Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationMon, 13 Apr 2020 23:51:58 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Apr/13/t1586814760va1bbuo1iony20h.htm/, Retrieved Wed, 21 Apr 2021 08:20:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319133, Retrieved Wed, 21 Apr 2021 08:20:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact42
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [] [2020-04-13 21:51:58] [d41d8cd98f00b204e9800998ecf8427e] [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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319133&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319133&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319133&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.999933893038648
betaFALSE
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.999933893038648 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319133&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]0.999933893038648[/C][/ROW]
[ROW][C]beta[/C][C]FALSE[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319133&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319133&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
alpha0.999933893038648
betaFALSE
gammaFALSE







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
21181126
3132117.99960335823214.0003966417681
4129131.99907447632-2.99907447632032
5121129.0001982597-8.00019825970048
6135121.00052886879713.9994711312028
7148134.99907453750313.000925462497
8148147.9991405483230.000859451677087009
9136147.999999943184-11.9999999431843
10119136.000793283532-17.0007932835325
11104119.001123870785-15.0011238707846
12118104.00099167871613.999008321284
13115117.999074568098-2.99907456809794
14126115.00019825970710.9998017402934
15141125.99927283653115.0007271634685
16135140.999008347509-5.99900834750918
17125135.000396576213-10.000396576213
18149125.0006610958323.99933890417
19170148.99841347663121.0015865233694
20170169.9986116489310.00138835106861279
21158169.99999990822-11.9999999082203
22133158.00079328353-25.0007932835302
23114133.001652726475-19.0016527264754
24140114.00125614152225.9987438584776
25145139.9982813020455.00171869795543
26150144.9996693515755.00033064842467
27178149.99966944333528.0003305566649
28163177.99814898323-14.9981489832301
29172163.0009914820558.99900851794482
30178171.9994051028926.0005948971083
31199177.99960331890521.0003966810949
32199198.9986117275880.00138827241178774
33184198.999999908226-14.9999999082255
34162184.000991604414-22.0009916044142
35146162.001454418702-16.0014544187017
36166146.00105780752919.9989421924712
37171165.9986779307015.0013220692986
38180170.9996693777959.00033062220473
39193179.99940501549113.0005949845086
40181192.99914057017-11.9991405701698
41183181.0007932267221.99920677327808
42218182.99986783851535.0001321614849
43230217.99768624761612.0023137523841
44242229.99920656350912.0007934364913
45209241.999206664012-32.9992066640121
46191209.00218147728-18.0021814772796
47172191.001190069515-19.0011900695152
48194172.00125611093821.9987438890624
49196193.9985457298882.00145427011205
50196195.999867689940.000132310060081409
51236195.99999999125340.0000000087466
52235235.997355721545-0.997355721545347
53229235.000065932156-6.00006593215613
54243229.00039664612713.9996033538733
55264242.99907452876221.0009254712379
56272263.9986116926328.00138830736847
57237271.999471052532-34.9994710525324
58211237.00231370868-26.0023137086802
59180211.001718933947-31.0017189339474
60201180.00204942943520.9979505705646
61204200.9986118892933.00138811070684
62188203.999801587352-15.9998015873522
63235188.00105769826546.9989423017348
64227234.996893042738-7.99689304273767
65234227.0005286502996.99947134970068
66264233.99953728621830.000462713782
67302263.99801676057138.0019832394291
68293301.997487804363-8.99748780436272
69259293.000594796579-34.0005947965785
70229259.002247676006-30.0022476760062
71203229.001983357428-26.0019833574276
72229203.00171891210925.9982810878911
73242228.99828133263713.0017186673631
74233241.999140495887-8.99914049588654
75267233.00059490583333.999405094167
76269266.9977524026412.00224759735852
77270268.9998676374951.00013236250453
78315269.99993388428945.0000661157114
79364314.99702518236849.0029748176315
80347363.996760562238-16.9967605622376
81312347.001123604194-35.0011236041936
82274312.002313817925-38.0023138179254
83237274.002512217491-37.0025122174908
84278237.00244612364540.9975538763549
85284277.997289776296.00271022370964
86277283.999603179067-6.99960317906726
87317277.00046272249739.9995372775032
88313316.997355752135-3.99735575213509
