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 computationMon, 29 Mar 2010 16:46:56 +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/Mar/29/t1269881244lcs8cz0nnitz4u7.htm/, Retrieved Tue, 18 Jan 2022 13:48:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=74584, Retrieved Tue, 18 Jan 2022 13:48:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [test (after main ...] [2010-03-29 16:46:56] [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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74584&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74584&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74584&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.275592474736350
beta0.0326929527336615
gamma0.87072922226501

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.275592474736350 \tabularnewline
beta & 0.0326929527336615 \tabularnewline
gamma & 0.87072922226501 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74584&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]0.275592474736350[/C][/ROW]
[ROW][C]beta[/C][C]0.0326929527336615[/C][/ROW]
[ROW][C]gamma[/C][C]0.87072922226501[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74584&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74584&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
alpha0.275592474736350
beta0.0326929527336615
gamma0.87072922226501







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
13115111.0818087088673.91819129113328
14126122.3314521410863.66854785891434
15141137.4390125699003.56098743009966
16135132.3233803453812.67661965461949
17125123.4796804627291.52031953727142
18149147.6672883148701.33271168512962
19170162.4432437124737.55675628752724
20170165.5295896133214.47041038667871
21158153.887712294254.11228770575008
22133136.318641448733-3.31864144873339
23114119.090610790114-5.09061079011435
24140133.9887199504986.01128004950164
25145134.83036828991910.1696317100805
26150149.7051434413650.294856558634905
27178166.54082599966011.4591740003402
28163161.7497283781891.25027162181149
29172149.75750799304122.2424920069588
30178185.647723596323-7.64772359632292
31199206.262153619027-7.26215361902663
32199203.180886325985-4.18088632598545
33184186.419252204207-2.41925220420688
34162158.1477991367523.85220086324750
35146138.3687281983277.63127180167297
36166169.141340932275-3.14134093227528
37171170.3609415353510.639058464649423
38180177.438190472372.56180952763009
39193206.247937478342-13.2479374783417
40181185.960743337438-4.96074333743809
41183184.798362273963-1.79836227396325
42218195.68439550542022.3156044945797
43230227.4983977187992.50160228120123
44242229.00477178943912.995228210561
45209215.630984242776-6.63098424277621
46191186.2865973703104.71340262969036
47172166.0376427611405.96235723886033
48194192.8425176702161.15748232978419
49196198.309951792614-2.30995179261362
50196206.984490574542-10.9844905745424
51236224.19296559617511.8070344038246
52235214.06542383627120.9345761637291
53229222.6752396896246.32476031037604
54243256.737994926100-13.7379949260995
55264268.358000825093-4.35800082509263
56272275.595138563011-3.59513856301129
57237240.950054518830-3.95005451882952
58211216.418708751084-5.41870875108356
59180191.302349202140-11.3023492021397
60201212.165209086813-11.1652090868132
61204211.884606924302-7.88460692430232
62188213.278507450624-25.2785074506242
63235241.844292266099-6.84429226609927
64227231.126648633712-4.12664863371231
65234222.84757726495611.1524227350442
66264244.40358955613319.5964104438671
67302270.92543012229331.0745698777072
68293288.463281003994.53671899600994
69259253.3255645210345.67443547896596
70229228.4572337108590.542766289141412
71203198.7667231438064.23327685619387
72229226.3253681439902.67463185601025
73242232.5076924006789.49230759932206
74233226.1554874965556.84451250344512
75267284.984109809782-17.9841098097817
76269271.954044414959-2.95404441495936
77270274.414349879443-4.41434987944342
78315301.11087591275813.8891240872424
79364337.48148021720826.5185197827919
80347335.98553316204211.0144668379583
81312297.83666607185614.1633339281443
82274267.3004454650886.69955453491184
83237236.992543135440.00745686455996974
84278266.90068916338611.0993108366135
85284281.6339259082812.36607409171881
86277269.9672458906017.03275410939943
