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 03:22:02 -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/t1237108960yx67zeimlq5z3nv.htm/, Retrieved Thu, 06 Oct 2022 23:13:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=39074, Retrieved Thu, 06 Oct 2022 23:13:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [test] [2009-03-15 09:22:02] [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 time4 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=39074&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=39074&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=39074&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 time4 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.275592931492623
beta0.0326927269946692
gamma0.8707309723751

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.275592931492623 \tabularnewline
beta & 0.0326927269946692 \tabularnewline
gamma & 0.8707309723751 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=39074&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]0.275592931492623[/C][/ROW]
[ROW][C]beta[/C][C]0.0326927269946692[/C][/ROW]
[ROW][C]gamma[/C][C]0.8707309723751[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=39074&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=39074&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
alpha0.275592931492623
beta0.0326927269946692
gamma0.8707309723751







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
13115111.0818087088673.91819129113324
14126122.3314538747443.66854612525638
15141137.4390153761593.56098462384080
16135132.3233832202962.67661677970432
17125123.4796828181491.52031718185103
18149147.667290233191.33270976680996
19170162.4432448019017.55675519809901
20170165.5295923942204.47040760577966
21158153.8877144649354.11228553506479
22133136.318642929538-3.31864292953784
23114119.090609201624-5.09060920162416
24140133.9887149606766.01128503932438
25145134.83037010966610.1696298903343
26150149.7051460153820.294853984617731
27178166.54082485992511.4591751400746
28163161.7497291200711.25027087992851
29172149.75750588289122.2424941171085
30178185.647729927994-7.64772992799351
31199206.262157804505-7.26215780450494
32199203.180877899401-4.18087789940057
33184186.419241979636-2.41924197963596
34162158.1477827595293.85221724047062
35146138.3687169810757.63128301892496
36166169.141347816420-3.14134781641982
37171170.3609458701950.63905412980543
38180177.4381780479142.56182195208552
39193206.247937689471-13.2479376894715
40181185.960724160929-4.960724160929
41183184.798364145757-1.79836414575698
42218195.68435973355122.3156402664486
43230227.4983879410412.50161205895935
44242229.0047696885212.9952303114799
45209215.630988418272-6.63098841827184
46191186.2865983510914.71340164890901
47172166.037643993565.96235600644013
48194192.8425095100681.15749048993163
49196198.309952885671-2.30995288567101
50196206.984486038046-10.9844860380460
51236224.19294089622011.8070591037796
52235214.06541785565920.9345821443411
53229222.6752524471096.32474755289078
54243256.738013695474-13.7380136954740
55264268.357973304408-4.35797330440784
56272275.595124348774-3.59512434877428
57237240.950022441517-3.95002244151664
58211216.418700337472-5.41870033747205
59180191.302342357417-11.3023423574168
60201212.165187519305-11.1651875193053
61204211.884585973221-7.88458597322119
62188213.278482377781-25.2784823777809
63235241.844280631672-6.84428063167161
64227231.126644343170-4.12664434317026
65234222.84756235743311.1524376425669
66264244.40357368198419.5964263180161
67302270.92543678752831.0745632124722
68293288.4633043826444.53669561735649
69259253.3255731144355.6744268855654
70229228.4572425854870.54275741451346
71203198.7667237110594.2332762889414
72229226.3253709300412.67462906995868
73242232.5076979211189.49230207888249
74233226.1554706830886.84452931691163
75267284.984135370835-17.9841353708349
76269271.954055875775-2.95405587577505
77270274.414364054018-4.41436405401839
78315301.11087543872113.8891245612785
79364337.48148399702426.5185160029757
80347335.98551213635611.0144878636443
81312297.83665553117314.1633444688269
82274267.3004377139316.69956228606935
83237236.9925415061970.00745849380322738
84278266.90068203576611.0993179642339
85284281.6339304999882.36606950001197
86277269.9672327497007.03276725030031