89318313.0002642530424.99973574695775
90374317.99966948266256.0003305173378
91413373.99629798831539.0037020116852
92405412.997421583779-7.99742158377853
93355405.00052868524-50.0005286852395
94306355.003305383017-49.0033053830174
95271306.003239459615-35.003239459615
96306271.00231395779834.9976860422018
97315305.9976864093219.00231359067863
98301314.999404884403-13.9994048844034
99356301.00092545811854.9990745418824
100348355.996364178305-7.99636417830487
101355348.0005286153386.99947138466234
102422354.99953728621667.0004627137843
103465421.99557080300143.0044291969992
104467464.9971571078612.00284289213886
105404466.999867598142-62.9998675981423
106347404.004164729812-57.0041647298125
107305347.003768372115-42.0037683721147
108336305.00277674149230.9972232585076
109340335.997950867764.00204913223996
110318339.999735436693-21.9997354366927
111362318.0014543356643.9985456643398
112348361.997091389842-13.9970913898422
113363348.0009253051814.9990746948205
114435362.99900845674972.0009915432512
115491434.99524023323556.0047597667652
116505490.99629769551114.0037023044894
117404504.999074257793-100.999074257793
118359404.006676741899-45.0066767418986
119310359.00297525464-49.00297525464
120337310.00323943779126.9967605622087
121360336.99821532619323.0017846738072
122342359.99847942191-17.9984794219096
123406342.00118982478463.9988101752164
124396405.995769233129-9.99576923312918
125420396.0006607899323.9993392100696
126472419.9984134766152.0015865233896
127548471.99656233312976.0034376668706
128559547.99497564368411.0050243563164
129463558.99927249128-95.9992724912802
130407463.006346220196-56.0063462201964
131362407.003702409365-45.003702409365
132405362.00297505801642.9970249419841
133417404.99715759733412.0028424026661
134391416.999206528561-25.9992065285612
135419391.00171872854127.9982812714588
136461418.99814911870242.0018508812979
137472460.99722338526711.0027766147329
138535471.99927263987263.0007273601285
139622534.99583521335187.0041647866487
140606621.994248419041-15.994248419041
141508606.001057331162-98.0010573311621
142461508.006478552109-47.0064785521095
143390461.003107455461-71.003107455461
144432390.0046937996841.9953062003196

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 118 & 112 & 6 \tabularnewline
3 & 132 & 117.999603358232 & 14.0003966417681 \tabularnewline
4 & 129 & 131.99907447632 & -2.99907447632032 \tabularnewline
5 & 121 & 129.0001982597 & -8.00019825970048 \tabularnewline
6 & 135 & 121.000528868797 & 13.9994711312028 \tabularnewline
7 & 148 & 134.999074537503 & 13.000925462497 \tabularnewline
8 & 148 & 147.999140548323 & 0.000859451677087009 \tabularnewline
9 & 136 & 147.999999943184 & -11.9999999431843 \tabularnewline
10 & 119 & 136.000793283532 & -17.0007932835325 \tabularnewline
11 & 104 & 119.001123870785 & -15.0011238707846 \tabularnewline
12 & 118 & 104.000991678716 & 13.999008321284 \tabularnewline
13 & 115 & 117.999074568098 & -2.99907456809794 \tabularnewline
14 & 126 & 115.000198259707 & 10.9998017402934 \tabularnewline
15 & 141 & 125.999272836531 & 15.0007271634685 \tabularnewline
16 & 135 & 140.999008347509 & -5.99900834750918 \tabularnewline
17 & 125 & 135.000396576213 & -10.000396576213 \tabularnewline
18 & 149 & 125.00066109583 & 23.99933890417 \tabularnewline
19 & 170 & 148.998413476631 & 21.0015865233694 \tabularnewline
20 & 170 & 169.998611648931 & 0.00138835106861279 \tabularnewline
21 & 158 & 169.99999990822 & -11.9999999082203 \tabularnewline
22 & 133 & 158.00079328353 & -25.0007932835302 \tabularnewline
23 & 114 & 133.001652726475 & -19.0016527264754 \tabularnewline
24 & 140 & 114.001256141522 & 25.9987438584776 \tabularnewline
25 & 145 & 139.998281302045 & 5.00171869795543 \tabularnewline
26 & 150 & 144.999669351575 & 5.00033064842467 \tabularnewline
27 & 178 & 149.999669443335 & 28.0003305566649 \tabularnewline
28 & 163 & 177.99814898323 & -14.9981489832301 \tabularnewline
29 & 172 & 163.000991482055 & 8.99900851794482 \tabularnewline
30 & 178 & 171.999405102892 & 6.0005948971083 \tabularnewline
31 & 199 & 177.999603318905 & 21.0003966810949 \tabularnewline
32 & 199 & 198.998611727588 & 0.00138827241178774 \tabularnewline
33 & 184 & 198.999999908226 & -14.9999999082255 \tabularnewline
34 & 162 & 184.000991604414 & -22.0009916044142 \tabularnewline
35 & 146 & 162.001454418702 & -16.0014544187017 \tabularnewline
36 & 166 & 146.001057807529 & 19.9989421924712 \tabularnewline
37 & 171 & 165.998677930701 & 5.0013220692986 \tabularnewline
38 & 180 & 170.999669377795 & 9.00033062220473 \tabularnewline
39 & 193 & 179.999405015491 & 13.0005949845086 \tabularnewline
