
22122131.500478750191-9.50047875019084
23131130.3549461608990.645053839101195
24138130.4327243670267.56727563297412
25131131.345158474576-0.345158474575811
26166131.30354054231234.6964594576878
27134135.487111295632-1.48711129563171
28121135.307800915824-14.3078009158241
29140133.5826192001296.41738079987095
30137134.3564032416972.6435967583034
31134134.675158361224-0.675158361223879
32141134.5937502631646.40624973683637
33139135.366192162323.63380783768028
34129135.80434327141-6.80434327141029
35136134.9839007262411.0160992737587
36140135.1064182185334.89358178146674
37133135.696468210374-2.69646821037429
38171135.37133804670635.6286619532937
39136139.667310329108-3.66731032910815
40121139.225119613513-18.2251196135133
41143137.0276020978195.97239790218094
42143137.7477317473945.25226825260614
43143138.381030827294.61896917271045
44143138.9379690464474.06203095355292
45147139.4277537150267.57224628497352
46133140.34078716541-7.34078716541009
47140139.455662199590.544337800409664
48144139.521296438284.47870356172035
49143140.0613219488492.93867805115059
50172140.41565687928331.5843431207175
51145144.2239801551420.776019844857615
52127144.317549757854-17.3175497578536
53153142.22946365862310.7705363413768
54149143.5281350948345.47186490516594
55145144.1879123268970.812087673103292
56154144.2858308549389.71416914506244
57149145.4571294416893.54287055831063
58141145.884315669974-4.88431566997357
59149145.2953829515853.70461704841517
60150145.7420719734114.25792802658864
61145146.255477186514-1.25547718651384
62175146.10409639086328.8959036091366
63152149.5882575738612.41174242613914
64139149.879056555551-10.8790565555506
65153148.5673001532874.43269984671323
66157149.101778706057.89822129395
67161150.05411701736710.945882982633
68159151.3739311030457.6260688969553
69155152.2934542849132.70654571508689
70146152.619799556827-6.61979955682673
71162151.8216086102110.1783913897899
72156153.0488813982622.95111860173807
73141153.404716364272-12.4047163642724
74186151.90900154506834.0909984549322
75159156.0195680500292.98043194997106
76140156.378937511151-16.3789375111512
77159154.4040258050834.59597419491678
78158154.9581913748673.04180862513255
79157155.3249614084731.67503859152671
80153155.526931366553-2.52693136655324
81161155.2222433284655.7777566715346
82142155.918903858348-13.9189038583476
83152154.240613911471-2.240613911471
84162153.9704489731458.02955102685482
85146154.938622537921-8.93862253792062
86190153.86083649262736.1391635073734
87154158.218363164282-4.21836316428175
88132157.709728535942-25.7097285359419
89155154.6097445589050.390255441095206
90155154.6568001165960.343199883404054
91150154.69818188918-4.69818188918006
92163154.1316924936468.86830750635374
93160155.2010002125014.79899978749933
94149155.779645857329-6.77964585732943
95171154.96218123500516.0378187649954
96176156.89596210432119.1040378956795
97156159.199456335717-3.1994563357172
98198158.81367772424439.1863222757562
99157163.53861952999-6.53861952998997
100139162.750216970757-23.7502169707574
101160159.8865036508140.11349634918588
102167159.9001886208227.09981137917788
103162160.7562576176091.24374238239059
104159160.906223473981-1.90622347398096
105169160.6763780978338.32362190216651
106153161.680009641105-8.68000964110547
107161160.6334061824050.36659381759452
108169160.677608709058.32239129095038
109156161.681091869773-5.6810918697727
110205160.99608682416344.0039131758368
111167166.3019159223320.698084077667829
112150166.386088318488-16.3860883184883
113174164.410314394529.58968560548038
114163165.566603216483-2.5666032164826
115163165.25713169346-2.25713169346031
116168164.9849751020753.01502489792523
