Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationSun, 15 Mar 2009 04:13:52 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Mar/15/t1237112069opdzrat1i19b7h8.htm/, Retrieved Fri, 07 Oct 2022 00:41:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=39078, Retrieved Fri, 07 Oct 2022 00:41:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [] [2009-03-15 10:13:52] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
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Dataseries X:
112
118
132
129
121
135
148
148
136
119
104
118
115
126
141
135
125
149
170
170
158
133
114
140
145
150
178
163
172
178
199
199
184
162
146
166
171
180
193
181
183
218
230
242
209
191
172
194
196
196
236
235
229
243
264
272
237
211
180
201
204
188
235
227
234
264
302
293
259
229
203
229
242
233
267
269
270
315
364
347
312
274
237
278
284
277
317
313
318
374
413
405
355
306
271
306
315
301
356
348
355
422
465
467
404
347
305
336
340
318
362
348
363
435
491
505
404
359
310
337
360
342
406
396
420
472
548
559
463
407
362
405
417
391
419
461
472
535
622
606
508
461
390
432




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=39078&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=39078&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=39078&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 time6 seconds
R Server'George Udny Yule' @ 72.249.76.132







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.275592931492333
beta0.0326927269947438
gamma0.870730972374008

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.275592931492333 \tabularnewline
beta & 0.0326927269947438 \tabularnewline
gamma & 0.870730972374008 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=39078&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]0.275592931492333[/C][/ROW]
[ROW][C]beta[/C][C]0.0326927269947438[/C][/ROW]
[ROW][C]gamma[/C][C]0.870730972374008[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=39078&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=39078&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
alpha0.275592931492333
beta0.0326927269947438
gamma0.870730972374008







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
13115111.0818087088673.91819129113328
14126122.3314538747423.66854612525761
15141137.4390153761573.56098462384284
16135132.3233832202932.67661677970653
17125123.4796828181471.52031718185303
18149147.6672902331881.33270976681194
19170162.4432448018997.55675519810066
20170165.5295923942174.47040760578255
21158153.8877144649334.11228553506743
22133136.318642929536-3.3186429295356
23114119.090609201624-5.09060920162396
24140133.9887149606776.0112850393225
25145134.83037010966310.1696298903367
26150149.7051460153790.294853984620914
27178166.54082485992411.4591751400757
28163161.7497291200691.25027087993098
29172149.75750588289122.2424941171092
30178185.647729927987-7.64772992798655
31199206.262157804499-7.26215780449877
32199203.180877899402-4.18087789940233
33184186.419241979639-2.41924197963931
34162158.1477827595373.85221724046292
35146138.3687169810807.63128301892016
36166169.141347816413-3.14134781641266
37171170.3609458701890.639054129810944
38180177.4381780479192.56182195208055
39193206.247937689468-13.2479376894684
40181185.960724160939-4.96072416093864
41183184.798364145754-1.79836414575391
42218195.68435973357222.3156402664278
43230227.4983879410452.5016120589554
44242229.00476968851912.9952303114810
45209215.630988418267-6.63098841826653
46191186.2865983510884.71340164891208
47172166.0376439935575.96235600644326
48194192.8425095100711.15749048992947
49196198.309952885667-2.30995288566717
50196206.984486038046-10.9844860380456
51236224.19294089623311.8070591037674
52235214.06541785565920.9345821443406
53229222.6752524470976.32474755290255
54243256.738013695458-13.7380136954578
55264268.357973304421-4.35797330442080
56272275.595124348779-3.59512434877928
57237240.950022441534-3.95002244153355
58211216.418700337475-5.41870033747466
59180191.302342357419-11.3023423574190
60201212.165187519317-11.1651875193170
61204211.884585973233-7.88458597323302
62188213.278482377796-25.2784823777957
63235241.844280631679-6.84428063167888
64227231.126644343174-4.12664434317369
65234222.84756235744411.1524376425561
66264244.40357368199619.5964263180045
67302270.92543678752531.0745632124749
68293288.4633043826304.53669561737041
69259253.3255731144295.67442688557071
70229228.4572425854800.542757414519542
