Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationMon, 31 May 2010 17:01:33 +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/May/31/t1275325481n0rwu902g2ccbsv.htm/, Retrieved Thu, 31 Oct 2024 23:32:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=76766, Retrieved Thu, 31 Oct 2024 23:32:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W62
Estimated Impact161
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [Paper - Aantal li...] [2010-05-31 17:01:33] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
464
675
703
887
1139
1077
1318
1260
1120
963
996
960
530
883
894
1045
1199
1287
1565
1577
1076
918
1008
1063
544
635
804
980
1018
1064
1404
1286
1104
999
996
1015
615
722
832
977
1270
1437
1520
1708
1151
934
1159
1209
699
830
996
1124
1458
1270
1753
2258
1208
1241
1265
1828
809
997
1164
1205
1538
1513
1378
2083
1357
1536
1526
1376
779
1005
1193
1522
1539
1546
2116
2326
1596
1356
1553
1613
814
1150
1225
1691
1759
1754
2100
2062
2012
1897
1964
2186
966
1549
1538
1612
2078
2137
2907
2249
1883
1739
1828
1868
1138
1430
1809
1763
2200
2067
2503
2141
2103
1972
2181
2344
970
1199
1718
1683
2025
2051
2439
2353
2230
1852
2147
2286
1007
1665
1642
1518
1831
2207
2822
2393
2306
1785
2047
2171
1212
1335
2011
1860
1954
2152
2835
2224
2182
1992
2389
2724
891
1247
2017
2257
2255
2255
3057
3330
1896
2096
2374
2535
1041
1728
2201
2455
2204
2660
3670
2665
2639
2226
2586
2684
1185
1749
2459
2618
2585
3310
3923




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 2 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76766&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76766&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76766&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 time2 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.128930926063041
beta0
gamma0.318961787795898

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.128930926063041 \tabularnewline
beta & 0 \tabularnewline
gamma & 0.318961787795898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76766&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]0.128930926063041[/C][/ROW]
[ROW][C]beta[/C][C]0[/C][/ROW]
[ROW][C]gamma[/C][C]0.318961787795898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76766&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76766&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
alpha0.128930926063041
beta0
gamma0.318961787795898







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
13530488.83695900046341.163040999537
14883814.75523893239868.2447610676024
15894834.61852403814659.3814759618537
161045999.13603200943145.8639679905687
1711991167.5174428106331.482557189365
1812871265.7678925140121.2321074859897
1915651471.5441169062193.4558830937872
2015771416.67395034437160.326049655626
2110761269.32628721627-193.326287216273
229181065.59758763115-147.59758763115
2310081084.10736657534-76.1073665753393
2410631034.9518793466728.0481206533341
25544581.982552876673-37.9825528766735
26635950.01704297119-315.01704297119
27804917.750504008931-113.750504008931
289801063.42543153308-83.425431533082
2910181216.09603740949-198.096037409493
3010641282.55780969-218.557809689996
3114041474.02347356886-70.0234735688623
3212861417.91458147564-131.914581475643
3311041148.84690724577-44.8469072457665
34999983.31927443459815.6807255654018
359961041.62335100716-45.6233510071593
3610151025.66177783381-10.661777833812
37615559.4112478402855.58875215972
38722858.653333515284-136.653333515284
39832906.056318391444-74.0563183914444
409771069.55862086222-92.558620862217
4112701191.2360260260578.7639739739534
4214371290.50915889254146.490841107455
4315201593.93855847725-73.9385584772474
4417081512.02680738276195.973192617235
4511511278.88506606663-127.88506606663
469341101.55325635511-167.553256355113