117168165.3485156529162.65148434708394
118156165.668221828726-9.66822182872551
119170164.5024633994375.49753660056285
120177165.16533602958211.8346639704184

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 150 & 127 & 23 \tabularnewline
3 & 130 & 129.773254932357 & 0.226745067643265 \tabularnewline
4 & 119 & 129.800595013975 & -10.8005950139754 \tabularnewline
5 & 129 & 128.498299214197 & 0.501700785802626 \tabularnewline
6 & 129 & 128.558792439362 & 0.44120756063765 \tabularnewline
7 & 131 & 128.611991615177 & 2.38800838482325 \tabularnewline
8 & 126 & 128.899928833947 & -2.8999288339472 \tabularnewline
9 & 131 & 128.550266140807 & 2.44973385919312 \tabularnewline
10 & 119 & 128.845645988979 & -9.84564598897944 \tabularnewline
11 & 127 & 127.658494410667 & -0.658494410667473 \tabularnewline
12 & 137 & 127.579095590132 & 9.42090440986787 \tabularnewline
13 & 124 & 128.715033399781 & -4.71503339978113 \tabularnewline
14 & 152 & 128.146512111426 & 23.8534878885741 \tabularnewline
15 & 134 & 131.022677456682 & 2.97732254331757 \tabularnewline
16 & 119 & 131.381671997051 & -12.3816719970508 \tabularnewline
17 & 131 & 129.888735782414 & 1.1112642175859 \tabularnewline
18 & 133 & 130.022727911656 & 2.97727208834365 \tabularnewline
19 & 131 & 130.381716368351 & 0.618283631649433 \tabularnewline
20 & 134 & 130.456266721875 & 3.54373327812476 \tabularnewline
21 & 136 & 130.883556973724 & 5.11644302627624 \tabularnewline
22 & 122 & 131.500478750191 & -9.50047875019084 \tabularnewline
23 & 131 & 130.354946160899 & 0.645053839101195 \tabularnewline
24 & 138 & 130.432724367026 & 7.56727563297412 \tabularnewline
25 & 131 & 131.345158474576 & -0.345158474575811 \tabularnewline
26 & 166 & 131.303540542312 & 34.6964594576878 \tabularnewline
27 & 134 & 135.487111295632 & -1.48711129563171 \tabularnewline
28 & 121 & 135.307800915824 & -14.3078009158241 \tabularnewline
29 & 140 & 133.582619200129 & 6.41738079987095 \tabularnewline
30 & 137 & 134.356403241697 & 2.6435967583034 \tabularnewline
31 & 134 & 134.675158361224 & -0.675158361223879 \tabularnewline
32 & 141 & 134.593750263164 & 6.40624973683637 \tabularnewline
33 & 139 & 135.36619216232 & 3.63380783768028 \tabularnewline
34 & 129 & 135.80434327141 & -6.80434327141029 \tabularnewline
35 & 136 & 134.983900726241 & 1.0160992737587 \tabularnewline
36 & 140 & 135.106418218533 & 4.89358178146674 \tabularnewline
37 & 133 & 135.696468210374 & -2.69646821037429 \tabularnewline
38 & 171 & 135.371338046706 & 35.6286619532937 \tabularnewline
39 & 136 & 139.667310329108 & -3.66731032910815 \tabularnewline
40 & 121 & 139.225119613513 & -18.2251196135133 \tabularnewline
41 & 143 & 137.027602097819 & 5.97239790218094 \tabularnewline
42 & 143 & 137.747731747394 & 5.25226825260614 \tabularnewline
43 & 143 & 138.38103082729 & 4.61896917271045 \tabularnewline
44 & 143 & 138.937969046447 & 4.06203095355292 \tabularnewline
45 & 147 & 139.427753715026 & 7.57224628497352 \tabularnewline
46 & 133 & 140.34078716541 & -7.34078716541009 \tabularnewline
47 & 140 & 139.45566219959 & 0.544337800409664 \tabularnewline
48 & 144 & 139.52129643828 & 4.47870356172035 \tabularnewline
49 & 143 & 140.061321948849 & 2.93867805115059 \tabularnewline
50 & 172 & 140.415656879283 & 31.5843431207175 \tabularnewline
51 & 145 & 144.223980155142 & 0.776019844857615 \tabularnewline
52 & 127 & 144.317549757854 & -17.3175497578536 \tabularnewline
53 & 153 & 142.229463658623 & 10.7705363413768 \tabularnewline
54 & 149 & 143.528135094834 & 5.47186490516594 \tabularnewline
55 & 145 & 144.187912326897 & 0.812087673103292 \tabularnewline
56 & 154 & 144.285830854938 & 9.71416914506244 \tabularnewline