71203198.7667237110574.23327628894256
72229226.3253709300382.67462906996178
73242232.5076979211129.49230207888777
74233226.1554706830966.84452931690356
75267284.984135370815-17.984135370815
76269271.954055875764-2.95405587576425
77270274.414364054006-4.41436405400611
78315301.11087543871913.8891245612812
79364337.48148399701926.5185160029808
80347335.98551213636611.0144878636339
81312297.83665553117714.1633444688228
82274267.3004377139336.69956228606708
83237236.9925415061950.00745849380476216
84278266.90068203576711.0993179642327
85284281.6339304999812.36606950001874
86277269.9672327497047.03276725029627
87317320.174971097699-3.17497109769943
88313321.276901514013-8.2769015140127
89318322.045741716523-4.04574171652263
90374367.951193908366.04880609163985
91413417.20324189844-4.20324189843973
92405394.60621928243910.3937807175610
93355352.3066369650062.69336303499392
94306308.449783989787-2.44978398978702
95271266.696313192674.30368680732988
96306309.394151440329-3.39415144032859
97315315.103159698916-0.103159698916272
98301304.405343346902-3.4053433469017
99356349.0581654503796.94183454962058
100348349.292530352998-1.29253035299848
101355355.07317102392-0.0731710239203949
102422414.3609383732847.6390616267164
103465462.0911562758252.90884372417509
104467448.97971785271118.0202821472888
105404397.6350099221196.36499007788103
106347345.4509662878941.54903371210571
107305304.2430311120760.756968887924245
108336345.615225431220-9.61522543121976
109340352.616275490595-12.6162754905953
110318334.810864863475-16.8108648634752
111362386.941342201651-24.9413422016506
112348372.190810667806-24.1908106678064
113363372.090086438278-9.09008643827758
114435435.498520155361-0.498520155360723
115491478.41837009479112.5816299052085
116505476.29775492897928.7022450710215
117404417.219149854204-13.2191498542045
118359354.4369029627594.56309703724105
119310311.876475452214-1.87647545221381
120337345.975115388041-8.97511538804093
121360350.643058140329.35694185967964
122342334.9945802071107.0054197928896
123406390.68587542554615.3141245744536
124396386.4988658447969.50113415520354
125420407.13076227321912.8692377267807
126472491.605011219962-19.6050112199623
127548543.7800465587394.2199534412614
128559549.93532739179.0646726082997
129463449.63874670942213.3612532905782
130407399.9130342117697.08696578823083
131362348.39721266746913.6027873325311
132405386.65208470331618.3479152966844
133417413.9167814674313.08321853256905
134391392.451160427228-1.45116042722816
135419460.170497130596-41.1704971305962
136461435.42175557135625.5782444286439
137472465.0951028577916.90489714220945
138535534.3526743899110.647325610088842
139622616.735366186225.26463381378016
140606627.551069242193-21.5510692421930
141508510.133139879417-2.1331398794174
142461446.16279163776914.8372083622306
143390395.452817969955-5.45281796995471
144432434.572452493995-2.57245249399460

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 115 & 111.081808708867 & 3.91819129113328 \tabularnewline
14 & 126 & 122.331453874742 & 3.66854612525761 \tabularnewline
15 & 141 & 137.439015376157 & 3.56098462384284 \tabularnewline
16 & 135 & 132.323383220293 & 2.67661677970653 \tabularnewline
17 & 125 & 123.479682818147 & 1.52031718185303 \tabularnewline
18 & 149 & 147.667290233188 & 1.33270976681194 \tabularnewline
19 & 170 & 162.443244801899 & 7.55675519810066 \tabularnewline
20 & 170 & 165.529592394217 & 4.47040760578255 \tabularnewline
21 & 158 & 153.887714464933 & 4.11228553506743 \tabularnewline
22 & 133 & 136.318642929536 & -3.3186429295356 \tabularnewline
23 & 114 & 119.090609201624 & -5.09060920162396 \tabularnewline
24 & 140 & 133.988714960677 & 6.0112850393225 \tabularnewline
25 & 145 & 134.830370109663 & 10.1696298903367 \tabularnewline
26 & 150 & 149.705146015379 & 0.294853984620914 \tabularnewline
27 & 178 & 166.540824859924 & 11.4591751400757 \tabularnewline
28 & 163 & 161.749729120069 & 1.25027087993098 \tabularnewline
29 & 172 & 149.757505882891 & 22.2424941171092 \tabularnewline
30 & 178 & 185.647729927987 & -7.64772992798655 \tabularnewline
31 & 199 & 206.262157804499 & -7.26215780449877 \tabularnewline
32 & 199 & 203.180877899402 & -4.18087789940233 \tabularnewline
33 & 184 & 186.419241979639 & -2.41924197963931 \tabularnewline
34 & 162 & 158.147782759537 & 3.85221724046292 \tabularnewline
35 & 146 & 138.368716981080 & 7.63128301892016 \tabularnewline
36 & 166 & 169.141347816413 & -3.14134781641266 \tabularnewline