40 & 181 & 192.99914057017 & -11.9991405701698 \tabularnewline
41 & 183 & 181.000793226722 & 1.99920677327808 \tabularnewline
42 & 218 & 182.999867838515 & 35.0001321614849 \tabularnewline
43 & 230 & 217.997686247616 & 12.0023137523841 \tabularnewline
44 & 242 & 229.999206563509 & 12.0007934364913 \tabularnewline
45 & 209 & 241.999206664012 & -32.9992066640121 \tabularnewline
46 & 191 & 209.00218147728 & -18.0021814772796 \tabularnewline
47 & 172 & 191.001190069515 & -19.0011900695152 \tabularnewline
48 & 194 & 172.001256110938 & 21.9987438890624 \tabularnewline
49 & 196 & 193.998545729888 & 2.00145427011205 \tabularnewline
50 & 196 & 195.99986768994 & 0.000132310060081409 \tabularnewline
51 & 236 & 195.999999991253 & 40.0000000087466 \tabularnewline
52 & 235 & 235.997355721545 & -0.997355721545347 \tabularnewline
53 & 229 & 235.000065932156 & -6.00006593215613 \tabularnewline
54 & 243 & 229.000396646127 & 13.9996033538733 \tabularnewline
55 & 264 & 242.999074528762 & 21.0009254712379 \tabularnewline
56 & 272 & 263.998611692632 & 8.00138830736847 \tabularnewline
57 & 237 & 271.999471052532 & -34.9994710525324 \tabularnewline
58 & 211 & 237.00231370868 & -26.0023137086802 \tabularnewline
59 & 180 & 211.001718933947 & -31.0017189339474 \tabularnewline
60 & 201 & 180.002049429435 & 20.9979505705646 \tabularnewline
61 & 204 & 200.998611889293 & 3.00138811070684 \tabularnewline
62 & 188 & 203.999801587352 & -15.9998015873522 \tabularnewline
63 & 235 & 188.001057698265 & 46.9989423017348 \tabularnewline
64 & 227 & 234.996893042738 & -7.99689304273767 \tabularnewline
65 & 234 & 227.000528650299 & 6.99947134970068 \tabularnewline
66 & 264 & 233.999537286218 & 30.000462713782 \tabularnewline
67 & 302 & 263.998016760571 & 38.0019832394291 \tabularnewline
68 & 293 & 301.997487804363 & -8.99748780436272 \tabularnewline
69 & 259 & 293.000594796579 & -34.0005947965785 \tabularnewline
70 & 229 & 259.002247676006 & -30.0022476760062 \tabularnewline
71 & 203 & 229.001983357428 & -26.0019833574276 \tabularnewline
72 & 229 & 203.001718912109 & 25.9982810878911 \tabularnewline
73 & 242 & 228.998281332637 & 13.0017186673631 \tabularnewline
74 & 233 & 241.999140495887 & -8.99914049588654 \tabularnewline
75 & 267 & 233.000594905833 & 33.999405094167 \tabularnewline
76 & 269 & 266.997752402641 & 2.00224759735852 \tabularnewline
77 & 270 & 268.999867637495 & 1.00013236250453 \tabularnewline
78 & 315 & 269.999933884289 & 45.0000661157114 \tabularnewline
79 & 364 & 314.997025182368 & 49.0029748176315 \tabularnewline
80 & 347 & 363.996760562238 & -16.9967605622376 \tabularnewline
81 & 312 & 347.001123604194 & -35.0011236041936 \tabularnewline
82 & 274 & 312.002313817925 & -38.0023138179254 \tabularnewline
83 & 237 & 274.002512217491 & -37.0025122174908 \tabularnewline
84 & 278 & 237.002446123645 & 40.9975538763549 \tabularnewline
85 & 284 & 277.99728977629 & 6.00271022370964 \tabularnewline
86 & 277 & 283.999603179067 & -6.99960317906726 \tabularnewline
87 & 317 & 277.000462722497 & 39.9995372775032 \tabularnewline
88 & 313 & 316.997355752135 & -3.99735575213509 \tabularnewline
89 & 318 & 313.000264253042 & 4.99973574695775 \tabularnewline
90 & 374 & 317.999669482662 & 56.0003305173378 \tabularnewline
91 & 413 & 373.996297988315 & 39.0037020116852 \tabularnewline
92 & 405 & 412.997421583779 & -7.99742158377853 \tabularnewline
93 & 355 & 405.00052868524 & -50.0005286852395 \tabularnewline
94 & 306 & 355.003305383017 & -49.0033053830174 \tabularnewline
95 & 271 & 306.003239459615 & -35.003239459615 \tabularnewline
96 & 306 & 271.002313957798 & 34.9976860422018 \tabularnewline
97 & 315 & 305.997686409321 & 9.00231359067863 \tabularnewline
98 & 301 & 314.999404884403 & -13.9994048844034 \tabularnewline
99 & 356 & 301.000925458118 & 54.9990745418824 \tabularnewline
100 & 348 & 355.996364178305 & -7.99636417830487 \tabularnewline
101 & 355 & 348.000528615338 & 6.99947138466234 \tabularnewline
102 & 422 & 354.999537286216 & 67.0004627137843 \tabularnewline
103 & 465 & 421.995570803001 & 43.0044291969992 \tabularnewline
104 & 467 & 464.997157107861 & 2.00284289213886 \tabularnewline
105 & 404 & 466.999867598142 & -62.9998675981423 \tabularnewline
106 & 347 & 404.004164729812 & -57.0041647298125 \tabularnewline
107 & 305 & 347.003768372115 & -42.0037683721147 \tabularnewline
108 & 336 & 305.002776741492 & 30.9972232585076 \tabularnewline
109 & 340 & 335.99795086776 & 4.00204913223996 \tabularnewline
110 & 318 & 339.999735436693 & -21.9997354366927 \tabularnewline
111 & 362 & 318.00145433566 & 43.9985456643398 \tabularnewline