57 & 149 & 145.457129441689 & 3.54287055831063 \tabularnewline
58 & 141 & 145.884315669974 & -4.88431566997357 \tabularnewline
59 & 149 & 145.295382951585 & 3.70461704841517 \tabularnewline
60 & 150 & 145.742071973411 & 4.25792802658864 \tabularnewline
61 & 145 & 146.255477186514 & -1.25547718651384 \tabularnewline
62 & 175 & 146.104096390863 & 28.8959036091366 \tabularnewline
63 & 152 & 149.588257573861 & 2.41174242613914 \tabularnewline
64 & 139 & 149.879056555551 & -10.8790565555506 \tabularnewline
65 & 153 & 148.567300153287 & 4.43269984671323 \tabularnewline
66 & 157 & 149.10177870605 & 7.89822129395 \tabularnewline
67 & 161 & 150.054117017367 & 10.945882982633 \tabularnewline
68 & 159 & 151.373931103045 & 7.6260688969553 \tabularnewline
69 & 155 & 152.293454284913 & 2.70654571508689 \tabularnewline
70 & 146 & 152.619799556827 & -6.61979955682673 \tabularnewline
71 & 162 & 151.82160861021 & 10.1783913897899 \tabularnewline
72 & 156 & 153.048881398262 & 2.95111860173807 \tabularnewline
73 & 141 & 153.404716364272 & -12.4047163642724 \tabularnewline
74 & 186 & 151.909001545068 & 34.0909984549322 \tabularnewline
75 & 159 & 156.019568050029 & 2.98043194997106 \tabularnewline
76 & 140 & 156.378937511151 & -16.3789375111512 \tabularnewline
77 & 159 & 154.404025805083 & 4.59597419491678 \tabularnewline
78 & 158 & 154.958191374867 & 3.04180862513255 \tabularnewline
79 & 157 & 155.324961408473 & 1.67503859152671 \tabularnewline
80 & 153 & 155.526931366553 & -2.52693136655324 \tabularnewline
81 & 161 & 155.222243328465 & 5.7777566715346 \tabularnewline
82 & 142 & 155.918903858348 & -13.9189038583476 \tabularnewline
83 & 152 & 154.240613911471 & -2.240613911471 \tabularnewline
84 & 162 & 153.970448973145 & 8.02955102685482 \tabularnewline
85 & 146 & 154.938622537921 & -8.93862253792062 \tabularnewline
86 & 190 & 153.860836492627 & 36.1391635073734 \tabularnewline
87 & 154 & 158.218363164282 & -4.21836316428175 \tabularnewline
88 & 132 & 157.709728535942 & -25.7097285359419 \tabularnewline
89 & 155 & 154.609744558905 & 0.390255441095206 \tabularnewline
90 & 155 & 154.656800116596 & 0.343199883404054 \tabularnewline
91 & 150 & 154.69818188918 & -4.69818188918006 \tabularnewline
92 & 163 & 154.131692493646 & 8.86830750635374 \tabularnewline
93 & 160 & 155.201000212501 & 4.79899978749933 \tabularnewline
94 & 149 & 155.779645857329 & -6.77964585732943 \tabularnewline
95 & 171 & 154.962181235005 & 16.0378187649954 \tabularnewline
96 & 176 & 156.895962104321 & 19.1040378956795 \tabularnewline
97 & 156 & 159.199456335717 & -3.1994563357172 \tabularnewline
98 & 198 & 158.813677724244 & 39.1863222757562 \tabularnewline
99 & 157 & 163.53861952999 & -6.53861952998997 \tabularnewline
100 & 139 & 162.750216970757 & -23.7502169707574 \tabularnewline
101 & 160 & 159.886503650814 & 0.11349634918588 \tabularnewline
102 & 167 & 159.900188620822 & 7.09981137917788 \tabularnewline
103 & 162 & 160.756257617609 & 1.24374238239059 \tabularnewline
104 & 159 & 160.906223473981 & -1.90622347398096 \tabularnewline
105 & 169 & 160.676378097833 & 8.32362190216651 \tabularnewline
106 & 153 & 161.680009641105 & -8.68000964110547 \tabularnewline
107 & 161 & 160.633406182405 & 0.36659381759452 \tabularnewline
108 & 169 & 160.67760870905 & 8.32239129095038 \tabularnewline
109 & 156 & 161.681091869773 & -5.6810918697727 \tabularnewline
110 & 205 & 160.996086824163 & 44.0039131758368 \tabularnewline
111 & 167 & 166.301915922332 & 0.698084077667829 \tabularnewline
112 & 150 & 166.386088318488 & -16.3860883184883 \tabularnewline
113 & 174 & 164.41031439452 & 9.58968560548038 \tabularnewline
114 & 163 & 165.566603216483 & -2.5666032164826 \tabularnewline