4711591121.7000205159537.2999794840518
4812091125.0045063484283.9954936515812
49699638.41288507032560.5871149296751
50830909.400985151323-79.4009851513226
51996990.0938507714375.90614922856298
5211241179.04487730169-55.044877301686
5314581375.8299184303282.1700815696847
5412701506.27911116514-236.279111165137
5517531718.3139041886134.6860958113941
5622581723.10131469528534.898685304722
5712081397.79915781359-189.799157813591
5812411178.6847664400162.3152335599891
5912651298.32903611964-33.3290361196446
6018281305.30176605924522.698233940756
61809774.56118204674334.4388179532569
629971040.77010167047-43.7701016704652
6311641168.750736122-4.75073612200322
6412051367.69835490318-162.698354903181
6515381626.18580375516-88.1858037551608
6615131647.32610180476-134.326101804765
6713781994.84739817591-616.847398175911
6820832061.2178209406321.7821790593721
6913571428.82192211009-71.8219221100858
7015361284.72546884146251.274531158537
7115261409.0793621541116.920637845899
7213761598.65940088833-222.659400888332
73779810.737142731786-31.7371427317856
7410051051.93266238054-46.9326623805407
7511931193.37607240146-0.376072401463716
7615221351.58581532865170.414184671352
7715391689.45800268104-150.458002681037
7815461689.68549575823-143.685495758233
7921161905.13461397818210.865386021819
8023262288.7565481025937.2434518974096
8115961559.5920370507736.4079629492337
8213561510.71249582127-154.712495821273
8315531548.271436262074.72856373793252
8416131632.16622149011-19.1662214901114
85814866.034135880248-52.0341358802476
8611501118.2730043574531.7269956425484
8712251295.90142116044-70.9014211604399
8816911506.94089288937184.059107110631
8917591773.46513372944-14.4651337294404
9017541793.43097722-39.4309772200031
9121002150.52505344622-50.5250534462157
9220622474.58202317733-412.582023177332
9320121649.1346153853362.865384614701
9418971579.57425173296317.425748267038
9519641733.10680944282230.893190557183
9621861848.4195148665337.580485133498
97966990.716000674304-24.7160006743045
9815491316.0008105535232.999189446502
9915381516.7255264078221.2744735921835
10016121865.30779150914-253.30779150914
10120782048.0874998808829.912500119121
10221372067.2207979543669.7792020456363
10329072493.16925096452413.830749035475
10422492815.31344148215-566.313441482155
10518832074.76570147992-191.765701479922
10617391898.77284610268-159.772846102682
10718281973.17415285425-145.174152854252
10818682074.50887478497-206.508874784973
10911381014.42752648855123.572473511447
11014301448.76169452103-18.761694521033
11118091560.78133262785248.218667372154
11217631872.46637985904-109.466379859042
11322002167.8289886827532.1710113172498
11420672198.99757413638-131.997574136377
11525032712.5061091222-209.506109122202
11621412677.56580949326-536.565809493257
11721032039.0539358569663.9460641430376
11819721900.5402223616271.4597776383785
11921812012.03876025337168.961239746629
12023442141.73511193064202.26488806936
1219701141.04354334108-171.043543341078
12211991516.72816114098-317.728161140977
12317181667.2649051732250.7350948267826
12416831855.10955042205-172.109550422053
12520252182.6473164392-157.647316439195
12620512143.70912748381-92.7091274838067
12724392637.04671442342-198.046714423421
12823532509.46343944918-156.463439449184
12922302081.6786838611148.321316138897
13018521953.3393933984-101.339393398398
13121472069.793257034177.2067429658987
13222862196.1855440394289.8144559605767
13310071084.64792832793-77.6479283279318
13416651430.19404726692234.805952733082
13516421769.23922025064-127.239220250645
13615181875.82278453686-357.82278453686
13718312191.54539780047-360.545397800471
13822072144.185288992162.8147110079021
13928222637.89742440654184.102575593462
14023932566.47025484257-173.470254842569
14123062207.317854147698.6821458523982