87317320.174971097687-3.17497109768715
88313321.27690151402-8.27690151402027
89318322.045741716529-4.04574171652934
90374367.9511939083776.04880609162313
91413417.203241898457-4.20324189845735
92405394.60621928242710.3937807175729
93355352.3066369649992.69336303500091
94306308.449783989775-2.44978398977509
95271266.6963131926584.30368680734244
96306309.394151440325-3.39415144032546
97315315.103159698907-0.103159698907120
98301304.405343346895-3.40534334689534
99356349.058165450366.94183454964002
100348349.292530352989-1.29253035298922
101355355.073171023921-0.0731710239210202
102422414.3609383732997.63906162670128
103465462.0911562758372.90884372416303
104467448.97971785272418.0202821472757
105404397.6350099221246.36499007787643
106347345.4509662878911.54903371210901
107305304.2430311120750.756968887925495
108336345.615225431213-9.61522543121265
109340352.616275490587-12.6162754905871
110318334.810864863463-16.8108648634627
111362386.941342201635-24.9413422016352
112348372.190810667781-24.1908106677807
113363372.090086438255-9.09008643825496
114435435.49852015535-0.49852015535015
115491478.41837009478312.5816299052173
116505476.29775492898928.7022450710107
117404417.219149854211-13.2191498542109
118359354.4369029627564.56309703724406
119310311.876475452214-1.87647545221358
120337345.975115388033-8.97511538803315
121360350.6430581403129.3569418596877
122342334.9945802071057.0054197928946
123406390.68587542554415.3141245744561
124396386.4988658447999.50113415520133
125420407.13076227323512.8692377267649
126472491.605011219988-19.6050112199878
127548543.7800465587574.21995344124275
128559549.9353273917249.06467260827571
129463449.6387467094113.3612532905901
130407399.9130342117767.08696578822429
131362348.39721266746913.6027873325307
132405386.65208470331218.3479152966877
133417413.9167814674453.08321853255524
134391392.451160427231-1.45116042723129
135419460.170497130598-41.1704971305977
136461435.42175557133825.5782444286623
137472465.0951028577886.90489714221195
138535534.3526743898930.647325610106805
139622616.7353661862325.26463381376755
140606627.551069242213-21.5510692422125
141508510.133139879417-2.1331398794174
142461446.16279163776614.8372083622340
143390395.452817969958-5.45281796995783
144432434.572452493991-2.57245249399091

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 115 & 111.081808708867 & 3.91819129113324 \tabularnewline
14 & 126 & 122.331453874744 & 3.66854612525638 \tabularnewline
15 & 141 & 137.439015376159 & 3.56098462384080 \tabularnewline
16 & 135 & 132.323383220296 & 2.67661677970432 \tabularnewline
17 & 125 & 123.479682818149 & 1.52031718185103 \tabularnewline
18 & 149 & 147.66729023319 & 1.33270976680996 \tabularnewline
19 & 170 & 162.443244801901 & 7.55675519809901 \tabularnewline
20 & 170 & 165.529592394220 & 4.47040760577966 \tabularnewline
21 & 158 & 153.887714464935 & 4.11228553506479 \tabularnewline
22 & 133 & 136.318642929538 & -3.31864292953784 \tabularnewline
23 & 114 & 119.090609201624 & -5.09060920162416 \tabularnewline
24 & 140 & 133.988714960676 & 6.01128503932438 \tabularnewline
25 & 145 & 134.830370109666 & 10.1696298903343 \tabularnewline
26 & 150 & 149.705146015382 & 0.294853984617731 \tabularnewline
27 & 178 & 166.540824859925 & 11.4591751400746 \tabularnewline
28 & 163 & 161.749729120071 & 1.25027087992851 \tabularnewline
29 & 172 & 149.757505882891 & 22.2424941171085 \tabularnewline
30 & 178 & 185.647729927994 & -7.64772992799351 \tabularnewline
31 & 199 & 206.262157804505 & -7.26215780450494 \tabularnewline
32 & 199 & 203.180877899401 & -4.18087789940057 \tabularnewline
33 & 184 & 186.419241979636 & -2.41924197963596 \tabularnewline
34 & 162 & 158.147782759529 & 3.85221724047062 \tabularnewline
35 & 146 & 138.368716981075 & 7.63128301892496 \tabularnewline
36 & 166 & 169.141347816420 & -3.14134781641982 \tabularnewline
37 & 171 & 170.360945870195 & 0.63905412980543 \tabularnewline
38 & 180 & 177.438178047914 & 2.56182195208552 \tabularnewline
39 & 193 & 206.247937689471 & -13.2479376894715 \tabularnewline
40 & 181 & 185.960724160929 & -4.960724160929 \tabularnewline
41 & 183 & 184.798364145757 & -1.79836414575698 \tabularnewline
42 & 218 & 195.684359733551 & 22.3156402664486 \tabularnewline
43 & 230 & 227.498387941041 & 2.50161205895935 \tabularnewline