112 & 348 & 361.997091389842 & -13.9970913898422 \tabularnewline
113 & 363 & 348.00092530518 & 14.9990746948205 \tabularnewline
114 & 435 & 362.999008456749 & 72.0009915432512 \tabularnewline
115 & 491 & 434.995240233235 & 56.0047597667652 \tabularnewline
116 & 505 & 490.996297695511 & 14.0037023044894 \tabularnewline
117 & 404 & 504.999074257793 & -100.999074257793 \tabularnewline
118 & 359 & 404.006676741899 & -45.0066767418986 \tabularnewline
119 & 310 & 359.00297525464 & -49.00297525464 \tabularnewline
120 & 337 & 310.003239437791 & 26.9967605622087 \tabularnewline
121 & 360 & 336.998215326193 & 23.0017846738072 \tabularnewline
122 & 342 & 359.99847942191 & -17.9984794219096 \tabularnewline
123 & 406 & 342.001189824784 & 63.9988101752164 \tabularnewline
124 & 396 & 405.995769233129 & -9.99576923312918 \tabularnewline
125 & 420 & 396.00066078993 & 23.9993392100696 \tabularnewline
126 & 472 & 419.99841347661 & 52.0015865233896 \tabularnewline
127 & 548 & 471.996562333129 & 76.0034376668706 \tabularnewline
128 & 559 & 547.994975643684 & 11.0050243563164 \tabularnewline
129 & 463 & 558.99927249128 & -95.9992724912802 \tabularnewline
130 & 407 & 463.006346220196 & -56.0063462201964 \tabularnewline
131 & 362 & 407.003702409365 & -45.003702409365 \tabularnewline
132 & 405 & 362.002975058016 & 42.9970249419841 \tabularnewline
133 & 417 & 404.997157597334 & 12.0028424026661 \tabularnewline
134 & 391 & 416.999206528561 & -25.9992065285612 \tabularnewline
135 & 419 & 391.001718728541 & 27.9982812714588 \tabularnewline
136 & 461 & 418.998149118702 & 42.0018508812979 \tabularnewline
137 & 472 & 460.997223385267 & 11.0027766147329 \tabularnewline
138 & 535 & 471.999272639872 & 63.0007273601285 \tabularnewline
139 & 622 & 534.995835213351 & 87.0041647866487 \tabularnewline
140 & 606 & 621.994248419041 & -15.994248419041 \tabularnewline
141 & 508 & 606.001057331162 & -98.0010573311621 \tabularnewline
142 & 461 & 508.006478552109 & -47.0064785521095 \tabularnewline
143 & 390 & 461.003107455461 & -71.003107455461 \tabularnewline
144 & 432 & 390.00469379968 & 41.9953062003196 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319133&T=2

[TABLE]
[ROW][C]Interpolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Residuals[/C][/ROW]
[ROW][C]2[/C][C]118[/C][C]112[/C][C]6[/C][/ROW]
[ROW][C]3[/C][C]132[/C][C]117.999603358232[/C][C]14.0003966417681[/C][/ROW]
[ROW][C]4[/C][C]129[/C][C]131.99907447632[/C][C]-2.99907447632032[/C][/ROW]
[ROW][C]5[/C][C]121[/C][C]129.0001982597[/C][C]-8.00019825970048[/C][/ROW]
[ROW][C]6[/C][C]135[/C][C]121.000528868797[/C][C]13.9994711312028[/C][/ROW]
[ROW][C]7[/C][C]148[/C][C]134.999074537503[/C][C]13.000925462497[/C][/ROW]
[ROW][C]8[/C][C]148[/C][C]147.999140548323[/C][C]0.000859451677087009[/C][/ROW]
[ROW][C]9[/C][C]136[/C][C]147.999999943184[/C][C]-11.9999999431843[/C][/ROW]
[ROW][C]10[/C][C]119[/C][C]136.000793283532[/C][C]-17.0007932835325[/C][/ROW]
[ROW][C]11[/C][C]104[/C][C]119.001123870785[/C][C]-15.0011238707846[/C][/ROW]
[ROW][C]12[/C][C]118[/C][C]104.000991678716[/C][C]13.999008321284[/C][/ROW]
[ROW][C]13[/C][C]115[/C][C]117.999074568098[/C][C]-2.99907456809794[/C][/ROW]
[ROW][C]14[/C][C]126[/C][C]115.000198259707[/C][C]10.9998017402934[/C][/ROW]
[ROW][C]15[/C][C]141[/C][C]125.999272836531[/C][C]15.0007271634685[/C][/ROW]
[ROW][C]16[/C][C]135[/C][C]140.999008347509[/C][C]-5.99900834750918[/C][/ROW]
[ROW][C]17[/C][C]125[/C][C]135.000396576213[/C][C]-10.000396576213[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]125.00066109583[/C][C]23.99933890417[/C][/ROW]
[ROW][C]19[/C][C]170[/C][C]148.998413476631[/C][C]21.0015865233694[/C][/ROW]
[ROW][C]20[/C][C]170[/C][C]169.998611648931[/C][C]0.00138835106861279[/C][/ROW]
[ROW][C]21[/C][C]158[/C][C]169.99999990822[/C][C]-11.9999999082203[/C][/ROW]
[ROW][C]22[/C][C]133[/C][C]158.00079328353[/C][C]-25.0007932835302[/C][/ROW]
[ROW][C]23[/C][C]114[/C][C]133.001652726475[/C][C]-19.0016527264754[/C][/ROW]
[ROW][C]24[/C][C]140[/C][C]114.001256141522[/C][C]25.9987438584776[/C][/ROW]
[ROW][C]25[/C][C]145[/C][C]139.998281302045[/C][C]5.00171869795543[/C][/ROW]
[ROW][C]26[/C][C]150[/C][C]144.999669351575[/C][C]5.00033064842467[/C][/ROW]
[ROW][C]27[/C][C]178[/C][C]149.999669443335[/C][C]28.0003305566649[/C][/ROW]
[ROW][C]28[/C][C]163[/C][C]177.99814898323[/C][C]-14.9981489832301[/C][/ROW]
[ROW][C]29[/C][C]172[/C][C]163.000991482055[/C][C]8.99900851794482[/C][/ROW]
[ROW][C]30[/C][C]178[/C][C]171.999405102892[/C][C]6.0005948971083[/C][/ROW]
[ROW][C]31[/C][C]199[/C][C]177.999603318905[/C][C]21.0003966810949[/C][/ROW]
[ROW][C]32[/C][C]199[/C][C]198.998611727588[/C][C]0.00138827241178774[/C][/ROW]