37 & 171 & 170.360945870189 & 0.639054129810944 \tabularnewline
38 & 180 & 177.438178047919 & 2.56182195208055 \tabularnewline
39 & 193 & 206.247937689468 & -13.2479376894684 \tabularnewline
40 & 181 & 185.960724160939 & -4.96072416093864 \tabularnewline
41 & 183 & 184.798364145754 & -1.79836414575391 \tabularnewline
42 & 218 & 195.684359733572 & 22.3156402664278 \tabularnewline
43 & 230 & 227.498387941045 & 2.5016120589554 \tabularnewline
44 & 242 & 229.004769688519 & 12.9952303114810 \tabularnewline
45 & 209 & 215.630988418267 & -6.63098841826653 \tabularnewline
46 & 191 & 186.286598351088 & 4.71340164891208 \tabularnewline
47 & 172 & 166.037643993557 & 5.96235600644326 \tabularnewline
48 & 194 & 192.842509510071 & 1.15749048992947 \tabularnewline
49 & 196 & 198.309952885667 & -2.30995288566717 \tabularnewline
50 & 196 & 206.984486038046 & -10.9844860380456 \tabularnewline
51 & 236 & 224.192940896233 & 11.8070591037674 \tabularnewline
52 & 235 & 214.065417855659 & 20.9345821443406 \tabularnewline
53 & 229 & 222.675252447097 & 6.32474755290255 \tabularnewline
54 & 243 & 256.738013695458 & -13.7380136954578 \tabularnewline
55 & 264 & 268.357973304421 & -4.35797330442080 \tabularnewline
56 & 272 & 275.595124348779 & -3.59512434877928 \tabularnewline
57 & 237 & 240.950022441534 & -3.95002244153355 \tabularnewline
58 & 211 & 216.418700337475 & -5.41870033747466 \tabularnewline
59 & 180 & 191.302342357419 & -11.3023423574190 \tabularnewline
60 & 201 & 212.165187519317 & -11.1651875193170 \tabularnewline
61 & 204 & 211.884585973233 & -7.88458597323302 \tabularnewline
62 & 188 & 213.278482377796 & -25.2784823777957 \tabularnewline
63 & 235 & 241.844280631679 & -6.84428063167888 \tabularnewline
64 & 227 & 231.126644343174 & -4.12664434317369 \tabularnewline
65 & 234 & 222.847562357444 & 11.1524376425561 \tabularnewline
66 & 264 & 244.403573681996 & 19.5964263180045 \tabularnewline
67 & 302 & 270.925436787525 & 31.0745632124749 \tabularnewline
68 & 293 & 288.463304382630 & 4.53669561737041 \tabularnewline
69 & 259 & 253.325573114429 & 5.67442688557071 \tabularnewline
70 & 229 & 228.457242585480 & 0.542757414519542 \tabularnewline
71 & 203 & 198.766723711057 & 4.23327628894256 \tabularnewline
72 & 229 & 226.325370930038 & 2.67462906996178 \tabularnewline
73 & 242 & 232.507697921112 & 9.49230207888777 \tabularnewline
74 & 233 & 226.155470683096 & 6.84452931690356 \tabularnewline
75 & 267 & 284.984135370815 & -17.984135370815 \tabularnewline
76 & 269 & 271.954055875764 & -2.95405587576425 \tabularnewline
77 & 270 & 274.414364054006 & -4.41436405400611 \tabularnewline
78 & 315 & 301.110875438719 & 13.8891245612812 \tabularnewline
79 & 364 & 337.481483997019 & 26.5185160029808 \tabularnewline
80 & 347 & 335.985512136366 & 11.0144878636339 \tabularnewline
81 & 312 & 297.836655531177 & 14.1633444688228 \tabularnewline
82 & 274 & 267.300437713933 & 6.69956228606708 \tabularnewline
83 & 237 & 236.992541506195 & 0.00745849380476216 \tabularnewline
84 & 278 & 266.900682035767 & 11.0993179642327 \tabularnewline
85 & 284 & 281.633930499981 & 2.36606950001874 \tabularnewline
86 & 277 & 269.967232749704 & 7.03276725029627 \tabularnewline
87 & 317 & 320.174971097699 & -3.17497109769943 \tabularnewline
88 & 313 & 321.276901514013 & -8.2769015140127 \tabularnewline
89 & 318 & 322.045741716523 & -4.04574171652263 \tabularnewline
90 & 374 & 367.95119390836 & 6.04880609163985 \tabularnewline
91 & 413 & 417.20324189844 & -4.20324189843973 \tabularnewline
92 & 405 & 394.606219282439 & 10.3937807175610 \tabularnewline
93 & 355 & 352.306636965006 & 2.69336303499392 \tabularnewline
94 & 306 & 308.449783989787 & -2.44978398978702 \tabularnewline
95 & 271 & 266.69631319267 & 4.30368680732988 \tabularnewline
96 & 306 & 309.394151440329 & -3.39415144032859 \tabularnewline
97 & 315 & 315.103159698916 & -0.103159698916272 \tabularnewline
98 & 301 & 304.405343346902 & -3.4053433469017 \tabularnewline
99 & 356 & 349.058165450379 & 6.94183454962058 \tabularnewline
100 & 348 & 349.292530352998 & -1.29253035299848 \tabularnewline
101 & 355 & 355.07317102392 & -0.0731710239203949 \tabularnewline
102 & 422 & 414.360938373284 & 7.6390616267164 \tabularnewline
103 & 465 & 462.091156275825 & 2.90884372417509 \tabularnewline
104 & 467 & 448.979717852711 & 18.0202821472888 \tabularnewline
105 & 404 & 397.635009922119 & 6.36499007788103 \tabularnewline