44 & 242 & 229.00476968852 & 12.9952303114799 \tabularnewline
45 & 209 & 215.630988418272 & -6.63098841827184 \tabularnewline
46 & 191 & 186.286598351091 & 4.71340164890901 \tabularnewline
47 & 172 & 166.03764399356 & 5.96235600644013 \tabularnewline
48 & 194 & 192.842509510068 & 1.15749048993163 \tabularnewline
49 & 196 & 198.309952885671 & -2.30995288567101 \tabularnewline
50 & 196 & 206.984486038046 & -10.9844860380460 \tabularnewline
51 & 236 & 224.192940896220 & 11.8070591037796 \tabularnewline
52 & 235 & 214.065417855659 & 20.9345821443411 \tabularnewline
53 & 229 & 222.675252447109 & 6.32474755289078 \tabularnewline
54 & 243 & 256.738013695474 & -13.7380136954740 \tabularnewline
55 & 264 & 268.357973304408 & -4.35797330440784 \tabularnewline
56 & 272 & 275.595124348774 & -3.59512434877428 \tabularnewline
57 & 237 & 240.950022441517 & -3.95002244151664 \tabularnewline
58 & 211 & 216.418700337472 & -5.41870033747205 \tabularnewline
59 & 180 & 191.302342357417 & -11.3023423574168 \tabularnewline
60 & 201 & 212.165187519305 & -11.1651875193053 \tabularnewline
61 & 204 & 211.884585973221 & -7.88458597322119 \tabularnewline
62 & 188 & 213.278482377781 & -25.2784823777809 \tabularnewline
63 & 235 & 241.844280631672 & -6.84428063167161 \tabularnewline
64 & 227 & 231.126644343170 & -4.12664434317026 \tabularnewline
65 & 234 & 222.847562357433 & 11.1524376425669 \tabularnewline
66 & 264 & 244.403573681984 & 19.5964263180161 \tabularnewline
67 & 302 & 270.925436787528 & 31.0745632124722 \tabularnewline
68 & 293 & 288.463304382644 & 4.53669561735649 \tabularnewline
69 & 259 & 253.325573114435 & 5.6744268855654 \tabularnewline
70 & 229 & 228.457242585487 & 0.54275741451346 \tabularnewline
71 & 203 & 198.766723711059 & 4.2332762889414 \tabularnewline
72 & 229 & 226.325370930041 & 2.67462906995868 \tabularnewline
73 & 242 & 232.507697921118 & 9.49230207888249 \tabularnewline
74 & 233 & 226.155470683088 & 6.84452931691163 \tabularnewline
75 & 267 & 284.984135370835 & -17.9841353708349 \tabularnewline
76 & 269 & 271.954055875775 & -2.95405587577505 \tabularnewline
77 & 270 & 274.414364054018 & -4.41436405401839 \tabularnewline
78 & 315 & 301.110875438721 & 13.8891245612785 \tabularnewline
79 & 364 & 337.481483997024 & 26.5185160029757 \tabularnewline
80 & 347 & 335.985512136356 & 11.0144878636443 \tabularnewline
81 & 312 & 297.836655531173 & 14.1633444688269 \tabularnewline
82 & 274 & 267.300437713931 & 6.69956228606935 \tabularnewline
83 & 237 & 236.992541506197 & 0.00745849380322738 \tabularnewline
84 & 278 & 266.900682035766 & 11.0993179642339 \tabularnewline
85 & 284 & 281.633930499988 & 2.36606950001197 \tabularnewline
86 & 277 & 269.967232749700 & 7.03276725030031 \tabularnewline
87 & 317 & 320.174971097687 & -3.17497109768715 \tabularnewline
88 & 313 & 321.27690151402 & -8.27690151402027 \tabularnewline
89 & 318 & 322.045741716529 & -4.04574171652934 \tabularnewline
90 & 374 & 367.951193908377 & 6.04880609162313 \tabularnewline
91 & 413 & 417.203241898457 & -4.20324189845735 \tabularnewline
92 & 405 & 394.606219282427 & 10.3937807175729 \tabularnewline
93 & 355 & 352.306636964999 & 2.69336303500091 \tabularnewline
94 & 306 & 308.449783989775 & -2.44978398977509 \tabularnewline
95 & 271 & 266.696313192658 & 4.30368680734244 \tabularnewline
96 & 306 & 309.394151440325 & -3.39415144032546 \tabularnewline
97 & 315 & 315.103159698907 & -0.103159698907120 \tabularnewline
98 & 301 & 304.405343346895 & -3.40534334689534 \tabularnewline
99 & 356 & 349.05816545036 & 6.94183454964002 \tabularnewline
100 & 348 & 349.292530352989 & -1.29253035298922 \tabularnewline
101 & 355 & 355.073171023921 & -0.0731710239210202 \tabularnewline
102 & 422 & 414.360938373299 & 7.63906162670128 \tabularnewline
103 & 465 & 462.091156275837 & 2.90884372416303 \tabularnewline
104 & 467 & 448.979717852724 & 18.0202821472757 \tabularnewline
105 & 404 & 397.635009922124 & 6.36499007787643 \tabularnewline
106 & 347 & 345.450966287891 & 1.54903371210901 \tabularnewline
107 & 305 & 304.243031112075 & 0.756968887925495 \tabularnewline
108 & 336 & 345.615225431213 & -9.61522543121265 \tabularnewline
109 & 340 & 352.616275490587 & -12.6162754905871 \tabularnewline
110 & 318 & 334.810864863463 & -16.8108648634627 \tabularnewline