115 & 163 & 165.25713169346 & -2.25713169346031 \tabularnewline
116 & 168 & 164.984975102075 & 3.01502489792523 \tabularnewline
117 & 168 & 165.348515652916 & 2.65148434708394 \tabularnewline
118 & 156 & 165.668221828726 & -9.66822182872551 \tabularnewline
119 & 170 & 164.502463399437 & 5.49753660056285 \tabularnewline
120 & 177 & 165.165336029582 & 11.8346639704184 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203555&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]150[/C][C]127[/C][C]23[/C][/ROW]
[ROW][C]3[/C][C]130[/C][C]129.773254932357[/C][C]0.226745067643265[/C][/ROW]
[ROW][C]4[/C][C]119[/C][C]129.800595013975[/C][C]-10.8005950139754[/C][/ROW]
[ROW][C]5[/C][C]129[/C][C]128.498299214197[/C][C]0.501700785802626[/C][/ROW]
[ROW][C]6[/C][C]129[/C][C]128.558792439362[/C][C]0.44120756063765[/C][/ROW]
[ROW][C]7[/C][C]131[/C][C]128.611991615177[/C][C]2.38800838482325[/C][/ROW]
[ROW][C]8[/C][C]126[/C][C]128.899928833947[/C][C]-2.8999288339472[/C][/ROW]
[ROW][C]9[/C][C]131[/C][C]128.550266140807[/C][C]2.44973385919312[/C][/ROW]
[ROW][C]10[/C][C]119[/C][C]128.845645988979[/C][C]-9.84564598897944[/C][/ROW]
[ROW][C]11[/C][C]127[/C][C]127.658494410667[/C][C]-0.658494410667473[/C][/ROW]
[ROW][C]12[/C][C]137[/C][C]127.579095590132[/C][C]9.42090440986787[/C][/ROW]
[ROW][C]13[/C][C]124[/C][C]128.715033399781[/C][C]-4.71503339978113[/C][/ROW]
[ROW][C]14[/C][C]152[/C][C]128.146512111426[/C][C]23.8534878885741[/C][/ROW]
[ROW][C]15[/C][C]134[/C][C]131.022677456682[/C][C]2.97732254331757[/C][/ROW]
[ROW][C]16[/C][C]119[/C][C]131.381671997051[/C][C]-12.3816719970508[/C][/ROW]
[ROW][C]17[/C][C]131[/C][C]129.888735782414[/C][C]1.1112642175859[/C][/ROW]
[ROW][C]18[/C][C]133[/C][C]130.022727911656[/C][C]2.97727208834365[/C][/ROW]
[ROW][C]19[/C][C]131[/C][C]130.381716368351[/C][C]0.618283631649433[/C][/ROW]
[ROW][C]20[/C][C]134[/C][C]130.456266721875[/C][C]3.54373327812476[/C][/ROW]
[ROW][C]21[/C][C]136[/C][C]130.883556973724[/C][C]5.11644302627624[/C][/ROW]
[ROW][C]22[/C][C]122[/C][C]131.500478750191[/C][C]-9.50047875019084[/C][/ROW]
[ROW][C]23[/C][C]131[/C][C]130.354946160899[/C][C]0.645053839101195[/C][/ROW]
[ROW][C]24[/C][C]138[/C][C]130.432724367026[/C][C]7.56727563297412[/C][/ROW]
[ROW][C]25[/C][C]131[/C][C]131.345158474576[/C][C]-0.345158474575811[/C][/ROW]
[ROW][C]26[/C][C]166[/C][C]131.303540542312[/C][C]34.6964594576878[/C][/ROW]
[ROW][C]27[/C][C]134[/C][C]135.487111295632[/C][C]-1.48711129563171[/C][/ROW]
[ROW][C]28[/C][C]121[/C][C]135.307800915824[/C][C]-14.3078009158241[/C][/ROW]
[ROW][C]29[/C][C]140[/C][C]133.582619200129[/C][C]6.41738079987095[/C][/ROW]
[ROW][C]30[/C][C]137[/C][C]134.356403241697[/C][C]2.6435967583034[/C][/ROW]
[ROW][C]31[/C][C]134[/C][C]134.675158361224[/C][C]-0.675158361223879[/C][/ROW]
[ROW][C]32[/C][C]141[/C][C]134.593750263164[/C][C]6.40624973683637[/C][/ROW]
[ROW][C]33[/C][C]139[/C][C]135.36619216232[/C][C]3.63380783768028[/C][/ROW]
[ROW][C]34[/C][C]129[/C][C]135.80434327141[/C][C]-6.80434327141029[/C][/ROW]
[ROW][C]35[/C][C]136[/C][C]134.983900726241[/C][C]1.0160992737587[/C][/ROW]
[ROW][C]36[/C][C]140[/C][C]135.106418218533[/C][C]4.89358178146674[/C][/ROW]
[ROW][C]37[/C][C]133[/C][C]135.696468210374[/C][C]-2.69646821037429[/C][/ROW]
[ROW][C]38[/C][C]171[/C][C]135.371338046706[/C][C]35.6286619532937[/C][/ROW]
[ROW][C]39[/C][C]136[/C][C]139.667310329108[/C][C]-3.66731032910815[/C][/ROW]
[ROW][C]40[/C][C]121[/C][C]139.225119613513[/C][C]-18.2251196135133[/C][/ROW]
[ROW][C]41[/C][C]143[/C][C]137.027602097819[/C][C]5.97239790218094[/C][/ROW]
[ROW][C]42[/C][C]143[/C][C]137.747731747394[/C][C]5.25226825260614[/C][/ROW]