87317320.174999871829-3.17499987182867
88313321.276899149144-8.27689914914367
89318322.045741325616-4.04574132561623
90374367.9511784742486.04882152575198
91413417.203225397332-4.20322539733195
92405394.60624771342310.3937522865770
93355352.306655336872.69334466312978
94306308.449808881235-2.44980888123547
95271266.6963378015154.30366219848491
96306309.394161372705-3.39416137270507
97315315.103179025908-0.103179025908162
98301304.405357956351-3.40535795635083
99356349.0582018178746.94179818212643
100348349.292551021806-1.29255102180582
101355355.073176743192-0.0731767431923913
102422414.3609222660917.6390777339086
103465462.0911466439312.90885335606902
104467448.97970596848618.0202940315142
105404397.6350103800216.3649896199791
106347345.4509781177981.54902188220177
107305304.2430386145030.756961385497391
108336345.615242815445-9.61524281544519
109340352.616294292299-12.6162942922995
110318334.810889595368-16.8108895953683
111362386.941371143576-24.941371143576
112348372.19085448139-24.1908544813901
113363372.090123850004-9.09012385000392
114435435.498537075613-0.498537075613456
115491478.41838352541612.5816164745842
116505476.29773642868728.7022635713128
117404417.219138602199-13.2191386021993
118359354.4369064406444.56309355935639
119310311.87647452998-1.87647452998021
120337345.975126181846-8.97512618184646
121360350.6430688312569.35693116874421
122342334.9945865126547.00541348734612
123406390.68587883978415.3141211602163
124396386.4988634128129.50113658718794
125420407.13073966459212.8692603354077
126472491.604975308879-19.6049753088789
127548543.7800223198734.21997768012739
128559549.9352939995999.06470600040075
129463449.63876988832213.3612301116776
130407399.9130272672637.0869727327372
131362348.39721511660913.6027848833913
132405386.65209391144218.3479060885584
133417413.9167645634643.08323543653609
134391392.451160571742-1.45116057174164
135419460.1705013673-41.1705013673003
136461435.42179027207925.5782097279213
137472465.0951127316986.90488726830233
138535534.3527108703320.647289129667797
139622616.7353556376025.26464436239837
140606627.551048082547-21.5510480825469
141508510.133147348626-2.13314734862615
142461446.16280294352714.8371970564727
143390395.452817646612-5.45281764661166
144432434.572462781261-2.57246278126127

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 115 & 111.081808708867 & 3.91819129113328 \tabularnewline
14 & 126 & 122.331452141086 & 3.66854785891434 \tabularnewline
15 & 141 & 137.439012569900 & 3.56098743009966 \tabularnewline
16 & 135 & 132.323380345381 & 2.67661965461949 \tabularnewline
17 & 125 & 123.479680462729 & 1.52031953727142 \tabularnewline
18 & 149 & 147.667288314870 & 1.33271168512962 \tabularnewline
19 & 170 & 162.443243712473 & 7.55675628752724 \tabularnewline
20 & 170 & 165.529589613321 & 4.47041038667871 \tabularnewline
21 & 158 & 153.88771229425 & 4.11228770575008 \tabularnewline
22 & 133 & 136.318641448733 & -3.31864144873339 \tabularnewline
23 & 114 & 119.090610790114 & -5.09061079011435 \tabularnewline
24 & 140 & 133.988719950498 & 6.01128004950164 \tabularnewline
25 & 145 & 134.830368289919 & 10.1696317100805 \tabularnewline
26 & 150 & 149.705143441365 & 0.294856558634905 \tabularnewline
27 & 178 & 166.540825999660 & 11.4591740003402 \tabularnewline
28 & 163 & 161.749728378189 & 1.25027162181149 \tabularnewline
29 & 172 & 149.757507993041 & 22.2424920069588 \tabularnewline
30 & 178 & 185.647723596323 & -7.64772359632292 \tabularnewline
31 & 199 & 206.262153619027 & -7.26215361902663 \tabularnewline
32 & 199 & 203.180886325985 & -4.18088632598545 \tabularnewline
33 & 184 & 186.419252204207 & -2.41925220420688 \tabularnewline
34 & 162 & 158.147799136752 & 3.85220086324750 \tabularnewline
35 & 146 & 138.368728198327 & 7.63127180167297 \tabularnewline
36 & 166 & 169.141340932275 & -3.14134093227528 \tabularnewline
37 & 171 & 170.360941535351 & 0.639058464649423 \tabularnewline
38 & 180 & 177.43819047237 & 2.56180952763009 \tabularnewline
39 & 193 & 206.247937478342 & -13.2479374783417 \tabularnewline
40 & 181 & 185.960743337438 & -4.96074333743809 \tabularnewline
41 & 183 & 184.798362273963 & -1.79836227396325 \tabularnewline
42 & 218 & 195.684395505420 & 22.3156044945797 \tabularnewline
43 & 230 & 227.498397718799 & 2.50160228120123 \tabularnewline