111 & 362 & 386.941342201635 & -24.9413422016352 \tabularnewline
112 & 348 & 372.190810667781 & -24.1908106677807 \tabularnewline
113 & 363 & 372.090086438255 & -9.09008643825496 \tabularnewline
114 & 435 & 435.49852015535 & -0.49852015535015 \tabularnewline
115 & 491 & 478.418370094783 & 12.5816299052173 \tabularnewline
116 & 505 & 476.297754928989 & 28.7022450710107 \tabularnewline
117 & 404 & 417.219149854211 & -13.2191498542109 \tabularnewline
118 & 359 & 354.436902962756 & 4.56309703724406 \tabularnewline
119 & 310 & 311.876475452214 & -1.87647545221358 \tabularnewline
120 & 337 & 345.975115388033 & -8.97511538803315 \tabularnewline
121 & 360 & 350.643058140312 & 9.3569418596877 \tabularnewline
122 & 342 & 334.994580207105 & 7.0054197928946 \tabularnewline
123 & 406 & 390.685875425544 & 15.3141245744561 \tabularnewline
124 & 396 & 386.498865844799 & 9.50113415520133 \tabularnewline
125 & 420 & 407.130762273235 & 12.8692377267649 \tabularnewline
126 & 472 & 491.605011219988 & -19.6050112199878 \tabularnewline
127 & 548 & 543.780046558757 & 4.21995344124275 \tabularnewline
128 & 559 & 549.935327391724 & 9.06467260827571 \tabularnewline
129 & 463 & 449.63874670941 & 13.3612532905901 \tabularnewline
130 & 407 & 399.913034211776 & 7.08696578822429 \tabularnewline
131 & 362 & 348.397212667469 & 13.6027873325307 \tabularnewline
132 & 405 & 386.652084703312 & 18.3479152966877 \tabularnewline
133 & 417 & 413.916781467445 & 3.08321853255524 \tabularnewline
134 & 391 & 392.451160427231 & -1.45116042723129 \tabularnewline
135 & 419 & 460.170497130598 & -41.1704971305977 \tabularnewline
136 & 461 & 435.421755571338 & 25.5782444286623 \tabularnewline
137 & 472 & 465.095102857788 & 6.90489714221195 \tabularnewline
138 & 535 & 534.352674389893 & 0.647325610106805 \tabularnewline
139 & 622 & 616.735366186232 & 5.26463381376755 \tabularnewline
140 & 606 & 627.551069242213 & -21.5510692422125 \tabularnewline
141 & 508 & 510.133139879417 & -2.1331398794174 \tabularnewline
142 & 461 & 446.162791637766 & 14.8372083622340 \tabularnewline
143 & 390 & 395.452817969958 & -5.45281796995783 \tabularnewline
144 & 432 & 434.572452493991 & -2.57245249399091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=39074&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]3.91819129113324[/C][/ROW]
[ROW][C]14[/C][C]126[/C][C]122.331453874744[/C][C]3.66854612525638[/C][/ROW]
[ROW][C]15[/C][C]141[/C][C]137.439015376159[/C][C]3.56098462384080[/C][/ROW]
[ROW][C]16[/C][C]135[/C][C]132.323383220296[/C][C]2.67661677970432[/C][/ROW]
[ROW][C]17[/C][C]125[/C][C]123.479682818149[/C][C]1.52031718185103[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]147.66729023319[/C][C]1.33270976680996[/C][/ROW]
[ROW][C]19[/C][C]170[/C][C]162.443244801901[/C][C]7.55675519809901[/C][/ROW]
[ROW][C]20[/C][C]170[/C][C]165.529592394220[/C][C]4.47040760577966[/C][/ROW]
[ROW][C]21[/C][C]158[/C][C]153.887714464935[/C][C]4.11228553506479[/C][/ROW]
[ROW][C]22[/C][C]133[/C][C]136.318642929538[/C][C]-3.31864292953784[/C][/ROW]
[ROW][C]23[/C][C]114[/C][C]119.090609201624[/C][C]-5.09060920162416[/C][/ROW]
[ROW][C]24[/C][C]140[/C][C]133.988714960676[/C][C]6.01128503932438[/C][/ROW]
[ROW][C]25[/C][C]145[/C][C]134.830370109666[/C][C]10.1696298903343[/C][/ROW]
[ROW][C]26[/C][C]150[/C][C]149.705146015382[/C][C]0.294853984617731[/C][/ROW]
[ROW][C]27[/C][C]178[/C][C]166.540824859925[/C][C]11.4591751400746[/C][/ROW]
[ROW][C]28[/C][C]163[/C][C]161.749729120071[/C][C]1.25027087992851[/C][/ROW]
[ROW][C]29[/C][C]172[/C][C]149.757505882891[/C][C]22.2424941171085[/C][/ROW]
[ROW][C]30[/C][C]178[/C][C]185.647729927994[/C][C]-7.64772992799351[/C][/ROW]
[ROW][C]31[/C][C]199[/C][C]206.262157804505[/C][C]-7.26215780450494[/C][/ROW]
[ROW][C]32[/C][C]199[/C][C]203.180877899401[/C][C]-4.18087789940057[/C][/ROW]
[ROW][C]33[/C][C]184[/C][C]186.419241979636[/C][C]-2.41924197963596[/C][/ROW]
[ROW][C]34[/C][C]162[/C][C]158.147782759529[/C][C]3.85221724047062[/C][/ROW]
[ROW][C]35[/C][C]146[/C][C]138.368716981075[/C][C]7.63128301892496[/C][/ROW]
[ROW][C]36[/C][C]166[/C][C]169.141347816420[/C][C]-3.14134781641982[/C][/ROW]
[ROW][C]37[/C][C]171[/C][C]170.360945870195[/C][C]0.63905412980543[/C][/ROW]
[ROW][C]38[/C][C]180[/C][C]177.438178047914[/C][C]2.56182195208552[/C][/ROW]