[ROW][C]43[/C][C]143[/C][C]138.38103082729[/C][C]4.61896917271045[/C][/ROW]
[ROW][C]44[/C][C]143[/C][C]138.937969046447[/C][C]4.06203095355292[/C][/ROW]
[ROW][C]45[/C][C]147[/C][C]139.427753715026[/C][C]7.57224628497352[/C][/ROW]
[ROW][C]46[/C][C]133[/C][C]140.34078716541[/C][C]-7.34078716541009[/C][/ROW]
[ROW][C]47[/C][C]140[/C][C]139.45566219959[/C][C]0.544337800409664[/C][/ROW]
[ROW][C]48[/C][C]144[/C][C]139.52129643828[/C][C]4.47870356172035[/C][/ROW]
[ROW][C]49[/C][C]143[/C][C]140.061321948849[/C][C]2.93867805115059[/C][/ROW]
[ROW][C]50[/C][C]172[/C][C]140.415656879283[/C][C]31.5843431207175[/C][/ROW]
[ROW][C]51[/C][C]145[/C][C]144.223980155142[/C][C]0.776019844857615[/C][/ROW]
[ROW][C]52[/C][C]127[/C][C]144.317549757854[/C][C]-17.3175497578536[/C][/ROW]
[ROW][C]53[/C][C]153[/C][C]142.229463658623[/C][C]10.7705363413768[/C][/ROW]
[ROW][C]54[/C][C]149[/C][C]143.528135094834[/C][C]5.47186490516594[/C][/ROW]
[ROW][C]55[/C][C]145[/C][C]144.187912326897[/C][C]0.812087673103292[/C][/ROW]
[ROW][C]56[/C][C]154[/C][C]144.285830854938[/C][C]9.71416914506244[/C][/ROW]
[ROW][C]57[/C][C]149[/C][C]145.457129441689[/C][C]3.54287055831063[/C][/ROW]
[ROW][C]58[/C][C]141[/C][C]145.884315669974[/C][C]-4.88431566997357[/C][/ROW]
[ROW][C]59[/C][C]149[/C][C]145.295382951585[/C][C]3.70461704841517[/C][/ROW]
[ROW][C]60[/C][C]150[/C][C]145.742071973411[/C][C]4.25792802658864[/C][/ROW]
[ROW][C]61[/C][C]145[/C][C]146.255477186514[/C][C]-1.25547718651384[/C][/ROW]
[ROW][C]62[/C][C]175[/C][C]146.104096390863[/C][C]28.8959036091366[/C][/ROW]
[ROW][C]63[/C][C]152[/C][C]149.588257573861[/C][C]2.41174242613914[/C][/ROW]
[ROW][C]64[/C][C]139[/C][C]149.879056555551[/C][C]-10.8790565555506[/C][/ROW]
[ROW][C]65[/C][C]153[/C][C]148.567300153287[/C][C]4.43269984671323[/C][/ROW]
[ROW][C]66[/C][C]157[/C][C]149.10177870605[/C][C]7.89822129395[/C][/ROW]
[ROW][C]67[/C][C]161[/C][C]150.054117017367[/C][C]10.945882982633[/C][/ROW]
[ROW][C]68[/C][C]159[/C][C]151.373931103045[/C][C]7.6260688969553[/C][/ROW]
[ROW][C]69[/C][C]155[/C][C]152.293454284913[/C][C]2.70654571508689[/C][/ROW]
[ROW][C]70[/C][C]146[/C][C]152.619799556827[/C][C]-6.61979955682673[/C][/ROW]
[ROW][C]71[/C][C]162[/C][C]151.82160861021[/C][C]10.1783913897899[/C][/ROW]
[ROW][C]72[/C][C]156[/C][C]153.048881398262[/C][C]2.95111860173807[/C][/ROW]
[ROW][C]73[/C][C]141[/C][C]153.404716364272[/C][C]-12.4047163642724[/C][/ROW]
[ROW][C]74[/C][C]186[/C][C]151.909001545068[/C][C]34.0909984549322[/C][/ROW]
[ROW][C]75[/C][C]159[/C][C]156.019568050029[/C][C]2.98043194997106[/C][/ROW]
[ROW][C]76[/C][C]140[/C][C]156.378937511151[/C][C]-16.3789375111512[/C][/ROW]
[ROW][C]77[/C][C]159[/C][C]154.404025805083[/C][C]4.59597419491678[/C][/ROW]
[ROW][C]78[/C][C]158[/C][C]154.958191374867[/C][C]3.04180862513255[/C][/ROW]
[ROW][C]79[/C][C]157[/C][C]155.324961408473[/C][C]1.67503859152671[/C][/ROW]
[ROW][C]80[/C][C]153[/C][C]155.526931366553[/C][C]-2.52693136655324[/C][/ROW]
[ROW][C]81[/C][C]161[/C][C]155.222243328465[/C][C]5.7777566715346[/C][/ROW]
[ROW][C]82[/C][C]142[/C][C]155.918903858348[/C][C]-13.9189038583476[/C][/ROW]
[ROW][C]83[/C][C]152[/C][C]154.240613911471[/C][C]-2.240613911471[/C][/ROW]
[ROW][C]84[/C][C]162[/C][C]153.970448973145[/C][C]8.02955102685482[/C][/ROW]
[ROW][C]85[/C][C]146[/C][C]154.938622537921[/C][C]-8.93862253792062[/C][/ROW]
[ROW][C]86[/C][C]190[/C][C]153.860836492627[/C][C]36.1391635073734[/C][/ROW]
[ROW][C]87[/C][C]154[/C][C]158.218363164282[/C][C]-4.21836316428175[/C][/ROW]
[ROW][C]88[/C][C]132[/C][C]157.709728535942[/C][C]-25.7097285359419[/C][/ROW]
[ROW][C]89[/C][C]155[/C][C]154.609744558905[/C][C]0.390255441095206[/C][/ROW]
[ROW][C]90[/C][C]155[/C][C]154.656800116596[/C][C]0.343199883404054[/C][/ROW]