44 & 242 & 229.004771789439 & 12.995228210561 \tabularnewline
45 & 209 & 215.630984242776 & -6.63098424277621 \tabularnewline
46 & 191 & 186.286597370310 & 4.71340262969036 \tabularnewline
47 & 172 & 166.037642761140 & 5.96235723886033 \tabularnewline
48 & 194 & 192.842517670216 & 1.15748232978419 \tabularnewline
49 & 196 & 198.309951792614 & -2.30995179261362 \tabularnewline
50 & 196 & 206.984490574542 & -10.9844905745424 \tabularnewline
51 & 236 & 224.192965596175 & 11.8070344038246 \tabularnewline
52 & 235 & 214.065423836271 & 20.9345761637291 \tabularnewline
53 & 229 & 222.675239689624 & 6.32476031037604 \tabularnewline
54 & 243 & 256.737994926100 & -13.7379949260995 \tabularnewline
55 & 264 & 268.358000825093 & -4.35800082509263 \tabularnewline
56 & 272 & 275.595138563011 & -3.59513856301129 \tabularnewline
57 & 237 & 240.950054518830 & -3.95005451882952 \tabularnewline
58 & 211 & 216.418708751084 & -5.41870875108356 \tabularnewline
59 & 180 & 191.302349202140 & -11.3023492021397 \tabularnewline
60 & 201 & 212.165209086813 & -11.1652090868132 \tabularnewline
61 & 204 & 211.884606924302 & -7.88460692430232 \tabularnewline
62 & 188 & 213.278507450624 & -25.2785074506242 \tabularnewline
63 & 235 & 241.844292266099 & -6.84429226609927 \tabularnewline
64 & 227 & 231.126648633712 & -4.12664863371231 \tabularnewline
65 & 234 & 222.847577264956 & 11.1524227350442 \tabularnewline
66 & 264 & 244.403589556133 & 19.5964104438671 \tabularnewline
67 & 302 & 270.925430122293 & 31.0745698777072 \tabularnewline
68 & 293 & 288.46328100399 & 4.53671899600994 \tabularnewline
69 & 259 & 253.325564521034 & 5.67443547896596 \tabularnewline
70 & 229 & 228.457233710859 & 0.542766289141412 \tabularnewline
71 & 203 & 198.766723143806 & 4.23327685619387 \tabularnewline
72 & 229 & 226.325368143990 & 2.67463185601025 \tabularnewline
73 & 242 & 232.507692400678 & 9.49230759932206 \tabularnewline
74 & 233 & 226.155487496555 & 6.84451250344512 \tabularnewline
75 & 267 & 284.984109809782 & -17.9841098097817 \tabularnewline
76 & 269 & 271.954044414959 & -2.95404441495936 \tabularnewline
77 & 270 & 274.414349879443 & -4.41434987944342 \tabularnewline
78 & 315 & 301.110875912758 & 13.8891240872424 \tabularnewline
79 & 364 & 337.481480217208 & 26.5185197827919 \tabularnewline
80 & 347 & 335.985533162042 & 11.0144668379583 \tabularnewline
81 & 312 & 297.836666071856 & 14.1633339281443 \tabularnewline
82 & 274 & 267.300445465088 & 6.69955453491184 \tabularnewline
83 & 237 & 236.99254313544 & 0.00745686455996974 \tabularnewline
84 & 278 & 266.900689163386 & 11.0993108366135 \tabularnewline
85 & 284 & 281.633925908281 & 2.36607409171881 \tabularnewline
86 & 277 & 269.967245890601 & 7.03275410939943 \tabularnewline
87 & 317 & 320.174999871829 & -3.17499987182867 \tabularnewline
88 & 313 & 321.276899149144 & -8.27689914914367 \tabularnewline
89 & 318 & 322.045741325616 & -4.04574132561623 \tabularnewline
90 & 374 & 367.951178474248 & 6.04882152575198 \tabularnewline
91 & 413 & 417.203225397332 & -4.20322539733195 \tabularnewline
92 & 405 & 394.606247713423 & 10.3937522865770 \tabularnewline
93 & 355 & 352.30665533687 & 2.69334466312978 \tabularnewline
94 & 306 & 308.449808881235 & -2.44980888123547 \tabularnewline
95 & 271 & 266.696337801515 & 4.30366219848491 \tabularnewline
96 & 306 & 309.394161372705 & -3.39416137270507 \tabularnewline
97 & 315 & 315.103179025908 & -0.103179025908162 \tabularnewline
98 & 301 & 304.405357956351 & -3.40535795635083 \tabularnewline
99 & 356 & 349.058201817874 & 6.94179818212643 \tabularnewline
100 & 348 & 349.292551021806 & -1.29255102180582 \tabularnewline
101 & 355 & 355.073176743192 & -0.0731767431923913 \tabularnewline
102 & 422 & 414.360922266091 & 7.6390777339086 \tabularnewline
103 & 465 & 462.091146643931 & 2.90885335606902 \tabularnewline
104 & 467 & 448.979705968486 & 18.0202940315142 \tabularnewline
105 & 404 & 397.635010380021 & 6.3649896199791 \tabularnewline
106 & 347 & 345.450978117798 & 1.54902188220177 \tabularnewline
107 & 305 & 304.243038614503 & 0.756961385497391 \tabularnewline
108 & 336 & 345.615242815445 & -9.61524281544519 \tabularnewline
109 & 340 & 352.616294292299 & -12.6162942922995 \tabularnewline
110 & 318 & 334.810889595368 & -16.8108895953683 \tabularnewline