[ROW][C]33[/C][C]184[/C][C]198.999999908226[/C][C]-14.9999999082255[/C][/ROW]
[ROW][C]34[/C][C]162[/C][C]184.000991604414[/C][C]-22.0009916044142[/C][/ROW]
[ROW][C]35[/C][C]146[/C][C]162.001454418702[/C][C]-16.0014544187017[/C][/ROW]
[ROW][C]36[/C][C]166[/C][C]146.001057807529[/C][C]19.9989421924712[/C][/ROW]
[ROW][C]37[/C][C]171[/C][C]165.998677930701[/C][C]5.0013220692986[/C][/ROW]
[ROW][C]38[/C][C]180[/C][C]170.999669377795[/C][C]9.00033062220473[/C][/ROW]
[ROW][C]39[/C][C]193[/C][C]179.999405015491[/C][C]13.0005949845086[/C][/ROW]
[ROW][C]40[/C][C]181[/C][C]192.99914057017[/C][C]-11.9991405701698[/C][/ROW]
[ROW][C]41[/C][C]183[/C][C]181.000793226722[/C][C]1.99920677327808[/C][/ROW]
[ROW][C]42[/C][C]218[/C][C]182.999867838515[/C][C]35.0001321614849[/C][/ROW]
[ROW][C]43[/C][C]230[/C][C]217.997686247616[/C][C]12.0023137523841[/C][/ROW]
[ROW][C]44[/C][C]242[/C][C]229.999206563509[/C][C]12.0007934364913[/C][/ROW]
[ROW][C]45[/C][C]209[/C][C]241.999206664012[/C][C]-32.9992066640121[/C][/ROW]
[ROW][C]46[/C][C]191[/C][C]209.00218147728[/C][C]-18.0021814772796[/C][/ROW]
[ROW][C]47[/C][C]172[/C][C]191.001190069515[/C][C]-19.0011900695152[/C][/ROW]
[ROW][C]48[/C][C]194[/C][C]172.001256110938[/C][C]21.9987438890624[/C][/ROW]
[ROW][C]49[/C][C]196[/C][C]193.998545729888[/C][C]2.00145427011205[/C][/ROW]
[ROW][C]50[/C][C]196[/C][C]195.99986768994[/C][C]0.000132310060081409[/C][/ROW]
[ROW][C]51[/C][C]236[/C][C]195.999999991253[/C][C]40.0000000087466[/C][/ROW]
[ROW][C]52[/C][C]235[/C][C]235.997355721545[/C][C]-0.997355721545347[/C][/ROW]
[ROW][C]53[/C][C]229[/C][C]235.000065932156[/C][C]-6.00006593215613[/C][/ROW]
[ROW][C]54[/C][C]243[/C][C]229.000396646127[/C][C]13.9996033538733[/C][/ROW]
[ROW][C]55[/C][C]264[/C][C]242.999074528762[/C][C]21.0009254712379[/C][/ROW]
[ROW][C]56[/C][C]272[/C][C]263.998611692632[/C][C]8.00138830736847[/C][/ROW]
[ROW][C]57[/C][C]237[/C][C]271.999471052532[/C][C]-34.9994710525324[/C][/ROW]
[ROW][C]58[/C][C]211[/C][C]237.00231370868[/C][C]-26.0023137086802[/C][/ROW]
[ROW][C]59[/C][C]180[/C][C]211.001718933947[/C][C]-31.0017189339474[/C][/ROW]
[ROW][C]60[/C][C]201[/C][C]180.002049429435[/C][C]20.9979505705646[/C][/ROW]
[ROW][C]61[/C][C]204[/C][C]200.998611889293[/C][C]3.00138811070684[/C][/ROW]
[ROW][C]62[/C][C]188[/C][C]203.999801587352[/C][C]-15.9998015873522[/C][/ROW]
[ROW][C]63[/C][C]235[/C][C]188.001057698265[/C][C]46.9989423017348[/C][/ROW]
[ROW][C]64[/C][C]227[/C][C]234.996893042738[/C][C]-7.99689304273767[/C][/ROW]
[ROW][C]65[/C][C]234[/C][C]227.000528650299[/C][C]6.99947134970068[/C][/ROW]
[ROW][C]66[/C][C]264[/C][C]233.999537286218[/C][C]30.000462713782[/C][/ROW]
[ROW][C]67[/C][C]302[/C][C]263.998016760571[/C][C]38.0019832394291[/C][/ROW]
[ROW][C]68[/C][C]293[/C][C]301.997487804363[/C][C]-8.99748780436272[/C][/ROW]
[ROW][C]69[/C][C]259[/C][C]293.000594796579[/C][C]-34.0005947965785[/C][/ROW]
[ROW][C]70[/C][C]229[/C][C]259.002247676006[/C][C]-30.0022476760062[/C][/ROW]
[ROW][C]71[/C][C]203[/C][C]229.001983357428[/C][C]-26.0019833574276[/C][/ROW]
[ROW][C]72[/C][C]229[/C][C]203.001718912109[/C][C]25.9982810878911[/C][/ROW]
[ROW][C]73[/C][C]242[/C][C]228.998281332637[/C][C]13.0017186673631[/C][/ROW]
[ROW][C]74[/C][C]233[/C][C]241.999140495887[/C][C]-8.99914049588654[/C][/ROW]
[ROW][C]75[/C][C]267[/C][C]233.000594905833[/C][C]33.999405094167[/C][/ROW]
[ROW][C]76[/C][C]269[/C][C]266.997752402641[/C][C]2.00224759735852[/C][/ROW]
[ROW][C]77[/C][C]270[/C][C]268.999867637495[/C][C]1.00013236250453[/C][/ROW]
[ROW][C]78[/C][C]315[/C][C]269.999933884289[/C][C]45.0000661157114[/C][/ROW]
[ROW][C]79[/C][C]364[/C][C]314.997025182368[/C][C]49.0029748176315[/C][/ROW]
[ROW][C]80[/C][C]347[/C][C]363.996760562238[/C][C]-16.9967605622376[/C][/ROW]
[ROW][C]81[/C][C]312[/C][C]347.001123604194[/C][C]-35.0011236041936[/C][/ROW]
[ROW][C]82[/C][C]274[/C][C]312.002313817925[/C][C]-38.0023138179254[/C][/ROW]
[ROW][C]83[/C][C]237[/C][C]274.002512217491[/C][C]-37.0025122174908[/C][/ROW]
[ROW][C]84[/C][C]278[/C][C]237.002446123645[/C][C]40.9975538763549[/C][/ROW]
[ROW][C]85[/C][C]284[/C][C]277.99728977629[/C][C]6.00271022370964[/C][/ROW]
[ROW][C]86[/C][C]277[/C][C]283.999603179067[/C][C]-6.99960317906726[/C][/ROW]
[ROW][C]87[/C][C]317[/C][C]277.000462722497[/C][C]39.9995372775032[/C][/ROW]
[ROW][C]88[/C][C]313[/C][C]316.997355752135[/C][C]-3.99735575213509[/C][/ROW]
[ROW][C]89[/C][C]318[/C][C]313.000264253042[/C][C]4.99973574695775[/C][/ROW]
[ROW][C]90[/C][C]374[/C][C]317.999669482662[/C][C]56.0003305173378[/C][/ROW]
[ROW][C]91[/C][C]413[/C][C]373.996297988315[/C][C]39.0037020116852[/C][/ROW]