[ROW][C]91[/C][C]150[/C][C]154.69818188918[/C][C]-4.69818188918006[/C][/ROW]
[ROW][C]92[/C][C]163[/C][C]154.131692493646[/C][C]8.86830750635374[/C][/ROW]
[ROW][C]93[/C][C]160[/C][C]155.201000212501[/C][C]4.79899978749933[/C][/ROW]
[ROW][C]94[/C][C]149[/C][C]155.779645857329[/C][C]-6.77964585732943[/C][/ROW]
[ROW][C]95[/C][C]171[/C][C]154.962181235005[/C][C]16.0378187649954[/C][/ROW]
[ROW][C]96[/C][C]176[/C][C]156.895962104321[/C][C]19.1040378956795[/C][/ROW]
[ROW][C]97[/C][C]156[/C][C]159.199456335717[/C][C]-3.1994563357172[/C][/ROW]
[ROW][C]98[/C][C]198[/C][C]158.813677724244[/C][C]39.1863222757562[/C][/ROW]
[ROW][C]99[/C][C]157[/C][C]163.53861952999[/C][C]-6.53861952998997[/C][/ROW]
[ROW][C]100[/C][C]139[/C][C]162.750216970757[/C][C]-23.7502169707574[/C][/ROW]
[ROW][C]101[/C][C]160[/C][C]159.886503650814[/C][C]0.11349634918588[/C][/ROW]
[ROW][C]102[/C][C]167[/C][C]159.900188620822[/C][C]7.09981137917788[/C][/ROW]
[ROW][C]103[/C][C]162[/C][C]160.756257617609[/C][C]1.24374238239059[/C][/ROW]
[ROW][C]104[/C][C]159[/C][C]160.906223473981[/C][C]-1.90622347398096[/C][/ROW]
[ROW][C]105[/C][C]169[/C][C]160.676378097833[/C][C]8.32362190216651[/C][/ROW]
[ROW][C]106[/C][C]153[/C][C]161.680009641105[/C][C]-8.68000964110547[/C][/ROW]
[ROW][C]107[/C][C]161[/C][C]160.633406182405[/C][C]0.36659381759452[/C][/ROW]
[ROW][C]108[/C][C]169[/C][C]160.67760870905[/C][C]8.32239129095038[/C][/ROW]
[ROW][C]109[/C][C]156[/C][C]161.681091869773[/C][C]-5.6810918697727[/C][/ROW]
[ROW][C]110[/C][C]205[/C][C]160.996086824163[/C][C]44.0039131758368[/C][/ROW]
[ROW][C]111[/C][C]167[/C][C]166.301915922332[/C][C]0.698084077667829[/C][/ROW]
[ROW][C]112[/C][C]150[/C][C]166.386088318488[/C][C]-16.3860883184883[/C][/ROW]
[ROW][C]113[/C][C]174[/C][C]164.41031439452[/C][C]9.58968560548038[/C][/ROW]
[ROW][C]114[/C][C]163[/C][C]165.566603216483[/C][C]-2.5666032164826[/C][/ROW]
[ROW][C]115[/C][C]163[/C][C]165.25713169346[/C][C]-2.25713169346031[/C][/ROW]
[ROW][C]116[/C][C]168[/C][C]164.984975102075[/C][C]3.01502489792523[/C][/ROW]
[ROW][C]117[/C][C]168[/C][C]165.348515652916[/C][C]2.65148434708394[/C][/ROW]
[ROW][C]118[/C][C]156[/C][C]165.668221828726[/C][C]-9.66822182872551[/C][/ROW]
[ROW][C]119[/C][C]170[/C][C]164.502463399437[/C][C]5.49753660056285[/C][/ROW]
[ROW][C]120[/C][C]177[/C][C]165.165336029582[/C][C]11.8346639704184[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203555&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203555&T=2

As an alternative you can also use a QR Code:  

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

Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
215012723
3130129.7732549323570.226745067643265
4119129.800595013975-10.8005950139754
5129128.4982992141970.501700785802626
6129128.5587924393620.44120756063765
7131128.6119916151772.38800838482325
8126128.899928833947-2.8999288339472
9131128.5502661408072.44973385919312
10119128.845645988979-9.84564598897944
11127127.658494410667-0.658494410667473
12137127.5790955901329.42090440986787
13124128.715033399781-4.71503339978113
14152128.14651211142623.8534878885741
15134131.0226774566822.97732254331757
16119131.381671997051-12.3816719970508
17131129.8887357824141.1112642175859
18133130.0227279116562.97727208834365
19131130.3817163683510.618283631649433
20134130.4562667218753.54373327812476
21136130.8835569737245.11644302627624
22122131.500478750191-9.50047875019084
23131130.3549461608990.645053839101195
24138130.4327243670267.56727563297412
25131131.345158474576-0.345158474575811
26166131.30354054231234.6964594576878
27134135.487111295632-1.48711129563171
28121135.307800915824-14.3078009158241