[ROW][C]39[/C][C]193[/C][C]206.247937689471[/C][C]-13.2479376894715[/C][/ROW]
[ROW][C]40[/C][C]181[/C][C]185.960724160929[/C][C]-4.960724160929[/C][/ROW]
[ROW][C]41[/C][C]183[/C][C]184.798364145757[/C][C]-1.79836414575698[/C][/ROW]
[ROW][C]42[/C][C]218[/C][C]195.684359733551[/C][C]22.3156402664486[/C][/ROW]
[ROW][C]43[/C][C]230[/C][C]227.498387941041[/C][C]2.50161205895935[/C][/ROW]
[ROW][C]44[/C][C]242[/C][C]229.00476968852[/C][C]12.9952303114799[/C][/ROW]
[ROW][C]45[/C][C]209[/C][C]215.630988418272[/C][C]-6.63098841827184[/C][/ROW]
[ROW][C]46[/C][C]191[/C][C]186.286598351091[/C][C]4.71340164890901[/C][/ROW]
[ROW][C]47[/C][C]172[/C][C]166.03764399356[/C][C]5.96235600644013[/C][/ROW]
[ROW][C]48[/C][C]194[/C][C]192.842509510068[/C][C]1.15749048993163[/C][/ROW]
[ROW][C]49[/C][C]196[/C][C]198.309952885671[/C][C]-2.30995288567101[/C][/ROW]
[ROW][C]50[/C][C]196[/C][C]206.984486038046[/C][C]-10.9844860380460[/C][/ROW]
[ROW][C]51[/C][C]236[/C][C]224.192940896220[/C][C]11.8070591037796[/C][/ROW]
[ROW][C]52[/C][C]235[/C][C]214.065417855659[/C][C]20.9345821443411[/C][/ROW]
[ROW][C]53[/C][C]229[/C][C]222.675252447109[/C][C]6.32474755289078[/C][/ROW]
[ROW][C]54[/C][C]243[/C][C]256.738013695474[/C][C]-13.7380136954740[/C][/ROW]
[ROW][C]55[/C][C]264[/C][C]268.357973304408[/C][C]-4.35797330440784[/C][/ROW]
[ROW][C]56[/C][C]272[/C][C]275.595124348774[/C][C]-3.59512434877428[/C][/ROW]
[ROW][C]57[/C][C]237[/C][C]240.950022441517[/C][C]-3.95002244151664[/C][/ROW]
[ROW][C]58[/C][C]211[/C][C]216.418700337472[/C][C]-5.41870033747205[/C][/ROW]
[ROW][C]59[/C][C]180[/C][C]191.302342357417[/C][C]-11.3023423574168[/C][/ROW]
[ROW][C]60[/C][C]201[/C][C]212.165187519305[/C][C]-11.1651875193053[/C][/ROW]
[ROW][C]61[/C][C]204[/C][C]211.884585973221[/C][C]-7.88458597322119[/C][/ROW]
[ROW][C]62[/C][C]188[/C][C]213.278482377781[/C][C]-25.2784823777809[/C][/ROW]
[ROW][C]63[/C][C]235[/C][C]241.844280631672[/C][C]-6.84428063167161[/C][/ROW]
[ROW][C]64[/C][C]227[/C][C]231.126644343170[/C][C]-4.12664434317026[/C][/ROW]
[ROW][C]65[/C][C]234[/C][C]222.847562357433[/C][C]11.1524376425669[/C][/ROW]
[ROW][C]66[/C][C]264[/C][C]244.403573681984[/C][C]19.5964263180161[/C][/ROW]
[ROW][C]67[/C][C]302[/C][C]270.925436787528[/C][C]31.0745632124722[/C][/ROW]
[ROW][C]68[/C][C]293[/C][C]288.463304382644[/C][C]4.53669561735649[/C][/ROW]
[ROW][C]69[/C][C]259[/C][C]253.325573114435[/C][C]5.6744268855654[/C][/ROW]
[ROW][C]70[/C][C]229[/C][C]228.457242585487[/C][C]0.54275741451346[/C][/ROW]
[ROW][C]71[/C][C]203[/C][C]198.766723711059[/C][C]4.2332762889414[/C][/ROW]
[ROW][C]72[/C][C]229[/C][C]226.325370930041[/C][C]2.67462906995868[/C][/ROW]
[ROW][C]73[/C][C]242[/C][C]232.507697921118[/C][C]9.49230207888249[/C][/ROW]
[ROW][C]74[/C][C]233[/C][C]226.155470683088[/C][C]6.84452931691163[/C][/ROW]
[ROW][C]75[/C][C]267[/C][C]284.984135370835[/C][C]-17.9841353708349[/C][/ROW]
[ROW][C]76[/C][C]269[/C][C]271.954055875775[/C][C]-2.95405587577505[/C][/ROW]
[ROW][C]77[/C][C]270[/C][C]274.414364054018[/C][C]-4.41436405401839[/C][/ROW]
[ROW][C]78[/C][C]315[/C][C]301.110875438721[/C][C]13.8891245612785[/C][/ROW]
[ROW][C]79[/C][C]364[/C][C]337.481483997024[/C][C]26.5185160029757[/C][/ROW]
[ROW][C]80[/C][C]347[/C][C]335.985512136356[/C][C]11.0144878636443[/C][/ROW]
[ROW][C]81[/C][C]312[/C][C]297.836655531173[/C][C]14.1633444688269[/C][/ROW]
[ROW][C]82[/C][C]274[/C][C]267.300437713931[/C][C]6.69956228606935[/C][/ROW]
[ROW][C]83[/C][C]237[/C][C]236.992541506197[/C][C]0.00745849380322738[/C][/ROW]
[ROW][C]84[/C][C]278[/C][C]266.900682035766[/C][C]11.0993179642339[/C][/ROW]
[ROW][C]85[/C][C]284[/C][C]281.633930499988[/C][C]2.36606950001197[/C][/ROW]
[ROW][C]86[/C][C]277[/C][C]269.967232749700[/C][C]7.03276725030031[/C][/ROW]
[ROW][C]87[/C][C]317[/C][C]320.174971097687[/C][C]-3.17497109768715[/C][/ROW]
[ROW][C]88[/C][C]313[/C][C]321.27690151402[/C][C]-8.27690151402027[/C][/ROW]
[ROW][C]89[/C][C]318[/C][C]322.045741716529[/C][C]-4.04574171652934[/C][/ROW]
[ROW][C]90[/C][C]374[/C][C]367.951193908377[/C][C]6.04880609162313[/C][/ROW]
[ROW][C]91[/C][C]413[/C][C]417.203241898457[/C][C]-4.20324189845735[/C][/ROW]
[ROW][C]92[/C][C]405[/C][C]394.606219282427[/C][C]10.3937807175729[/C][/ROW]
[ROW][C]93[/C][C]355[/C][C]352.306636964999[/C][C]2.69336303500091[/C][/ROW]