106 & 347 & 345.450966287894 & 1.54903371210571 \tabularnewline
107 & 305 & 304.243031112076 & 0.756968887924245 \tabularnewline
108 & 336 & 345.615225431220 & -9.61522543121976 \tabularnewline
109 & 340 & 352.616275490595 & -12.6162754905953 \tabularnewline
110 & 318 & 334.810864863475 & -16.8108648634752 \tabularnewline
111 & 362 & 386.941342201651 & -24.9413422016506 \tabularnewline
112 & 348 & 372.190810667806 & -24.1908106678064 \tabularnewline
113 & 363 & 372.090086438278 & -9.09008643827758 \tabularnewline
114 & 435 & 435.498520155361 & -0.498520155360723 \tabularnewline
115 & 491 & 478.418370094791 & 12.5816299052085 \tabularnewline
116 & 505 & 476.297754928979 & 28.7022450710215 \tabularnewline
117 & 404 & 417.219149854204 & -13.2191498542045 \tabularnewline
118 & 359 & 354.436902962759 & 4.56309703724105 \tabularnewline
119 & 310 & 311.876475452214 & -1.87647545221381 \tabularnewline
120 & 337 & 345.975115388041 & -8.97511538804093 \tabularnewline
121 & 360 & 350.64305814032 & 9.35694185967964 \tabularnewline
122 & 342 & 334.994580207110 & 7.0054197928896 \tabularnewline
123 & 406 & 390.685875425546 & 15.3141245744536 \tabularnewline
124 & 396 & 386.498865844796 & 9.50113415520354 \tabularnewline
125 & 420 & 407.130762273219 & 12.8692377267807 \tabularnewline
126 & 472 & 491.605011219962 & -19.6050112199623 \tabularnewline
127 & 548 & 543.780046558739 & 4.2199534412614 \tabularnewline
128 & 559 & 549.9353273917 & 9.0646726082997 \tabularnewline
129 & 463 & 449.638746709422 & 13.3612532905782 \tabularnewline
130 & 407 & 399.913034211769 & 7.08696578823083 \tabularnewline
131 & 362 & 348.397212667469 & 13.6027873325311 \tabularnewline
132 & 405 & 386.652084703316 & 18.3479152966844 \tabularnewline
133 & 417 & 413.916781467431 & 3.08321853256905 \tabularnewline
134 & 391 & 392.451160427228 & -1.45116042722816 \tabularnewline
135 & 419 & 460.170497130596 & -41.1704971305962 \tabularnewline
136 & 461 & 435.421755571356 & 25.5782444286439 \tabularnewline
137 & 472 & 465.095102857791 & 6.90489714220945 \tabularnewline
138 & 535 & 534.352674389911 & 0.647325610088842 \tabularnewline
139 & 622 & 616.73536618622 & 5.26463381378016 \tabularnewline
140 & 606 & 627.551069242193 & -21.5510692421930 \tabularnewline
141 & 508 & 510.133139879417 & -2.1331398794174 \tabularnewline
142 & 461 & 446.162791637769 & 14.8372083622306 \tabularnewline
143 & 390 & 395.452817969955 & -5.45281796995471 \tabularnewline
144 & 432 & 434.572452493995 & -2.57245249399460 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=39078&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]13[/C][C]115[/C][C]111.081808708867[/C][C]3.91819129113328[/C][/ROW]
[ROW][C]14[/C][C]126[/C][C]122.331453874742[/C][C]3.66854612525761[/C][/ROW]
[ROW][C]15[/C][C]141[/C][C]137.439015376157[/C][C]3.56098462384284[/C][/ROW]
[ROW][C]16[/C][C]135[/C][C]132.323383220293[/C][C]2.67661677970653[/C][/ROW]
[ROW][C]17[/C][C]125[/C][C]123.479682818147[/C][C]1.52031718185303[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]147.667290233188[/C][C]1.33270976681194[/C][/ROW]
[ROW][C]19[/C][C]170[/C][C]162.443244801899[/C][C]7.55675519810066[/C][/ROW]
[ROW][C]20[/C][C]170[/C][C]165.529592394217[/C][C]4.47040760578255[/C][/ROW]
[ROW][C]21[/C][C]158[/C][C]153.887714464933[/C][C]4.11228553506743[/C][/ROW]
[ROW][C]22[/C][C]133[/C][C]136.318642929536[/C][C]-3.3186429295356[/C][/ROW]
[ROW][C]23[/C][C]114[/C][C]119.090609201624[/C][C]-5.09060920162396[/C][/ROW]
[ROW][C]24[/C][C]140[/C][C]133.988714960677[/C][C]6.0112850393225[/C][/ROW]
[ROW][C]25[/C][C]145[/C][C]134.830370109663[/C][C]10.1696298903367[/C][/ROW]
[ROW][C]26[/C][C]150[/C][C]149.705146015379[/C][C]0.294853984620914[/C][/ROW]
[ROW][C]27[/C][C]178[/C][C]166.540824859924[/C][C]11.4591751400757[/C][/ROW]
[ROW][C]28[/C][C]163[/C][C]161.749729120069[/C][C]1.25027087993098[/C][/ROW]
[ROW][C]29[/C][C]172[/C][C]149.757505882891[/C][C]22.2424941171092[/C][/ROW]
[ROW][C]30[/C][C]178[/C][C]185.647729927987[/C][C]-7.64772992798655[/C][/ROW]
[ROW][C]31[/C][C]199[/C][C]206.262157804499[/C][C]-7.26215780449877[/C][/ROW]
[ROW][C]32[/C][C]199[/C][C]203.180877899402[/C][C]-4.18087789940233[/C][/ROW]
[ROW][C]33[/C][C]184[/C][C]186.419241979639[/C][C]-2.41924197963931[/C][/ROW]
[ROW][C]34[/C][C]162[/C][C]158.147782759537[/C][C]3.85221724046292[/C][/ROW]
[ROW][C]35[/C][C]146[/C][C]138.368716981080[/C][C]7.63128301892016[/C][/ROW]