29140133.5826192001296.41738079987095
30137134.3564032416972.6435967583034
31134134.675158361224-0.675158361223879
32141134.5937502631646.40624973683637
33139135.366192162323.63380783768028
34129135.80434327141-6.80434327141029
35136134.9839007262411.0160992737587
36140135.1064182185334.89358178146674
37133135.696468210374-2.69646821037429
38171135.37133804670635.6286619532937
39136139.667310329108-3.66731032910815
40121139.225119613513-18.2251196135133
41143137.0276020978195.97239790218094
42143137.7477317473945.25226825260614
43143138.381030827294.61896917271045
44143138.9379690464474.06203095355292
45147139.4277537150267.57224628497352
46133140.34078716541-7.34078716541009
47140139.455662199590.544337800409664
48144139.521296438284.47870356172035
49143140.0613219488492.93867805115059
50172140.41565687928331.5843431207175
51145144.2239801551420.776019844857615
52127144.317549757854-17.3175497578536
53153142.22946365862310.7705363413768
54149143.5281350948345.47186490516594
55145144.1879123268970.812087673103292
56154144.2858308549389.71416914506244
57149145.4571294416893.54287055831063
58141145.884315669974-4.88431566997357
59149145.2953829515853.70461704841517
60150145.7420719734114.25792802658864
61145146.255477186514-1.25547718651384
62175146.10409639086328.8959036091366
63152149.5882575738612.41174242613914
64139149.879056555551-10.8790565555506
65153148.5673001532874.43269984671323
66157149.101778706057.89822129395
67161150.05411701736710.945882982633
68159151.3739311030457.6260688969553
69155152.2934542849132.70654571508689
70146152.619799556827-6.61979955682673
71162151.8216086102110.1783913897899
72156153.0488813982622.95111860173807
73141153.404716364272-12.4047163642724
74186151.90900154506834.0909984549322
75159156.0195680500292.98043194997106
76140156.378937511151-16.3789375111512
77159154.4040258050834.59597419491678
78158154.9581913748673.04180862513255
79157155.3249614084731.67503859152671
80153155.526931366553-2.52693136655324
81161155.2222433284655.7777566715346
82142155.918903858348-13.9189038583476
83152154.240613911471-2.240613911471
84162153.9704489731458.02955102685482
85146154.938622537921-8.93862253792062
86190153.86083649262736.1391635073734
87154158.218363164282-4.21836316428175
88132157.709728535942-25.7097285359419
89155154.6097445589050.390255441095206
90155154.6568001165960.343199883404054
91150154.69818188918-4.69818188918006
92163154.1316924936468.86830750635374
93160155.2010002125014.79899978749933
94149155.779645857329-6.77964585732943
95171154.96218123500516.0378187649954
96176156.89596210432119.1040378956795
97156159.199456335717-3.1994563357172
98198158.81367772424439.1863222757562
99157163.53861952999-6.53861952998997
100139162.750216970757-23.7502169707574
101160159.8865036508140.11349634918588
102167159.9001886208227.09981137917788
103162160.7562576176091.24374238239059
104159160.906223473981-1.90622347398096
105169160.6763780978338.32362190216651
106153161.680009641105-8.68000964110547
107161160.6334061824050.36659381759452
108169160.677608709058.32239129095038
109156161.681091869773-5.6810918697727
110205160.99608682416344.0039131758368
111167166.3019159223320.698084077667829
112150166.386088318488-16.3860883184883
113174164.410314394529.58968560548038
114163165.566603216483-2.5666032164826
115163165.25713169346-2.25713169346031
116168164.9849751020753.01502489792523
117168165.3485156529162.65148434708394
118156165.668221828726-9.66822182872551
119170164.5024633994375.49753660056285
120177165.16533602958211.8346639704184







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
121166.592316039527142.95217742294190.232454656115