[ROW][C]94[/C][C]306[/C][C]308.449783989775[/C][C]-2.44978398977509[/C][/ROW]
[ROW][C]95[/C][C]271[/C][C]266.696313192658[/C][C]4.30368680734244[/C][/ROW]
[ROW][C]96[/C][C]306[/C][C]309.394151440325[/C][C]-3.39415144032546[/C][/ROW]
[ROW][C]97[/C][C]315[/C][C]315.103159698907[/C][C]-0.103159698907120[/C][/ROW]
[ROW][C]98[/C][C]301[/C][C]304.405343346895[/C][C]-3.40534334689534[/C][/ROW]
[ROW][C]99[/C][C]356[/C][C]349.05816545036[/C][C]6.94183454964002[/C][/ROW]
[ROW][C]100[/C][C]348[/C][C]349.292530352989[/C][C]-1.29253035298922[/C][/ROW]
[ROW][C]101[/C][C]355[/C][C]355.073171023921[/C][C]-0.0731710239210202[/C][/ROW]
[ROW][C]102[/C][C]422[/C][C]414.360938373299[/C][C]7.63906162670128[/C][/ROW]
[ROW][C]103[/C][C]465[/C][C]462.091156275837[/C][C]2.90884372416303[/C][/ROW]
[ROW][C]104[/C][C]467[/C][C]448.979717852724[/C][C]18.0202821472757[/C][/ROW]
[ROW][C]105[/C][C]404[/C][C]397.635009922124[/C][C]6.36499007787643[/C][/ROW]
[ROW][C]106[/C][C]347[/C][C]345.450966287891[/C][C]1.54903371210901[/C][/ROW]
[ROW][C]107[/C][C]305[/C][C]304.243031112075[/C][C]0.756968887925495[/C][/ROW]
[ROW][C]108[/C][C]336[/C][C]345.615225431213[/C][C]-9.61522543121265[/C][/ROW]
[ROW][C]109[/C][C]340[/C][C]352.616275490587[/C][C]-12.6162754905871[/C][/ROW]
[ROW][C]110[/C][C]318[/C][C]334.810864863463[/C][C]-16.8108648634627[/C][/ROW]
[ROW][C]111[/C][C]362[/C][C]386.941342201635[/C][C]-24.9413422016352[/C][/ROW]
[ROW][C]112[/C][C]348[/C][C]372.190810667781[/C][C]-24.1908106677807[/C][/ROW]
[ROW][C]113[/C][C]363[/C][C]372.090086438255[/C][C]-9.09008643825496[/C][/ROW]
[ROW][C]114[/C][C]435[/C][C]435.49852015535[/C][C]-0.49852015535015[/C][/ROW]
[ROW][C]115[/C][C]491[/C][C]478.418370094783[/C][C]12.5816299052173[/C][/ROW]
[ROW][C]116[/C][C]505[/C][C]476.297754928989[/C][C]28.7022450710107[/C][/ROW]
[ROW][C]117[/C][C]404[/C][C]417.219149854211[/C][C]-13.2191498542109[/C][/ROW]
[ROW][C]118[/C][C]359[/C][C]354.436902962756[/C][C]4.56309703724406[/C][/ROW]
[ROW][C]119[/C][C]310[/C][C]311.876475452214[/C][C]-1.87647545221358[/C][/ROW]
[ROW][C]120[/C][C]337[/C][C]345.975115388033[/C][C]-8.97511538803315[/C][/ROW]
[ROW][C]121[/C][C]360[/C][C]350.643058140312[/C][C]9.3569418596877[/C][/ROW]
[ROW][C]122[/C][C]342[/C][C]334.994580207105[/C][C]7.0054197928946[/C][/ROW]
[ROW][C]123[/C][C]406[/C][C]390.685875425544[/C][C]15.3141245744561[/C][/ROW]
[ROW][C]124[/C][C]396[/C][C]386.498865844799[/C][C]9.50113415520133[/C][/ROW]
[ROW][C]125[/C][C]420[/C][C]407.130762273235[/C][C]12.8692377267649[/C][/ROW]
[ROW][C]126[/C][C]472[/C][C]491.605011219988[/C][C]-19.6050112199878[/C][/ROW]
[ROW][C]127[/C][C]548[/C][C]543.780046558757[/C][C]4.21995344124275[/C][/ROW]
[ROW][C]128[/C][C]559[/C][C]549.935327391724[/C][C]9.06467260827571[/C][/ROW]
[ROW][C]129[/C][C]463[/C][C]449.63874670941[/C][C]13.3612532905901[/C][/ROW]
[ROW][C]130[/C][C]407[/C][C]399.913034211776[/C][C]7.08696578822429[/C][/ROW]
[ROW][C]131[/C][C]362[/C][C]348.397212667469[/C][C]13.6027873325307[/C][/ROW]
[ROW][C]132[/C][C]405[/C][C]386.652084703312[/C][C]18.3479152966877[/C][/ROW]
[ROW][C]133[/C][C]417[/C][C]413.916781467445[/C][C]3.08321853255524[/C][/ROW]
[ROW][C]134[/C][C]391[/C][C]392.451160427231[/C][C]-1.45116042723129[/C][/ROW]
[ROW][C]135[/C][C]419[/C][C]460.170497130598[/C][C]-41.1704971305977[/C][/ROW]
[ROW][C]136[/C][C]461[/C][C]435.421755571338[/C][C]25.5782444286623[/C][/ROW]
[ROW][C]137[/C][C]472[/C][C]465.095102857788[/C][C]6.90489714221195[/C][/ROW]
[ROW][C]138[/C][C]535[/C][C]534.352674389893[/C][C]0.647325610106805[/C][/ROW]
[ROW][C]139[/C][C]622[/C][C]616.735366186232[/C][C]5.26463381376755[/C][/ROW]
[ROW][C]140[/C][C]606[/C][C]627.551069242213[/C][C]-21.5510692422125[/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]446.162791637766[/C][C]14.8372083622340[/C][/ROW]
[ROW][C]143[/C][C]390[/C][C]395.452817969958[/C][C]-5.45281796995783[/C][/ROW]
[ROW][C]144[/C][C]432[/C][C]434.572452493991[/C][C]-2.57245249399091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=39074&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=39074&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
13115111.0818087088673.91819129113324
14126122.3314538747443.66854612525638
15141137.4390153761593.56098462384080
16135132.3233832202962.67661677970432
17125123.4796828181491.52031718185103
18149147.667290233191.33270976680996