[ROW][C]36[/C][C]166[/C][C]169.141347816413[/C][C]-3.14134781641266[/C][/ROW]
[ROW][C]37[/C][C]171[/C][C]170.360945870189[/C][C]0.639054129810944[/C][/ROW]
[ROW][C]38[/C][C]180[/C][C]177.438178047919[/C][C]2.56182195208055[/C][/ROW]
[ROW][C]39[/C][C]193[/C][C]206.247937689468[/C][C]-13.2479376894684[/C][/ROW]
[ROW][C]40[/C][C]181[/C][C]185.960724160939[/C][C]-4.96072416093864[/C][/ROW]
[ROW][C]41[/C][C]183[/C][C]184.798364145754[/C][C]-1.79836414575391[/C][/ROW]
[ROW][C]42[/C][C]218[/C][C]195.684359733572[/C][C]22.3156402664278[/C][/ROW]
[ROW][C]43[/C][C]230[/C][C]227.498387941045[/C][C]2.5016120589554[/C][/ROW]
[ROW][C]44[/C][C]242[/C][C]229.004769688519[/C][C]12.9952303114810[/C][/ROW]
[ROW][C]45[/C][C]209[/C][C]215.630988418267[/C][C]-6.63098841826653[/C][/ROW]
[ROW][C]46[/C][C]191[/C][C]186.286598351088[/C][C]4.71340164891208[/C][/ROW]
[ROW][C]47[/C][C]172[/C][C]166.037643993557[/C][C]5.96235600644326[/C][/ROW]
[ROW][C]48[/C][C]194[/C][C]192.842509510071[/C][C]1.15749048992947[/C][/ROW]
[ROW][C]49[/C][C]196[/C][C]198.309952885667[/C][C]-2.30995288566717[/C][/ROW]
[ROW][C]50[/C][C]196[/C][C]206.984486038046[/C][C]-10.9844860380456[/C][/ROW]
[ROW][C]51[/C][C]236[/C][C]224.192940896233[/C][C]11.8070591037674[/C][/ROW]
[ROW][C]52[/C][C]235[/C][C]214.065417855659[/C][C]20.9345821443406[/C][/ROW]
[ROW][C]53[/C][C]229[/C][C]222.675252447097[/C][C]6.32474755290255[/C][/ROW]
[ROW][C]54[/C][C]243[/C][C]256.738013695458[/C][C]-13.7380136954578[/C][/ROW]
[ROW][C]55[/C][C]264[/C][C]268.357973304421[/C][C]-4.35797330442080[/C][/ROW]
[ROW][C]56[/C][C]272[/C][C]275.595124348779[/C][C]-3.59512434877928[/C][/ROW]
[ROW][C]57[/C][C]237[/C][C]240.950022441534[/C][C]-3.95002244153355[/C][/ROW]
[ROW][C]58[/C][C]211[/C][C]216.418700337475[/C][C]-5.41870033747466[/C][/ROW]
[ROW][C]59[/C][C]180[/C][C]191.302342357419[/C][C]-11.3023423574190[/C][/ROW]
[ROW][C]60[/C][C]201[/C][C]212.165187519317[/C][C]-11.1651875193170[/C][/ROW]
[ROW][C]61[/C][C]204[/C][C]211.884585973233[/C][C]-7.88458597323302[/C][/ROW]
[ROW][C]62[/C][C]188[/C][C]213.278482377796[/C][C]-25.2784823777957[/C][/ROW]
[ROW][C]63[/C][C]235[/C][C]241.844280631679[/C][C]-6.84428063167888[/C][/ROW]
[ROW][C]64[/C][C]227[/C][C]231.126644343174[/C][C]-4.12664434317369[/C][/ROW]
[ROW][C]65[/C][C]234[/C][C]222.847562357444[/C][C]11.1524376425561[/C][/ROW]
[ROW][C]66[/C][C]264[/C][C]244.403573681996[/C][C]19.5964263180045[/C][/ROW]
[ROW][C]67[/C][C]302[/C][C]270.925436787525[/C][C]31.0745632124749[/C][/ROW]
[ROW][C]68[/C][C]293[/C][C]288.463304382630[/C][C]4.53669561737041[/C][/ROW]
[ROW][C]69[/C][C]259[/C][C]253.325573114429[/C][C]5.67442688557071[/C][/ROW]
[ROW][C]70[/C][C]229[/C][C]228.457242585480[/C][C]0.542757414519542[/C][/ROW]
[ROW][C]71[/C][C]203[/C][C]198.766723711057[/C][C]4.23327628894256[/C][/ROW]
[ROW][C]72[/C][C]229[/C][C]226.325370930038[/C][C]2.67462906996178[/C][/ROW]
[ROW][C]73[/C][C]242[/C][C]232.507697921112[/C][C]9.49230207888777[/C][/ROW]
[ROW][C]74[/C][C]233[/C][C]226.155470683096[/C][C]6.84452931690356[/C][/ROW]
[ROW][C]75[/C][C]267[/C][C]284.984135370815[/C][C]-17.984135370815[/C][/ROW]
[ROW][C]76[/C][C]269[/C][C]271.954055875764[/C][C]-2.95405587576425[/C][/ROW]
[ROW][C]77[/C][C]270[/C][C]274.414364054006[/C][C]-4.41436405400611[/C][/ROW]
[ROW][C]78[/C][C]315[/C][C]301.110875438719[/C][C]13.8891245612812[/C][/ROW]
[ROW][C]79[/C][C]364[/C][C]337.481483997019[/C][C]26.5185160029808[/C][/ROW]
[ROW][C]80[/C][C]347[/C][C]335.985512136366[/C][C]11.0144878636339[/C][/ROW]
[ROW][C]81[/C][C]312[/C][C]297.836655531177[/C][C]14.1633444688228[/C][/ROW]
[ROW][C]82[/C][C]274[/C][C]267.300437713933[/C][C]6.69956228606708[/C][/ROW]
[ROW][C]83[/C][C]237[/C][C]236.992541506195[/C][C]0.00745849380476216[/C][/ROW]
[ROW][C]84[/C][C]278[/C][C]266.900682035767[/C][C]11.0993179642327[/C][/ROW]
[ROW][C]85[/C][C]284[/C][C]281.633930499981[/C][C]2.36606950001874[/C][/ROW]
[ROW][C]86[/C][C]277[/C][C]269.967232749704[/C][C]7.03276725029627[/C][/ROW]
[ROW][C]87[/C][C]317[/C][C]320.174971097699[/C][C]-3.17497109769943[/C][/ROW]
[ROW][C]88[/C][C]313[/C][C]321.276901514013[/C][C]-8.2769015140127[/C][/ROW]
[ROW][C]89[/C][C]318[/C][C]322.045741716523[/C][C]-4.04574171652263[/C][/ROW]
[ROW][C]90[/C][C]374[/C][C]367.95119390836[/C][C]6.04880609163985[/C][/ROW]
[ROW][C]91[/C][C]413[/C][C]417.20324189844[/C][C]-4.20324189843973[/C][/ROW]
[ROW][C]92[/C][C]405[/C][C]394.606219282439[/C][C]10.3937807175610[/C][/ROW]