122166.592316039527142.780949746587190.403682332467
123166.592316039527142.610944609569190.573687469485
124166.592316039527142.442136193681190.742495885373
125166.592316039527142.274499576864190.91013250219
126166.592316039527142.108010690246191.076621388809
127166.592316039527141.942646277806191.241985801249
128166.592316039527141.778383858452191.406248220603
129166.592316039527141.615201690346191.569430388709
130166.592316039527141.45307873731191.731553341744
131166.592316039527141.291994637177191.892637441878
132166.592316039527141.13192967193192.052702407124

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
121 & 166.592316039527 & 142.95217742294 & 190.232454656115 \tabularnewline
122 & 166.592316039527 & 142.780949746587 & 190.403682332467 \tabularnewline
123 & 166.592316039527 & 142.610944609569 & 190.573687469485 \tabularnewline
124 & 166.592316039527 & 142.442136193681 & 190.742495885373 \tabularnewline
125 & 166.592316039527 & 142.274499576864 & 190.91013250219 \tabularnewline
126 & 166.592316039527 & 142.108010690246 & 191.076621388809 \tabularnewline
127 & 166.592316039527 & 141.942646277806 & 191.241985801249 \tabularnewline
128 & 166.592316039527 & 141.778383858452 & 191.406248220603 \tabularnewline
129 & 166.592316039527 & 141.615201690346 & 191.569430388709 \tabularnewline
130 & 166.592316039527 & 141.45307873731 & 191.731553341744 \tabularnewline
131 & 166.592316039527 & 141.291994637177 & 191.892637441878 \tabularnewline
132 & 166.592316039527 & 141.13192967193 & 192.052702407124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203555&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]166.592316039527[/C][C]142.95217742294[/C][C]190.232454656115[/C][/ROW]
[ROW][C]122[/C][C]166.592316039527[/C][C]142.780949746587[/C][C]190.403682332467[/C][/ROW]
[ROW][C]123[/C][C]166.592316039527[/C][C]142.610944609569[/C][C]190.573687469485[/C][/ROW]
[ROW][C]124[/C][C]166.592316039527[/C][C]142.442136193681[/C][C]190.742495885373[/C][/ROW]
[ROW][C]125[/C][C]166.592316039527[/C][C]142.274499576864[/C][C]190.91013250219[/C][/ROW]
[ROW][C]126[/C][C]166.592316039527[/C][C]142.108010690246[/C][C]191.076621388809[/C][/ROW]
[ROW][C]127[/C][C]166.592316039527[/C][C]141.942646277806[/C][C]191.241985801249[/C][/ROW]
[ROW][C]128[/C][C]166.592316039527[/C][C]141.778383858452[/C][C]191.406248220603[/C][/ROW]
[ROW][C]129[/C][C]166.592316039527[/C][C]141.615201690346[/C][C]191.569430388709[/C][/ROW]
[ROW][C]130[/C][C]166.592316039527[/C][C]141.45307873731[/C][C]191.731553341744[/C][/ROW]
[ROW][C]131[/C][C]166.592316039527[/C][C]141.291994637177[/C][C]191.892637441878[/C][/ROW]
[ROW][C]132[/C][C]166.592316039527[/C][C]141.13192967193[/C][C]192.052702407124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203555&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203555&T=3

As an alternative you can also use a QR Code:  

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

Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
121166.592316039527142.95217742294190.232454656115
122166.592316039527142.780949746587190.403682332467
123166.592316039527142.610944609569190.573687469485
124166.592316039527142.442136193681190.742495885373
125166.592316039527142.274499576864190.91013250219
126166.592316039527142.108010690246191.076621388809
127166.592316039527141.942646277806191.241985801249
128166.592316039527141.778383858452191.406248220603
129166.592316039527141.615201690346191.569430388709
130166.592316039527141.45307873731191.731553341744
131166.592316039527141.291994637177191.892637441878
132166.592316039527141.13192967193192.052702407124



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')