111 & 362 & 386.941371143576 & -24.941371143576 \tabularnewline
112 & 348 & 372.19085448139 & -24.1908544813901 \tabularnewline
113 & 363 & 372.090123850004 & -9.09012385000392 \tabularnewline
114 & 435 & 435.498537075613 & -0.498537075613456 \tabularnewline
115 & 491 & 478.418383525416 & 12.5816164745842 \tabularnewline
116 & 505 & 476.297736428687 & 28.7022635713128 \tabularnewline
117 & 404 & 417.219138602199 & -13.2191386021993 \tabularnewline
118 & 359 & 354.436906440644 & 4.56309355935639 \tabularnewline
119 & 310 & 311.87647452998 & -1.87647452998021 \tabularnewline
120 & 337 & 345.975126181846 & -8.97512618184646 \tabularnewline
121 & 360 & 350.643068831256 & 9.35693116874421 \tabularnewline
122 & 342 & 334.994586512654 & 7.00541348734612 \tabularnewline
123 & 406 & 390.685878839784 & 15.3141211602163 \tabularnewline
124 & 396 & 386.498863412812 & 9.50113658718794 \tabularnewline
125 & 420 & 407.130739664592 & 12.8692603354077 \tabularnewline
126 & 472 & 491.604975308879 & -19.6049753088789 \tabularnewline
127 & 548 & 543.780022319873 & 4.21997768012739 \tabularnewline
128 & 559 & 549.935293999599 & 9.06470600040075 \tabularnewline
129 & 463 & 449.638769888322 & 13.3612301116776 \tabularnewline
130 & 407 & 399.913027267263 & 7.0869727327372 \tabularnewline
131 & 362 & 348.397215116609 & 13.6027848833913 \tabularnewline
132 & 405 & 386.652093911442 & 18.3479060885584 \tabularnewline
133 & 417 & 413.916764563464 & 3.08323543653609 \tabularnewline
134 & 391 & 392.451160571742 & -1.45116057174164 \tabularnewline
135 & 419 & 460.1705013673 & -41.1705013673003 \tabularnewline
136 & 461 & 435.421790272079 & 25.5782097279213 \tabularnewline
137 & 472 & 465.095112731698 & 6.90488726830233 \tabularnewline
138 & 535 & 534.352710870332 & 0.647289129667797 \tabularnewline
139 & 622 & 616.735355637602 & 5.26464436239837 \tabularnewline
140 & 606 & 627.551048082547 & -21.5510480825469 \tabularnewline
141 & 508 & 510.133147348626 & -2.13314734862615 \tabularnewline
142 & 461 & 446.162802943527 & 14.8371970564727 \tabularnewline
143 & 390 & 395.452817646612 & -5.45281764661166 \tabularnewline
144 & 432 & 434.572462781261 & -2.57246278126127 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74584&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]122.331452141086[/C][C]3.66854785891434[/C][/ROW]
[ROW][C]15[/C][C]141[/C][C]137.439012569900[/C][C]3.56098743009966[/C][/ROW]
[ROW][C]16[/C][C]135[/C][C]132.323380345381[/C][C]2.67661965461949[/C][/ROW]
[ROW][C]17[/C][C]125[/C][C]123.479680462729[/C][C]1.52031953727142[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]147.667288314870[/C][C]1.33271168512962[/C][/ROW]
[ROW][C]19[/C][C]170[/C][C]162.443243712473[/C][C]7.55675628752724[/C][/ROW]
[ROW][C]20[/C][C]170[/C][C]165.529589613321[/C][C]4.47041038667871[/C][/ROW]
[ROW][C]21[/C][C]158[/C][C]153.88771229425[/C][C]4.11228770575008[/C][/ROW]
[ROW][C]22[/C][C]133[/C][C]136.318641448733[/C][C]-3.31864144873339[/C][/ROW]
[ROW][C]23[/C][C]114[/C][C]119.090610790114[/C][C]-5.09061079011435[/C][/ROW]
[ROW][C]24[/C][C]140[/C][C]133.988719950498[/C][C]6.01128004950164[/C][/ROW]
[ROW][C]25[/C][C]145[/C][C]134.830368289919[/C][C]10.1696317100805[/C][/ROW]
[ROW][C]26[/C][C]150[/C][C]149.705143441365[/C][C]0.294856558634905[/C][/ROW]
[ROW][C]27[/C][C]178[/C][C]166.540825999660[/C][C]11.4591740003402[/C][/ROW]
[ROW][C]28[/C][C]163[/C][C]161.749728378189[/C][C]1.25027162181149[/C][/ROW]
[ROW][C]29[/C][C]172[/C][C]149.757507993041[/C][C]22.2424920069588[/C][/ROW]
[ROW][C]30[/C][C]178[/C][C]185.647723596323[/C][C]-7.64772359632292[/C][/ROW]
[ROW][C]31[/C][C]199[/C][C]206.262153619027[/C][C]-7.26215361902663[/C][/ROW]
[ROW][C]32[/C][C]199[/C][C]203.180886325985[/C][C]-4.18088632598545[/C][/ROW]
[ROW][C]33[/C][C]184[/C][C]186.419252204207[/C][C]-2.41925220420688[/C][/ROW]
[ROW][C]34[/C][C]162[/C][C]158.147799136752[/C][C]3.85220086324750[/C][/ROW]
[ROW][C]35[/C][C]146[/C][C]138.368728198327[/C][C]7.63127180167297[/C][/ROW]
[ROW][C]36[/C][C]166[/C][C]169.141340932275[/C][C]-3.14134093227528[/C][/ROW]
[ROW][C]37[/C][C]171[/C][C]170.360941535351[/C][C]0.639058464649423[/C][/ROW]
[ROW][C]38[/C][C]180[/C][C]177.43819047237[/C][C]2.56180952763009[/C][/ROW]
[ROW][C]39[/C][C]193[/C][C]206.247937478342[/C][C]-13.2479374783417[/C][/ROW]