[ROW][C]93[/C][C]355[/C][C]352.306636965006[/C][C]2.69336303499392[/C][/ROW]
[ROW][C]94[/C][C]306[/C][C]308.449783989787[/C][C]-2.44978398978702[/C][/ROW]
[ROW][C]95[/C][C]271[/C][C]266.69631319267[/C][C]4.30368680732988[/C][/ROW]
[ROW][C]96[/C][C]306[/C][C]309.394151440329[/C][C]-3.39415144032859[/C][/ROW]
[ROW][C]97[/C][C]315[/C][C]315.103159698916[/C][C]-0.103159698916272[/C][/ROW]
[ROW][C]98[/C][C]301[/C][C]304.405343346902[/C][C]-3.4053433469017[/C][/ROW]
[ROW][C]99[/C][C]356[/C][C]349.058165450379[/C][C]6.94183454962058[/C][/ROW]
[ROW][C]100[/C][C]348[/C][C]349.292530352998[/C][C]-1.29253035299848[/C][/ROW]
[ROW][C]101[/C][C]355[/C][C]355.07317102392[/C][C]-0.0731710239203949[/C][/ROW]
[ROW][C]102[/C][C]422[/C][C]414.360938373284[/C][C]7.6390616267164[/C][/ROW]
[ROW][C]103[/C][C]465[/C][C]462.091156275825[/C][C]2.90884372417509[/C][/ROW]
[ROW][C]104[/C][C]467[/C][C]448.979717852711[/C][C]18.0202821472888[/C][/ROW]
[ROW][C]105[/C][C]404[/C][C]397.635009922119[/C][C]6.36499007788103[/C][/ROW]
[ROW][C]106[/C][C]347[/C][C]345.450966287894[/C][C]1.54903371210571[/C][/ROW]
[ROW][C]107[/C][C]305[/C][C]304.243031112076[/C][C]0.756968887924245[/C][/ROW]
[ROW][C]108[/C][C]336[/C][C]345.615225431220[/C][C]-9.61522543121976[/C][/ROW]
[ROW][C]109[/C][C]340[/C][C]352.616275490595[/C][C]-12.6162754905953[/C][/ROW]
[ROW][C]110[/C][C]318[/C][C]334.810864863475[/C][C]-16.8108648634752[/C][/ROW]
[ROW][C]111[/C][C]362[/C][C]386.941342201651[/C][C]-24.9413422016506[/C][/ROW]
[ROW][C]112[/C][C]348[/C][C]372.190810667806[/C][C]-24.1908106678064[/C][/ROW]
[ROW][C]113[/C][C]363[/C][C]372.090086438278[/C][C]-9.09008643827758[/C][/ROW]
[ROW][C]114[/C][C]435[/C][C]435.498520155361[/C][C]-0.498520155360723[/C][/ROW]
[ROW][C]115[/C][C]491[/C][C]478.418370094791[/C][C]12.5816299052085[/C][/ROW]
[ROW][C]116[/C][C]505[/C][C]476.297754928979[/C][C]28.7022450710215[/C][/ROW]
[ROW][C]117[/C][C]404[/C][C]417.219149854204[/C][C]-13.2191498542045[/C][/ROW]
[ROW][C]118[/C][C]359[/C][C]354.436902962759[/C][C]4.56309703724105[/C][/ROW]
[ROW][C]119[/C][C]310[/C][C]311.876475452214[/C][C]-1.87647545221381[/C][/ROW]
[ROW][C]120[/C][C]337[/C][C]345.975115388041[/C][C]-8.97511538804093[/C][/ROW]
[ROW][C]121[/C][C]360[/C][C]350.64305814032[/C][C]9.35694185967964[/C][/ROW]
[ROW][C]122[/C][C]342[/C][C]334.994580207110[/C][C]7.0054197928896[/C][/ROW]
[ROW][C]123[/C][C]406[/C][C]390.685875425546[/C][C]15.3141245744536[/C][/ROW]
[ROW][C]124[/C][C]396[/C][C]386.498865844796[/C][C]9.50113415520354[/C][/ROW]
[ROW][C]125[/C][C]420[/C][C]407.130762273219[/C][C]12.8692377267807[/C][/ROW]
[ROW][C]126[/C][C]472[/C][C]491.605011219962[/C][C]-19.6050112199623[/C][/ROW]
[ROW][C]127[/C][C]548[/C][C]543.780046558739[/C][C]4.2199534412614[/C][/ROW]
[ROW][C]128[/C][C]559[/C][C]549.9353273917[/C][C]9.0646726082997[/C][/ROW]
[ROW][C]129[/C][C]463[/C][C]449.638746709422[/C][C]13.3612532905782[/C][/ROW]
[ROW][C]130[/C][C]407[/C][C]399.913034211769[/C][C]7.08696578823083[/C][/ROW]
[ROW][C]131[/C][C]362[/C][C]348.397212667469[/C][C]13.6027873325311[/C][/ROW]
[ROW][C]132[/C][C]405[/C][C]386.652084703316[/C][C]18.3479152966844[/C][/ROW]
[ROW][C]133[/C][C]417[/C][C]413.916781467431[/C][C]3.08321853256905[/C][/ROW]
[ROW][C]134[/C][C]391[/C][C]392.451160427228[/C][C]-1.45116042722816[/C][/ROW]
[ROW][C]135[/C][C]419[/C][C]460.170497130596[/C][C]-41.1704971305962[/C][/ROW]
[ROW][C]136[/C][C]461[/C][C]435.421755571356[/C][C]25.5782444286439[/C][/ROW]
[ROW][C]137[/C][C]472[/C][C]465.095102857791[/C][C]6.90489714220945[/C][/ROW]
[ROW][C]138[/C][C]535[/C][C]534.352674389911[/C][C]0.647325610088842[/C][/ROW]
[ROW][C]139[/C][C]622[/C][C]616.73536618622[/C][C]5.26463381378016[/C][/ROW]
[ROW][C]140[/C][C]606[/C][C]627.551069242193[/C][C]-21.5510692421930[/C][/ROW]
[ROW][C]141[/C][C]508[/C][C]510.133139879417[/C][C]-2.1331398794174[/C][/ROW]
[ROW][C]142[/C][C]461[/C][C]446.162791637769[/C][C]14.8372083622306[/C][/ROW]
[ROW][C]143[/C][C]390[/C][C]395.452817969955[/C][C]-5.45281796995471[/C][/ROW]
[ROW][C]144[/C][C]432[/C][C]434.572452493995[/C][C]-2.57245249399460[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=39078&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=39078&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
13115111.0818087088673.91819129113328
14126122.3314538747423.66854612525761
15141137.4390153761573.56098462384284
16135132.3233832202932.67661677970653
17125123.4796828181471.52031718185303
18149147.6672902331881.33270976681194