19170162.4432448019017.55675519809901
20170165.5295923942204.47040760577966
21158153.8877144649354.11228553506479
22133136.318642929538-3.31864292953784
23114119.090609201624-5.09060920162416
24140133.9887149606766.01128503932438
25145134.83037010966610.1696298903343
26150149.7051460153820.294853984617731
27178166.54082485992511.4591751400746
28163161.7497291200711.25027087992851
29172149.75750588289122.2424941171085
30178185.647729927994-7.64772992799351
31199206.262157804505-7.26215780450494
32199203.180877899401-4.18087789940057
33184186.419241979636-2.41924197963596
34162158.1477827595293.85221724047062
35146138.3687169810757.63128301892496
36166169.141347816420-3.14134781641982
37171170.3609458701950.63905412980543
38180177.4381780479142.56182195208552
39193206.247937689471-13.2479376894715
40181185.960724160929-4.960724160929
41183184.798364145757-1.79836414575698
42218195.68435973355122.3156402664486
43230227.4983879410412.50161205895935
44242229.0047696885212.9952303114799
45209215.630988418272-6.63098841827184
46191186.2865983510914.71340164890901
47172166.037643993565.96235600644013
48194192.8425095100681.15749048993163
49196198.309952885671-2.30995288567101
50196206.984486038046-10.9844860380460
51236224.19294089622011.8070591037796
52235214.06541785565920.9345821443411
53229222.6752524471096.32474755289078
54243256.738013695474-13.7380136954740
55264268.357973304408-4.35797330440784
56272275.595124348774-3.59512434877428
57237240.950022441517-3.95002244151664
58211216.418700337472-5.41870033747205
59180191.302342357417-11.3023423574168
60201212.165187519305-11.1651875193053
61204211.884585973221-7.88458597322119
62188213.278482377781-25.2784823777809
63235241.844280631672-6.84428063167161
64227231.126644343170-4.12664434317026
65234222.84756235743311.1524376425669
66264244.40357368198419.5964263180161
67302270.92543678752831.0745632124722
68293288.4633043826444.53669561735649
69259253.3255731144355.6744268855654
70229228.4572425854870.54275741451346
71203198.7667237110594.2332762889414
72229226.3253709300412.67462906995868
73242232.5076979211189.49230207888249
74233226.1554706830886.84452931691163
75267284.984135370835-17.9841353708349
76269271.954055875775-2.95405587577505
77270274.414364054018-4.41436405401839
78315301.11087543872113.8891245612785
79364337.48148399702426.5185160029757
80347335.98551213635611.0144878636443
81312297.83665553117314.1633444688269
82274267.3004377139316.69956228606935
83237236.9925415061970.00745849380322738
84278266.90068203576611.0993179642339
85284281.6339304999882.36606950001197
86277269.9672327497007.03276725030031
87317320.174971097687-3.17497109768715
88313321.27690151402-8.27690151402027
89318322.045741716529-4.04574171652934
90374367.9511939083776.04880609162313
91413417.203241898457-4.20324189845735
92405394.60621928242710.3937807175729
93355352.3066369649992.69336303500091
94306308.449783989775-2.44978398977509
95271266.6963131926584.30368680734244
96306309.394151440325-3.39415144032546
97315315.103159698907-0.103159698907120
98301304.405343346895-3.40534334689534
99356349.058165450366.94183454964002
100348349.292530352989-1.29253035298922
101355355.073171023921-0.0731710239210202
102422414.3609383732997.63906162670128
103465462.0911562758372.90884372416303
104467448.97971785272418.0202821472757
105404397.6350099221246.36499007787643
106347345.4509662878911.54903371210901
107305304.2430311120750.756968887925495
108336345.615225431213-9.61522543121265
109340352.616275490587-12.6162754905871
110318334.810864863463-16.8108648634627
111362386.941342201635-24.9413422016352
112348372.190810667781-24.1908106677807
113363372.090086438255-9.09008643825496
114435435.49852015535-0.49852015535015
115491478.41837009478312.5816299052173
116505476.29775492898928.7022450710107
117404417.219149854211-13.2191498542109
118359354.4369029627564.56309703724406
119310311.876475452214-1.87647545221358
120337345.975115388033-8.97511538803315
121360350.6430581403129.3569418596877
122342334.9945802071057.0054197928946
123406390.68587542554415.3141245744561
124396386.4988658447999.50113415520133
125420407.13076227323512.8692377267649
126472491.605011219988-19.6050112199878
127548543.7800465587574.21995344124275
128559549.9353273917249.06467260827571