[ROW][C]92[/C][C]405[/C][C]412.997421583779[/C][C]-7.99742158377853[/C][/ROW]
[ROW][C]93[/C][C]355[/C][C]405.00052868524[/C][C]-50.0005286852395[/C][/ROW]
[ROW][C]94[/C][C]306[/C][C]355.003305383017[/C][C]-49.0033053830174[/C][/ROW]
[ROW][C]95[/C][C]271[/C][C]306.003239459615[/C][C]-35.003239459615[/C][/ROW]
[ROW][C]96[/C][C]306[/C][C]271.002313957798[/C][C]34.9976860422018[/C][/ROW]
[ROW][C]97[/C][C]315[/C][C]305.997686409321[/C][C]9.00231359067863[/C][/ROW]
[ROW][C]98[/C][C]301[/C][C]314.999404884403[/C][C]-13.9994048844034[/C][/ROW]
[ROW][C]99[/C][C]356[/C][C]301.000925458118[/C][C]54.9990745418824[/C][/ROW]
[ROW][C]100[/C][C]348[/C][C]355.996364178305[/C][C]-7.99636417830487[/C][/ROW]
[ROW][C]101[/C][C]355[/C][C]348.000528615338[/C][C]6.99947138466234[/C][/ROW]
[ROW][C]102[/C][C]422[/C][C]354.999537286216[/C][C]67.0004627137843[/C][/ROW]
[ROW][C]103[/C][C]465[/C][C]421.995570803001[/C][C]43.0044291969992[/C][/ROW]
[ROW][C]104[/C][C]467[/C][C]464.997157107861[/C][C]2.00284289213886[/C][/ROW]
[ROW][C]105[/C][C]404[/C][C]466.999867598142[/C][C]-62.9998675981423[/C][/ROW]
[ROW][C]106[/C][C]347[/C][C]404.004164729812[/C][C]-57.0041647298125[/C][/ROW]
[ROW][C]107[/C][C]305[/C][C]347.003768372115[/C][C]-42.0037683721147[/C][/ROW]
[ROW][C]108[/C][C]336[/C][C]305.002776741492[/C][C]30.9972232585076[/C][/ROW]
[ROW][C]109[/C][C]340[/C][C]335.99795086776[/C][C]4.00204913223996[/C][/ROW]
[ROW][C]110[/C][C]318[/C][C]339.999735436693[/C][C]-21.9997354366927[/C][/ROW]
[ROW][C]111[/C][C]362[/C][C]318.00145433566[/C][C]43.9985456643398[/C][/ROW]
[ROW][C]112[/C][C]348[/C][C]361.997091389842[/C][C]-13.9970913898422[/C][/ROW]
[ROW][C]113[/C][C]363[/C][C]348.00092530518[/C][C]14.9990746948205[/C][/ROW]
[ROW][C]114[/C][C]435[/C][C]362.999008456749[/C][C]72.0009915432512[/C][/ROW]
[ROW][C]115[/C][C]491[/C][C]434.995240233235[/C][C]56.0047597667652[/C][/ROW]
[ROW][C]116[/C][C]505[/C][C]490.996297695511[/C][C]14.0037023044894[/C][/ROW]
[ROW][C]117[/C][C]404[/C][C]504.999074257793[/C][C]-100.999074257793[/C][/ROW]
[ROW][C]118[/C][C]359[/C][C]404.006676741899[/C][C]-45.0066767418986[/C][/ROW]
[ROW][C]119[/C][C]310[/C][C]359.00297525464[/C][C]-49.00297525464[/C][/ROW]
[ROW][C]120[/C][C]337[/C][C]310.003239437791[/C][C]26.9967605622087[/C][/ROW]
[ROW][C]121[/C][C]360[/C][C]336.998215326193[/C][C]23.0017846738072[/C][/ROW]
[ROW][C]122[/C][C]342[/C][C]359.99847942191[/C][C]-17.9984794219096[/C][/ROW]
[ROW][C]123[/C][C]406[/C][C]342.001189824784[/C][C]63.9988101752164[/C][/ROW]
[ROW][C]124[/C][C]396[/C][C]405.995769233129[/C][C]-9.99576923312918[/C][/ROW]
[ROW][C]125[/C][C]420[/C][C]396.00066078993[/C][C]23.9993392100696[/C][/ROW]
[ROW][C]126[/C][C]472[/C][C]419.99841347661[/C][C]52.0015865233896[/C][/ROW]
[ROW][C]127[/C][C]548[/C][C]471.996562333129[/C][C]76.0034376668706[/C][/ROW]
[ROW][C]128[/C][C]559[/C][C]547.994975643684[/C][C]11.0050243563164[/C][/ROW]
[ROW][C]129[/C][C]463[/C][C]558.99927249128[/C][C]-95.9992724912802[/C][/ROW]
[ROW][C]130[/C][C]407[/C][C]463.006346220196[/C][C]-56.0063462201964[/C][/ROW]
[ROW][C]131[/C][C]362[/C][C]407.003702409365[/C][C]-45.003702409365[/C][/ROW]
[ROW][C]132[/C][C]405[/C][C]362.002975058016[/C][C]42.9970249419841[/C][/ROW]
[ROW][C]133[/C][C]417[/C][C]404.997157597334[/C][C]12.0028424026661[/C][/ROW]
[ROW][C]134[/C][C]391[/C][C]416.999206528561[/C][C]-25.9992065285612[/C][/ROW]
[ROW][C]135[/C][C]419[/C][C]391.001718728541[/C][C]27.9982812714588[/C][/ROW]
[ROW][C]136[/C][C]461[/C][C]418.998149118702[/C][C]42.0018508812979[/C][/ROW]
[ROW][C]137[/C][C]472[/C][C]460.997223385267[/C][C]11.0027766147329[/C][/ROW]
[ROW][C]138[/C][C]535[/C][C]471.999272639872[/C][C]63.0007273601285[/C][/ROW]
[ROW][C]139[/C][C]622[/C][C]534.995835213351[/C][C]87.0041647866487[/C][/ROW]
[ROW][C]140[/C][C]606[/C][C]621.994248419041[/C][C]-15.994248419041[/C][/ROW]
[ROW][C]141[/C][C]508[/C][C]606.001057331162[/C][C]-98.0010573311621[/C][/ROW]
[ROW][C]142[/C][C]461[/C][C]508.006478552109[/C][C]-47.0064785521095[/C][/ROW]
[ROW][C]143[/C][C]390[/C][C]461.003107455461[/C][C]-71.003107455461[/C][/ROW]
[ROW][C]144[/C][C]432[/C][C]390.00469379968[/C][C]41.9953062003196[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319133&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319133&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
21181126
3132117.99960335823214.0003966417681
4129131.99907447632-2.99907447632032
5121129.0001982597-8.00019825970048
6135121.00052886879713.9994711312028
7148134.99907453750313.000925462497
8148147.9991405483230.000859451677087009
9136147.999999943184-11.9999999431843
10119136.000793283532-17.0007932835325
11104119.001123870785-15.0011238707846