19170162.4432448018997.55675519810066
20170165.5295923942174.47040760578255
21158153.8877144649334.11228553506743
22133136.318642929536-3.3186429295356
23114119.090609201624-5.09060920162396
24140133.9887149606776.0112850393225
25145134.83037010966310.1696298903367
26150149.7051460153790.294853984620914
27178166.54082485992411.4591751400757
28163161.7497291200691.25027087993098
29172149.75750588289122.2424941171092
30178185.647729927987-7.64772992798655
31199206.262157804499-7.26215780449877
32199203.180877899402-4.18087789940233
33184186.419241979639-2.41924197963931
34162158.1477827595373.85221724046292
35146138.3687169810807.63128301892016
36166169.141347816413-3.14134781641266
37171170.3609458701890.639054129810944
38180177.4381780479192.56182195208055
39193206.247937689468-13.2479376894684
40181185.960724160939-4.96072416093864
41183184.798364145754-1.79836414575391
42218195.68435973357222.3156402664278
43230227.4983879410452.5016120589554
44242229.00476968851912.9952303114810
45209215.630988418267-6.63098841826653
46191186.2865983510884.71340164891208
47172166.0376439935575.96235600644326
48194192.8425095100711.15749048992947
49196198.309952885667-2.30995288566717
50196206.984486038046-10.9844860380456
51236224.19294089623311.8070591037674
52235214.06541785565920.9345821443406
53229222.6752524470976.32474755290255
54243256.738013695458-13.7380136954578
55264268.357973304421-4.35797330442080
56272275.595124348779-3.59512434877928
57237240.950022441534-3.95002244153355
58211216.418700337475-5.41870033747466
59180191.302342357419-11.3023423574190
60201212.165187519317-11.1651875193170
61204211.884585973233-7.88458597323302
62188213.278482377796-25.2784823777957
63235241.844280631679-6.84428063167888
64227231.126644343174-4.12664434317369
65234222.84756235744411.1524376425561
66264244.40357368199619.5964263180045
67302270.92543678752531.0745632124749
68293288.4633043826304.53669561737041
69259253.3255731144295.67442688557071
70229228.4572425854800.542757414519542
71203198.7667237110574.23327628894256
72229226.3253709300382.67462906996178
73242232.5076979211129.49230207888777
74233226.1554706830966.84452931690356
75267284.984135370815-17.984135370815
76269271.954055875764-2.95405587576425
77270274.414364054006-4.41436405400611
78315301.11087543871913.8891245612812
79364337.48148399701926.5185160029808
80347335.98551213636611.0144878636339
81312297.83665553117714.1633444688228
82274267.3004377139336.69956228606708
83237236.9925415061950.00745849380476216
84278266.90068203576711.0993179642327
85284281.6339304999812.36606950001874
86277269.9672327497047.03276725029627
87317320.174971097699-3.17497109769943
88313321.276901514013-8.2769015140127
89318322.045741716523-4.04574171652263
90374367.951193908366.04880609163985
91413417.20324189844-4.20324189843973
92405394.60621928243910.3937807175610
93355352.3066369650062.69336303499392
94306308.449783989787-2.44978398978702
95271266.696313192674.30368680732988
96306309.394151440329-3.39415144032859
97315315.103159698916-0.103159698916272
98301304.405343346902-3.4053433469017
99356349.0581654503796.94183454962058
100348349.292530352998-1.29253035299848
101355355.07317102392-0.0731710239203949
102422414.3609383732847.6390616267164
103465462.0911562758252.90884372417509
104467448.97971785271118.0202821472888
105404397.6350099221196.36499007788103
106347345.4509662878941.54903371210571
107305304.2430311120760.756968887924245
108336345.615225431220-9.61522543121976
109340352.616275490595-12.6162754905953
110318334.810864863475-16.8108648634752
111362386.941342201651-24.9413422016506
112348372.190810667806-24.1908106678064
113363372.090086438278-9.09008643827758
114435435.498520155361-0.498520155360723
115491478.41837009479112.5816299052085
116505476.29775492897928.7022450710215
117404417.219149854204-13.2191498542045
118359354.4369029627594.56309703724105
119310311.876475452214-1.87647545221381
120337345.975115388041-8.97511538804093
121360350.643058140329.35694185967964
122342334.9945802071107.0054197928896
123406390.68587542554615.3141245744536
124396386.4988658447969.50113415520354
125420407.13076227321912.8692377267807
126472491.605011219962-19.6050112199623
127548543.7800465587394.2199534412614
128559549.93532739179.0646726082997
129463449.63874670942213.3612532905782
130407399.9130342117697.08696578823083
131362348.39721266746913.6027873325311
132405386.65208470331618.3479152966844