[ROW][C]40[/C][C]181[/C][C]185.960743337438[/C][C]-4.96074333743809[/C][/ROW]
[ROW][C]41[/C][C]183[/C][C]184.798362273963[/C][C]-1.79836227396325[/C][/ROW]
[ROW][C]42[/C][C]218[/C][C]195.684395505420[/C][C]22.3156044945797[/C][/ROW]
[ROW][C]43[/C][C]230[/C][C]227.498397718799[/C][C]2.50160228120123[/C][/ROW]
[ROW][C]44[/C][C]242[/C][C]229.004771789439[/C][C]12.995228210561[/C][/ROW]
[ROW][C]45[/C][C]209[/C][C]215.630984242776[/C][C]-6.63098424277621[/C][/ROW]
[ROW][C]46[/C][C]191[/C][C]186.286597370310[/C][C]4.71340262969036[/C][/ROW]
[ROW][C]47[/C][C]172[/C][C]166.037642761140[/C][C]5.96235723886033[/C][/ROW]
[ROW][C]48[/C][C]194[/C][C]192.842517670216[/C][C]1.15748232978419[/C][/ROW]
[ROW][C]49[/C][C]196[/C][C]198.309951792614[/C][C]-2.30995179261362[/C][/ROW]
[ROW][C]50[/C][C]196[/C][C]206.984490574542[/C][C]-10.9844905745424[/C][/ROW]
[ROW][C]51[/C][C]236[/C][C]224.192965596175[/C][C]11.8070344038246[/C][/ROW]
[ROW][C]52[/C][C]235[/C][C]214.065423836271[/C][C]20.9345761637291[/C][/ROW]
[ROW][C]53[/C][C]229[/C][C]222.675239689624[/C][C]6.32476031037604[/C][/ROW]
[ROW][C]54[/C][C]243[/C][C]256.737994926100[/C][C]-13.7379949260995[/C][/ROW]
[ROW][C]55[/C][C]264[/C][C]268.358000825093[/C][C]-4.35800082509263[/C][/ROW]
[ROW][C]56[/C][C]272[/C][C]275.595138563011[/C][C]-3.59513856301129[/C][/ROW]
[ROW][C]57[/C][C]237[/C][C]240.950054518830[/C][C]-3.95005451882952[/C][/ROW]
[ROW][C]58[/C][C]211[/C][C]216.418708751084[/C][C]-5.41870875108356[/C][/ROW]
[ROW][C]59[/C][C]180[/C][C]191.302349202140[/C][C]-11.3023492021397[/C][/ROW]
[ROW][C]60[/C][C]201[/C][C]212.165209086813[/C][C]-11.1652090868132[/C][/ROW]
[ROW][C]61[/C][C]204[/C][C]211.884606924302[/C][C]-7.88460692430232[/C][/ROW]
[ROW][C]62[/C][C]188[/C][C]213.278507450624[/C][C]-25.2785074506242[/C][/ROW]
[ROW][C]63[/C][C]235[/C][C]241.844292266099[/C][C]-6.84429226609927[/C][/ROW]
[ROW][C]64[/C][C]227[/C][C]231.126648633712[/C][C]-4.12664863371231[/C][/ROW]
[ROW][C]65[/C][C]234[/C][C]222.847577264956[/C][C]11.1524227350442[/C][/ROW]
[ROW][C]66[/C][C]264[/C][C]244.403589556133[/C][C]19.5964104438671[/C][/ROW]
[ROW][C]67[/C][C]302[/C][C]270.925430122293[/C][C]31.0745698777072[/C][/ROW]
[ROW][C]68[/C][C]293[/C][C]288.46328100399[/C][C]4.53671899600994[/C][/ROW]
[ROW][C]69[/C][C]259[/C][C]253.325564521034[/C][C]5.67443547896596[/C][/ROW]
[ROW][C]70[/C][C]229[/C][C]228.457233710859[/C][C]0.542766289141412[/C][/ROW]
[ROW][C]71[/C][C]203[/C][C]198.766723143806[/C][C]4.23327685619387[/C][/ROW]
[ROW][C]72[/C][C]229[/C][C]226.325368143990[/C][C]2.67463185601025[/C][/ROW]
[ROW][C]73[/C][C]242[/C][C]232.507692400678[/C][C]9.49230759932206[/C][/ROW]
[ROW][C]74[/C][C]233[/C][C]226.155487496555[/C][C]6.84451250344512[/C][/ROW]
[ROW][C]75[/C][C]267[/C][C]284.984109809782[/C][C]-17.9841098097817[/C][/ROW]
[ROW][C]76[/C][C]269[/C][C]271.954044414959[/C][C]-2.95404441495936[/C][/ROW]
[ROW][C]77[/C][C]270[/C][C]274.414349879443[/C][C]-4.41434987944342[/C][/ROW]
[ROW][C]78[/C][C]315[/C][C]301.110875912758[/C][C]13.8891240872424[/C][/ROW]
[ROW][C]79[/C][C]364[/C][C]337.481480217208[/C][C]26.5185197827919[/C][/ROW]
[ROW][C]80[/C][C]347[/C][C]335.985533162042[/C][C]11.0144668379583[/C][/ROW]
[ROW][C]81[/C][C]312[/C][C]297.836666071856[/C][C]14.1633339281443[/C][/ROW]
[ROW][C]82[/C][C]274[/C][C]267.300445465088[/C][C]6.69955453491184[/C][/ROW]
[ROW][C]83[/C][C]237[/C][C]236.99254313544[/C][C]0.00745686455996974[/C][/ROW]
[ROW][C]84[/C][C]278[/C][C]266.900689163386[/C][C]11.0993108366135[/C][/ROW]
[ROW][C]85[/C][C]284[/C][C]281.633925908281[/C][C]2.36607409171881[/C][/ROW]
[ROW][C]86[/C][C]277[/C][C]269.967245890601[/C][C]7.03275410939943[/C][/ROW]
[ROW][C]87[/C][C]317[/C][C]320.174999871829[/C][C]-3.17499987182867[/C][/ROW]
[ROW][C]88[/C][C]313[/C][C]321.276899149144[/C][C]-8.27689914914367[/C][/ROW]
[ROW][C]89[/C][C]318[/C][C]322.045741325616[/C][C]-4.04574132561623[/C][/ROW]
[ROW][C]90[/C][C]374[/C][C]367.951178474248[/C][C]6.04882152575198[/C][/ROW]
[ROW][C]91[/C][C]413[/C][C]417.203225397332[/C][C]-4.20322539733195[/C][/ROW]
[ROW][C]92[/C][C]405[/C][C]394.606247713423[/C][C]10.3937522865770[/C][/ROW]
[ROW][C]93[/C][C]355[/C][C]352.30665533687[/C][C]2.69334466312978[/C][/ROW]
[ROW][C]94[/C][C]306[/C][C]308.449808881235[/C][C]-2.44980888123547[/C][/ROW]