12118104.00099167871613.999008321284
13115117.999074568098-2.99907456809794
14126115.00019825970710.9998017402934
15141125.99927283653115.0007271634685
16135140.999008347509-5.99900834750918
17125135.000396576213-10.000396576213
18149125.0006610958323.99933890417
19170148.99841347663121.0015865233694
20170169.9986116489310.00138835106861279
21158169.99999990822-11.9999999082203
22133158.00079328353-25.0007932835302
23114133.001652726475-19.0016527264754
24140114.00125614152225.9987438584776
25145139.9982813020455.00171869795543
26150144.9996693515755.00033064842467
27178149.99966944333528.0003305566649
28163177.99814898323-14.9981489832301
29172163.0009914820558.99900851794482
30178171.9994051028926.0005948971083
31199177.99960331890521.0003966810949
32199198.9986117275880.00138827241178774
33184198.999999908226-14.9999999082255
34162184.000991604414-22.0009916044142
35146162.001454418702-16.0014544187017
36166146.00105780752919.9989421924712
37171165.9986779307015.0013220692986
38180170.9996693777959.00033062220473
39193179.99940501549113.0005949845086
40181192.99914057017-11.9991405701698
41183181.0007932267221.99920677327808
42218182.99986783851535.0001321614849
43230217.99768624761612.0023137523841
44242229.99920656350912.0007934364913
45209241.999206664012-32.9992066640121
46191209.00218147728-18.0021814772796
47172191.001190069515-19.0011900695152
48194172.00125611093821.9987438890624
49196193.9985457298882.00145427011205
50196195.999867689940.000132310060081409
51236195.99999999125340.0000000087466
52235235.997355721545-0.997355721545347
53229235.000065932156-6.00006593215613
54243229.00039664612713.9996033538733
55264242.99907452876221.0009254712379
56272263.9986116926328.00138830736847
57237271.999471052532-34.9994710525324
58211237.00231370868-26.0023137086802
59180211.001718933947-31.0017189339474
60201180.00204942943520.9979505705646
61204200.9986118892933.00138811070684
62188203.999801587352-15.9998015873522
63235188.00105769826546.9989423017348
64227234.996893042738-7.99689304273767
65234227.0005286502996.99947134970068
66264233.99953728621830.000462713782
67302263.99801676057138.0019832394291
68293301.997487804363-8.99748780436272
69259293.000594796579-34.0005947965785
70229259.002247676006-30.0022476760062
71203229.001983357428-26.0019833574276
72229203.00171891210925.9982810878911
73242228.99828133263713.0017186673631
74233241.999140495887-8.99914049588654
75267233.00059490583333.999405094167
76269266.9977524026412.00224759735852
77270268.9998676374951.00013236250453
78315269.99993388428945.0000661157114
79364314.99702518236849.0029748176315
80347363.996760562238-16.9967605622376
81312347.001123604194-35.0011236041936
82274312.002313817925-38.0023138179254
83237274.002512217491-37.0025122174908
84278237.00244612364540.9975538763549
85284277.997289776296.00271022370964
86277283.999603179067-6.99960317906726
87317277.00046272249739.9995372775032
88313316.997355752135-3.99735575213509
89318313.0002642530424.99973574695775
90374317.99966948266256.0003305173378
91413373.99629798831539.0037020116852
92405412.997421583779-7.99742158377853
93355405.00052868524-50.0005286852395
94306355.003305383017-49.0033053830174
95271306.003239459615-35.003239459615
96306271.00231395779834.9976860422018
97315305.9976864093219.00231359067863
98301314.999404884403-13.9994048844034
99356301.00092545811854.9990745418824
100348355.996364178305-7.99636417830487
101355348.0005286153386.99947138466234
102422354.99953728621667.0004627137843
103465421.99557080300143.0044291969992
104467464.9971571078612.00284289213886
105404466.999867598142-62.9998675981423
106347404.004164729812-57.0041647298125
107305347.003768372115-42.0037683721147
108336305.00277674149230.9972232585076
109340335.997950867764.00204913223996
110318339.999735436693-21.9997354366927
111362318.0014543356643.9985456643398
112348361.997091389842-13.9970913898422
113363348.0009253051814.9990746948205
114435362.99900845674972.0009915432512
115491434.99524023323556.0047597667652
116505490.99629769551114.0037023044894
117404504.999074257793-100.999074257793
118359404.006676741899-45.0066767418986
119310359.00297525464-49.00297525464
120337310.00323943779126.9967605622087
121360336.99821532619323.0017846738072
122342359.99847942191-17.9984794219096
123406342.00118982478463.9988101752164
124396405.995769233129-9.99576923312918
125420396.0006607899323.9993392100696
126472419.9984134766152.0015865233896
127548471.99656233312976.0034376668706
128559547.99497564368411.0050243563164
129463558.99927249128-95.9992724912802
130407463.006346220196-56.0063462201964