129463449.6387467094113.3612532905901
130407399.9130342117767.08696578822429
131362348.39721266746913.6027873325307
132405386.65208470331218.3479152966877
133417413.9167814674453.08321853255524
134391392.451160427231-1.45116042723129
135419460.170497130598-41.1704971305977
136461435.42175557133825.5782444286623
137472465.0951028577886.90489714221195
138535534.3526743898930.647325610106805
139622616.7353661862325.26463381376755
140606627.551069242213-21.5510692422125
141508510.133139879417-2.1331398794174
142461446.16279163776614.8372083622340
143390395.452817969958-5.45281796995783
144432434.572452493991-2.57245249399091







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
145447.055907793736427.306081839850466.805733747622
146419.712252491671399.132529133903440.291975849439
147464.867062938293442.962936376519486.771189500067
148496.083938082045472.832869325620519.33500683847
149507.532607460904483.137451881795531.927763040012
150575.450839384531548.708168067462602.1935107016
151666.592241551563636.628745108619696.555737994507
152657.913647955733627.181984104025688.645311807442
153550.308727275636521.639727613372578.9777269379
154492.98528697611465.015260234124520.955313718097
155420.207239449299393.468712587961446.945766310636
156465.634459283957443.300359438502487.968559129412

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
145 & 447.055907793736 & 427.306081839850 & 466.805733747622 \tabularnewline
146 & 419.712252491671 & 399.132529133903 & 440.291975849439 \tabularnewline
147 & 464.867062938293 & 442.962936376519 & 486.771189500067 \tabularnewline
148 & 496.083938082045 & 472.832869325620 & 519.33500683847 \tabularnewline
149 & 507.532607460904 & 483.137451881795 & 531.927763040012 \tabularnewline
150 & 575.450839384531 & 548.708168067462 & 602.1935107016 \tabularnewline
151 & 666.592241551563 & 636.628745108619 & 696.555737994507 \tabularnewline
152 & 657.913647955733 & 627.181984104025 & 688.645311807442 \tabularnewline
153 & 550.308727275636 & 521.639727613372 & 578.9777269379 \tabularnewline
154 & 492.98528697611 & 465.015260234124 & 520.955313718097 \tabularnewline
155 & 420.207239449299 & 393.468712587961 & 446.945766310636 \tabularnewline
156 & 465.634459283957 & 443.300359438502 & 487.968559129412 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=39074&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]447.055907793736[/C][C]427.306081839850[/C][C]466.805733747622[/C][/ROW]
[ROW][C]146[/C][C]419.712252491671[/C][C]399.132529133903[/C][C]440.291975849439[/C][/ROW]
[ROW][C]147[/C][C]464.867062938293[/C][C]442.962936376519[/C][C]486.771189500067[/C][/ROW]
[ROW][C]148[/C][C]496.083938082045[/C][C]472.832869325620[/C][C]519.33500683847[/C][/ROW]
[ROW][C]149[/C][C]507.532607460904[/C][C]483.137451881795[/C][C]531.927763040012[/C][/ROW]
[ROW][C]150[/C][C]575.450839384531[/C][C]548.708168067462[/C][C]602.1935107016[/C][/ROW]
[ROW][C]151[/C][C]666.592241551563[/C][C]636.628745108619[/C][C]696.555737994507[/C][/ROW]
[ROW][C]152[/C][C]657.913647955733[/C][C]627.181984104025[/C][C]688.645311807442[/C][/ROW]
[ROW][C]153[/C][C]550.308727275636[/C][C]521.639727613372[/C][C]578.9777269379[/C][/ROW]
[ROW][C]154[/C][C]492.98528697611[/C][C]465.015260234124[/C][C]520.955313718097[/C][/ROW]
[ROW][C]155[/C][C]420.207239449299[/C][C]393.468712587961[/C][C]446.945766310636[/C][/ROW]
[ROW][C]156[/C][C]465.634459283957[/C][C]443.300359438502[/C][C]487.968559129412[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=39074&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=39074&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
145447.055907793736427.306081839850466.805733747622
146419.712252491671399.132529133903440.291975849439
147464.867062938293442.962936376519486.771189500067
148496.083938082045472.832869325620519.33500683847
149507.532607460904483.137451881795531.927763040012
150575.450839384531548.708168067462602.1935107016
151666.592241551563636.628745108619696.555737994507
152657.913647955733627.181984104025688.645311807442
153550.308727275636521.639727613372578.9777269379
154492.98528697611465.015260234124520.955313718097
155420.207239449299393.468712587961446.945766310636
156465.634459283957443.300359438502487.968559129412



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')