[ROW][C]95[/C][C]271[/C][C]266.696337801515[/C][C]4.30366219848491[/C][/ROW]
[ROW][C]96[/C][C]306[/C][C]309.394161372705[/C][C]-3.39416137270507[/C][/ROW]
[ROW][C]97[/C][C]315[/C][C]315.103179025908[/C][C]-0.103179025908162[/C][/ROW]
[ROW][C]98[/C][C]301[/C][C]304.405357956351[/C][C]-3.40535795635083[/C][/ROW]
[ROW][C]99[/C][C]356[/C][C]349.058201817874[/C][C]6.94179818212643[/C][/ROW]
[ROW][C]100[/C][C]348[/C][C]349.292551021806[/C][C]-1.29255102180582[/C][/ROW]
[ROW][C]101[/C][C]355[/C][C]355.073176743192[/C][C]-0.0731767431923913[/C][/ROW]
[ROW][C]102[/C][C]422[/C][C]414.360922266091[/C][C]7.6390777339086[/C][/ROW]
[ROW][C]103[/C][C]465[/C][C]462.091146643931[/C][C]2.90885335606902[/C][/ROW]
[ROW][C]104[/C][C]467[/C][C]448.979705968486[/C][C]18.0202940315142[/C][/ROW]
[ROW][C]105[/C][C]404[/C][C]397.635010380021[/C][C]6.3649896199791[/C][/ROW]
[ROW][C]106[/C][C]347[/C][C]345.450978117798[/C][C]1.54902188220177[/C][/ROW]
[ROW][C]107[/C][C]305[/C][C]304.243038614503[/C][C]0.756961385497391[/C][/ROW]
[ROW][C]108[/C][C]336[/C][C]345.615242815445[/C][C]-9.61524281544519[/C][/ROW]
[ROW][C]109[/C][C]340[/C][C]352.616294292299[/C][C]-12.6162942922995[/C][/ROW]
[ROW][C]110[/C][C]318[/C][C]334.810889595368[/C][C]-16.8108895953683[/C][/ROW]
[ROW][C]111[/C][C]362[/C][C]386.941371143576[/C][C]-24.941371143576[/C][/ROW]
[ROW][C]112[/C][C]348[/C][C]372.19085448139[/C][C]-24.1908544813901[/C][/ROW]
[ROW][C]113[/C][C]363[/C][C]372.090123850004[/C][C]-9.09012385000392[/C][/ROW]
[ROW][C]114[/C][C]435[/C][C]435.498537075613[/C][C]-0.498537075613456[/C][/ROW]
[ROW][C]115[/C][C]491[/C][C]478.418383525416[/C][C]12.5816164745842[/C][/ROW]
[ROW][C]116[/C][C]505[/C][C]476.297736428687[/C][C]28.7022635713128[/C][/ROW]
[ROW][C]117[/C][C]404[/C][C]417.219138602199[/C][C]-13.2191386021993[/C][/ROW]
[ROW][C]118[/C][C]359[/C][C]354.436906440644[/C][C]4.56309355935639[/C][/ROW]
[ROW][C]119[/C][C]310[/C][C]311.87647452998[/C][C]-1.87647452998021[/C][/ROW]
[ROW][C]120[/C][C]337[/C][C]345.975126181846[/C][C]-8.97512618184646[/C][/ROW]
[ROW][C]121[/C][C]360[/C][C]350.643068831256[/C][C]9.35693116874421[/C][/ROW]
[ROW][C]122[/C][C]342[/C][C]334.994586512654[/C][C]7.00541348734612[/C][/ROW]
[ROW][C]123[/C][C]406[/C][C]390.685878839784[/C][C]15.3141211602163[/C][/ROW]
[ROW][C]124[/C][C]396[/C][C]386.498863412812[/C][C]9.50113658718794[/C][/ROW]
[ROW][C]125[/C][C]420[/C][C]407.130739664592[/C][C]12.8692603354077[/C][/ROW]
[ROW][C]126[/C][C]472[/C][C]491.604975308879[/C][C]-19.6049753088789[/C][/ROW]
[ROW][C]127[/C][C]548[/C][C]543.780022319873[/C][C]4.21997768012739[/C][/ROW]
[ROW][C]128[/C][C]559[/C][C]549.935293999599[/C][C]9.06470600040075[/C][/ROW]
[ROW][C]129[/C][C]463[/C][C]449.638769888322[/C][C]13.3612301116776[/C][/ROW]
[ROW][C]130[/C][C]407[/C][C]399.913027267263[/C][C]7.0869727327372[/C][/ROW]
[ROW][C]131[/C][C]362[/C][C]348.397215116609[/C][C]13.6027848833913[/C][/ROW]
[ROW][C]132[/C][C]405[/C][C]386.652093911442[/C][C]18.3479060885584[/C][/ROW]
[ROW][C]133[/C][C]417[/C][C]413.916764563464[/C][C]3.08323543653609[/C][/ROW]
[ROW][C]134[/C][C]391[/C][C]392.451160571742[/C][C]-1.45116057174164[/C][/ROW]
[ROW][C]135[/C][C]419[/C][C]460.1705013673[/C][C]-41.1705013673003[/C][/ROW]
[ROW][C]136[/C][C]461[/C][C]435.421790272079[/C][C]25.5782097279213[/C][/ROW]
[ROW][C]137[/C][C]472[/C][C]465.095112731698[/C][C]6.90488726830233[/C][/ROW]
[ROW][C]138[/C][C]535[/C][C]534.352710870332[/C][C]0.647289129667797[/C][/ROW]
[ROW][C]139[/C][C]622[/C][C]616.735355637602[/C][C]5.26464436239837[/C][/ROW]
[ROW][C]140[/C][C]606[/C][C]627.551048082547[/C][C]-21.5510480825469[/C][/ROW]
[ROW][C]141[/C][C]508[/C][C]510.133147348626[/C][C]-2.13314734862615[/C][/ROW]
[ROW][C]142[/C][C]461[/C][C]446.162802943527[/C][C]14.8371970564727[/C][/ROW]
[ROW][C]143[/C][C]390[/C][C]395.452817646612[/C][C]-5.45281764661166[/C][/ROW]
[ROW][C]144[/C][C]432[/C][C]434.572462781261[/C][C]-2.57246278126127[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74584&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74584&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
14126122.3314521410863.66854785891434
15141137.4390125699003.56098743009966
16135132.3233803453812.67661965461949
17125123.4796804627291.52031953727142
18149147.6672883148701.33271168512962
19170162.4432437124737.55675628752724