131362407.003702409365-45.003702409365
132405362.00297505801642.9970249419841
133417404.99715759733412.0028424026661
134391416.999206528561-25.9992065285612
135419391.00171872854127.9982812714588
136461418.99814911870242.0018508812979
137472460.99722338526711.0027766147329
138535471.99927263987263.0007273601285
139622534.99583521335187.0041647866487
140606621.994248419041-15.994248419041
141508606.001057331162-98.0010573311621
142461508.006478552109-47.0064785521095
143390461.003107455461-71.003107455461
144432390.0046937996841.9953062003196







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
145431.997223817916365.838723296861498.155724338972
146431.997223817916338.43806762164525.556380014192
147431.997223817916317.412389633265546.582058002567
148431.997223817916299.686783027748564.307664608084
149431.997223817916284.070142933268579.924304702565
150431.997223817916269.951582790402594.04286484543
151431.997223817916256.968202530066607.026245105766
152431.997223817916244.883550319832619.110897316
153431.997223817916233.533384978413630.461062657419
154431.997223817916222.798122854559641.196324781273
155431.997223817916212.587487568948651.406960066884
156431.997223817916202.831343116171661.163104519661

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
145 & 431.997223817916 & 365.838723296861 & 498.155724338972 \tabularnewline
146 & 431.997223817916 & 338.43806762164 & 525.556380014192 \tabularnewline
147 & 431.997223817916 & 317.412389633265 & 546.582058002567 \tabularnewline
148 & 431.997223817916 & 299.686783027748 & 564.307664608084 \tabularnewline
149 & 431.997223817916 & 284.070142933268 & 579.924304702565 \tabularnewline
150 & 431.997223817916 & 269.951582790402 & 594.04286484543 \tabularnewline
151 & 431.997223817916 & 256.968202530066 & 607.026245105766 \tabularnewline
152 & 431.997223817916 & 244.883550319832 & 619.110897316 \tabularnewline
153 & 431.997223817916 & 233.533384978413 & 630.461062657419 \tabularnewline
154 & 431.997223817916 & 222.798122854559 & 641.196324781273 \tabularnewline
155 & 431.997223817916 & 212.587487568948 & 651.406960066884 \tabularnewline
156 & 431.997223817916 & 202.831343116171 & 661.163104519661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319133&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]431.997223817916[/C][C]365.838723296861[/C][C]498.155724338972[/C][/ROW]
[ROW][C]146[/C][C]431.997223817916[/C][C]338.43806762164[/C][C]525.556380014192[/C][/ROW]
[ROW][C]147[/C][C]431.997223817916[/C][C]317.412389633265[/C][C]546.582058002567[/C][/ROW]
[ROW][C]148[/C][C]431.997223817916[/C][C]299.686783027748[/C][C]564.307664608084[/C][/ROW]
[ROW][C]149[/C][C]431.997223817916[/C][C]284.070142933268[/C][C]579.924304702565[/C][/ROW]
[ROW][C]150[/C][C]431.997223817916[/C][C]269.951582790402[/C][C]594.04286484543[/C][/ROW]
[ROW][C]151[/C][C]431.997223817916[/C][C]256.968202530066[/C][C]607.026245105766[/C][/ROW]
[ROW][C]152[/C][C]431.997223817916[/C][C]244.883550319832[/C][C]619.110897316[/C][/ROW]
[ROW][C]153[/C][C]431.997223817916[/C][C]233.533384978413[/C][C]630.461062657419[/C][/ROW]
[ROW][C]154[/C][C]431.997223817916[/C][C]222.798122854559[/C][C]641.196324781273[/C][/ROW]
[ROW][C]155[/C][C]431.997223817916[/C][C]212.587487568948[/C][C]651.406960066884[/C][/ROW]
[ROW][C]156[/C][C]431.997223817916[/C][C]202.831343116171[/C][C]661.163104519661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319133&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319133&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
145431.997223817916365.838723296861498.155724338972
146431.997223817916338.43806762164525.556380014192
147431.997223817916317.412389633265546.582058002567
148431.997223817916299.686783027748564.307664608084
149431.997223817916284.070142933268579.924304702565
150431.997223817916269.951582790402594.04286484543
151431.997223817916256.968202530066607.026245105766
152431.997223817916244.883550319832619.110897316
153431.997223817916233.533384978413630.461062657419
154431.997223817916222.798122854559641.196324781273
155431.997223817916212.587487568948651.406960066884
156431.997223817916202.831343116171661.163104519661



Parameters (Session):
par1 = 12 ; par2 = Single ; par3 = additive ; par4 = 12 ;
Parameters (R input):
par1 = 12 ; par2 = Single ; par3 = additive ; par4 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par4 <- as.numeric(par4)
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$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par4, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$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')