133417413.9167814674313.08321853256905
134391392.451160427228-1.45116042722816
135419460.170497130596-41.1704971305962
136461435.42175557135625.5782444286439
137472465.0951028577916.90489714220945
138535534.3526743899110.647325610088842
139622616.735366186225.26463381378016
140606627.551069242193-21.5510692421930
141508510.133139879417-2.1331398794174
142461446.16279163776914.8372083622306
143390395.452817969955-5.45281796995471
144432434.572452493995-2.57245249399460







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
145447.055907793748427.306081839881466.805733747616
146419.712252491685399.132529133936440.291975849435
147464.867062938332442.962936376578486.771189500086
148496.08393808204472.832869325636519.335006838444
149507.532607460917483.13745188183531.927763040004
150575.45083938456548.708168067514602.193510701605
151666.592241551587636.628745108668696.555737994505
152657.913647955767627.181984104084688.64531180745
153550.308727275651521.63972761341578.977726937891
154492.985286976115465.015260234151520.955313718078
155420.207239449315393.468712587999446.945766310631
156465.634459283973443.300359438534487.968559129412

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
145 & 447.055907793748 & 427.306081839881 & 466.805733747616 \tabularnewline
146 & 419.712252491685 & 399.132529133936 & 440.291975849435 \tabularnewline
147 & 464.867062938332 & 442.962936376578 & 486.771189500086 \tabularnewline
148 & 496.08393808204 & 472.832869325636 & 519.335006838444 \tabularnewline
149 & 507.532607460917 & 483.13745188183 & 531.927763040004 \tabularnewline
150 & 575.45083938456 & 548.708168067514 & 602.193510701605 \tabularnewline
151 & 666.592241551587 & 636.628745108668 & 696.555737994505 \tabularnewline
152 & 657.913647955767 & 627.181984104084 & 688.64531180745 \tabularnewline
153 & 550.308727275651 & 521.63972761341 & 578.977726937891 \tabularnewline
154 & 492.985286976115 & 465.015260234151 & 520.955313718078 \tabularnewline
155 & 420.207239449315 & 393.468712587999 & 446.945766310631 \tabularnewline
156 & 465.634459283973 & 443.300359438534 & 487.968559129412 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=39078&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]145[/C][C]447.055907793748[/C][C]427.306081839881[/C][C]466.805733747616[/C][/ROW]
[ROW][C]146[/C][C]419.712252491685[/C][C]399.132529133936[/C][C]440.291975849435[/C][/ROW]
[ROW][C]147[/C][C]464.867062938332[/C][C]442.962936376578[/C][C]486.771189500086[/C][/ROW]
[ROW][C]148[/C][C]496.08393808204[/C][C]472.832869325636[/C][C]519.335006838444[/C][/ROW]
[ROW][C]149[/C][C]507.532607460917[/C][C]483.13745188183[/C][C]531.927763040004[/C][/ROW]
[ROW][C]150[/C][C]575.45083938456[/C][C]548.708168067514[/C][C]602.193510701605[/C][/ROW]
[ROW][C]151[/C][C]666.592241551587[/C][C]636.628745108668[/C][C]696.555737994505[/C][/ROW]
[ROW][C]152[/C][C]657.913647955767[/C][C]627.181984104084[/C][C]688.64531180745[/C][/ROW]
[ROW][C]153[/C][C]550.308727275651[/C][C]521.63972761341[/C][C]578.977726937891[/C][/ROW]
[ROW][C]154[/C][C]492.985286976115[/C][C]465.015260234151[/C][C]520.955313718078[/C][/ROW]
[ROW][C]155[/C][C]420.207239449315[/C][C]393.468712587999[/C][C]446.945766310631[/C][/ROW]
[ROW][C]156[/C][C]465.634459283973[/C][C]443.300359438534[/C][C]487.968559129412[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=39078&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=39078&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
145447.055907793748427.306081839881466.805733747616
146419.712252491685399.132529133936440.291975849435
147464.867062938332442.962936376578486.771189500086
148496.08393808204472.832869325636519.335006838444
149507.532607460917483.13745188183531.927763040004
150575.45083938456548.708168067514602.193510701605
151666.592241551587636.628745108668696.555737994505
152657.913647955767627.181984104084688.64531180745
153550.308727275651521.63972761341578.977726937891
154492.985286976115465.015260234151520.955313718078
155420.207239449315393.468712587999446.945766310631
156465.634459283973443.300359438534487.968559129412



Parameters (Session):
par1 = 12 ; par2 = Triple ; par3 = multiplicative ;
Parameters (R input):
par1 = 12 ; par2 = Triple ; par3 = multiplicative ;
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=0, beta=0)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=0)
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')