20170165.5295896133214.47041038667871
21158153.887712294254.11228770575008
22133136.318641448733-3.31864144873339
23114119.090610790114-5.09061079011435
24140133.9887199504986.01128004950164
25145134.83036828991910.1696317100805
26150149.7051434413650.294856558634905
27178166.54082599966011.4591740003402
28163161.7497283781891.25027162181149
29172149.75750799304122.2424920069588
30178185.647723596323-7.64772359632292
31199206.262153619027-7.26215361902663
32199203.180886325985-4.18088632598545
33184186.419252204207-2.41925220420688
34162158.1477991367523.85220086324750
35146138.3687281983277.63127180167297
36166169.141340932275-3.14134093227528
37171170.3609415353510.639058464649423
38180177.438190472372.56180952763009
39193206.247937478342-13.2479374783417
40181185.960743337438-4.96074333743809
41183184.798362273963-1.79836227396325
42218195.68439550542022.3156044945797
43230227.4983977187992.50160228120123
44242229.00477178943912.995228210561
45209215.630984242776-6.63098424277621
46191186.2865973703104.71340262969036
47172166.0376427611405.96235723886033
48194192.8425176702161.15748232978419
49196198.309951792614-2.30995179261362
50196206.984490574542-10.9844905745424
51236224.19296559617511.8070344038246
52235214.06542383627120.9345761637291
53229222.6752396896246.32476031037604
54243256.737994926100-13.7379949260995
55264268.358000825093-4.35800082509263
56272275.595138563011-3.59513856301129
57237240.950054518830-3.95005451882952
58211216.418708751084-5.41870875108356
59180191.302349202140-11.3023492021397
60201212.165209086813-11.1652090868132
61204211.884606924302-7.88460692430232
62188213.278507450624-25.2785074506242
63235241.844292266099-6.84429226609927
64227231.126648633712-4.12664863371231
65234222.84757726495611.1524227350442
66264244.40358955613319.5964104438671
67302270.92543012229331.0745698777072
68293288.463281003994.53671899600994
69259253.3255645210345.67443547896596
70229228.4572337108590.542766289141412
71203198.7667231438064.23327685619387
72229226.3253681439902.67463185601025
73242232.5076924006789.49230759932206
74233226.1554874965556.84451250344512
75267284.984109809782-17.9841098097817
76269271.954044414959-2.95404441495936
77270274.414349879443-4.41434987944342
78315301.11087591275813.8891240872424
79364337.48148021720826.5185197827919
80347335.98553316204211.0144668379583
81312297.83666607185614.1633339281443
82274267.3004454650886.69955453491184
83237236.992543135440.00745686455996974
84278266.90068916338611.0993108366135
85284281.6339259082812.36607409171881
86277269.9672458906017.03275410939943
87317320.174999871829-3.17499987182867
88313321.276899149144-8.27689914914367
89318322.045741325616-4.04574132561623
90374367.9511784742486.04882152575198
91413417.203225397332-4.20322539733195
92405394.60624771342310.3937522865770
93355352.306655336872.69334466312978
94306308.449808881235-2.44980888123547
95271266.6963378015154.30366219848491
96306309.394161372705-3.39416137270507
97315315.103179025908-0.103179025908162
98301304.405357956351-3.40535795635083
99356349.0582018178746.94179818212643
100348349.292551021806-1.29255102180582
101355355.073176743192-0.0731767431923913
102422414.3609222660917.6390777339086
103465462.0911466439312.90885335606902
104467448.97970596848618.0202940315142
105404397.6350103800216.3649896199791
106347345.4509781177981.54902188220177
107305304.2430386145030.756961385497391
108336345.615242815445-9.61524281544519
109340352.616294292299-12.6162942922995
110318334.810889595368-16.8108895953683
111362386.941371143576-24.941371143576
112348372.19085448139-24.1908544813901
113363372.090123850004-9.09012385000392
114435435.498537075613-0.498537075613456
115491478.41838352541612.5816164745842
116505476.29773642868728.7022635713128
117404417.219138602199-13.2191386021993
118359354.4369064406444.56309355935639
119310311.87647452998-1.87647452998021
120337345.975126181846-8.97512618184646
121360350.6430688312569.35693116874421
122342334.9945865126547.00541348734612
123406390.68587883978415.3141211602163
124396386.4988634128129.50113658718794
125420407.13073966459212.8692603354077
126472491.604975308879-19.6049753088789
127548543.7800223198734.21997768012739
128559549.9352939995999.06470600040075
129463449.63876988832213.3612301116776
130407399.9130272672637.0869727327372
131362348.39721511660913.6027848833913
132405386.65209391144218.3479060885584
133417413.9167645634643.08323543653609
134391392.451160571742-1.45116057174164
135419460.1705013673-41.1705013673003
136461435.42179027207925.5782097279213
137472465.0951127316986.90488726830233
138535534.3527108703320.647289129667797
139622616.7353556376025.26464436239837
140606627.551048082547-21.5510480825469
141508510.133147348626-2.13314734862615
142461446.16280294352714.8371970564727
143390395.452817646612-5.45281764661166
144432434.572462781261-2.57246278126127







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
145447.055931344916427.306133026716466.805729663116
146419.712279855699399.132585244972440.291974466426
147464.867131095767442.963034360731486.771227830803
148496.083937621018472.832900505232519.334974736803
149507.532637500571483.137513327878531.927761673263
150575.450895961051548.708256350216602.193535571887
151666.592293420418636.628829419529696.555757421307
152657.913718340472627.182084720006688.645351960938
153550.308766449002521.639795163271578.977737734733
154492.985309235296465.015309913896520.955308556697
155420.207279555462393.4687785372446.945780573723
156465.634500936807443.300414442854487.96858743076

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
145 & 447.055931344916 & 427.306133026716 & 466.805729663116 \tabularnewline
146 & 419.712279855699 & 399.132585244972 & 440.291974466426 \tabularnewline
147 & 464.867131095767 & 442.963034360731 & 486.771227830803 \tabularnewline
148 & 496.083937621018 & 472.832900505232 & 519.334974736803 \tabularnewline
149 & 507.532637500571 & 483.137513327878 & 531.927761673263 \tabularnewline
150 & 575.450895961051 & 548.708256350216 & 602.193535571887 \tabularnewline
151 & 666.592293420418 & 636.628829419529 & 696.555757421307 \tabularnewline
152 & 657.913718340472 & 627.182084720006 & 688.645351960938 \tabularnewline
153 & 550.308766449002 & 521.639795163271 & 578.977737734733 \tabularnewline
154 & 492.985309235296 & 465.015309913896 & 520.955308556697 \tabularnewline
155 & 420.207279555462 & 393.4687785372 & 446.945780573723 \tabularnewline
156 & 465.634500936807 & 443.300414442854 & 487.96858743076 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74584&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]447.055931344916[/C][C]427.306133026716[/C][C]466.805729663116[/C][/ROW]
[ROW][C]146[/C][C]419.712279855699[/C][C]399.132585244972[/C][C]440.291974466426[/C][/ROW]
[ROW][C]147[/C][C]464.867131095767[/C][C]442.963034360731[/C][C]486.771227830803[/C][/ROW]
[ROW][C]148[/C][C]496.083937621018[/C][C]472.832900505232[/C][C]519.334974736803[/C][/ROW]
[ROW][C]149[/C][C]507.532637500571[/C][C]483.137513327878[/C][C]531.927761673263[/C][/ROW]
[ROW][C]150[/C][C]575.450895961051[/C][C]548.708256350216[/C][C]602.193535571887[/C][/ROW]
[ROW][C]151[/C][C]666.592293420418[/C][C]636.628829419529[/C][C]696.555757421307[/C][/ROW]
[ROW][C]152[/C][C]657.913718340472[/C][C]627.182084720006[/C][C]688.645351960938[/C][/ROW]
[ROW][C]153[/C][C]550.308766449002[/C][C]521.639795163271[/C][C]578.977737734733[/C][/ROW]
[ROW][C]154[/C][C]492.985309235296[/C][C]465.015309913896[/C][C]520.955308556697[/C][/ROW]
[ROW][C]155[/C][C]420.207279555462[/C][C]393.4687785372[/C][C]446.945780573723[/C][/ROW]
[ROW][C]156[/C][C]465.634500936807[/C][C]443.300414442854[/C][C]487.96858743076[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74584&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74584&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
145447.055931344916427.306133026716466.805729663116
146419.712279855699399.132585244972440.291974466426
147464.867131095767442.963034360731486.771227830803
148496.083937621018472.832900505232519.334974736803
149507.532637500571483.137513327878531.927761673263
150575.450895961051548.708256350216602.193535571887
151666.592293420418636.628829419529696.555757421307
152657.913718340472627.182084720006688.645351960938
153550.308766449002521.639795163271578.977737734733
154492.985309235296465.015309913896520.955308556697
155420.207279555462393.4687785372446.945780573723
156465.634500936807443.300414442854487.96858743076



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=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')