14217851994.72252902757-209.722529027573
14320472151.5657479414-104.565747941396
14421712259.77921774681-88.7792177468114
14512121070.26508143284141.734918567159
14613351544.13335047299-209.133350472994
14720111723.90986670662287.090133293384
14818601818.5252811856541.4747188143513
14919542201.59379662451-247.593796624513
15021522292.73539533376-140.735395333763
15128352817.7963195002117.2036804997865
15222242616.82823281662-392.828232816615
15321822297.14668489139-115.146684891395
15419921965.4977454508326.5022545491749
15523892188.10276913206200.897230867938
15627242345.61843415765378.381565842351
1578911193.66533165031-302.665331650305
15812471519.99089775424-272.990897754237
15920171833.6017823691183.398217630903
16022571846.38991421296410.610085787036
16122552205.6598829930749.3401170069328
16222552371.89557184747-116.895571847472
16330572974.9589710768282.0410289231831
16433302647.35367654447682.646323455534
16518962520.14220101209-624.142201012089
16620962137.97226036437-41.9722603643736
16723742419.71801410653-45.7180141065269
16825352603.13644850773-68.1364485077324
16910411148.50094503261-107.500945032608
17017281528.24102368982199.75897631018
17122012076.81793642793124.182063572066
17224552145.67557931211309.324420687888
17322042408.11543436068-204.115434360685
17426602502.4100837042157.589916295803
17536703252.23288577993417.767114220065
17626653109.72008884198-444.720088841978
17726392442.44363392995196.556366070055
17822262314.42316529854-88.4231652985432
17925862612.65435638818-26.6543563881764
18026842806.99681258428-122.996812584276
18111851211.47427616189-26.4742761618943
18217491730.0709769897818.929023010221
18324592271.62383173616187.37616826384
18426182405.78894534092212.211054659084
18525852517.2877721545867.7122278454153
18633102764.81400431411545.185995685893
18739233717.0807290368205.919270963203

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 530 & 488.836959000463 & 41.163040999537 \tabularnewline
14 & 883 & 814.755238932398 & 68.2447610676024 \tabularnewline
15 & 894 & 834.618524038146 & 59.3814759618537 \tabularnewline
16 & 1045 & 999.136032009431 & 45.8639679905687 \tabularnewline
17 & 1199 & 1167.51744281063 & 31.482557189365 \tabularnewline
18 & 1287 & 1265.76789251401 & 21.2321074859897 \tabularnewline
19 & 1565 & 1471.54411690621 & 93.4558830937872 \tabularnewline
20 & 1577 & 1416.67395034437 & 160.326049655626 \tabularnewline
21 & 1076 & 1269.32628721627 & -193.326287216273 \tabularnewline
22 & 918 & 1065.59758763115 & -147.59758763115 \tabularnewline
23 & 1008 & 1084.10736657534 & -76.1073665753393 \tabularnewline
24 & 1063 & 1034.95187934667 & 28.0481206533341 \tabularnewline
25 & 544 & 581.982552876673 & -37.9825528766735 \tabularnewline
26 & 635 & 950.01704297119 & -315.01704297119 \tabularnewline
27 & 804 & 917.750504008931 & -113.750504008931 \tabularnewline
28 & 980 & 1063.42543153308 & -83.425431533082 \tabularnewline
29 & 1018 & 1216.09603740949 & -198.096037409493 \tabularnewline
30 & 1064 & 1282.55780969 & -218.557809689996 \tabularnewline
31 & 1404 & 1474.02347356886 & -70.0234735688623 \tabularnewline
32 & 1286 & 1417.91458147564 & -131.914581475643 \tabularnewline
33 & 1104 & 1148.84690724577 & -44.8469072457665 \tabularnewline
34 & 999 & 983.319274434598 & 15.6807255654018 \tabularnewline
35 & 996 & 1041.62335100716 & -45.6233510071593 \tabularnewline
36 & 1015 & 1025.66177783381 & -10.661777833812 \tabularnewline
37 & 615 & 559.41124784028 & 55.58875215972 \tabularnewline
38 & 722 & 858.653333515284 & -136.653333515284 \tabularnewline
39 & 832 & 906.056318391444 & -74.0563183914444 \tabularnewline
40 & 977 & 1069.55862086222 & -92.558620862217 \tabularnewline
41 & 1270 & 1191.23602602605 & 78.7639739739534 \tabularnewline
42 & 1437 & 1290.50915889254 & 146.490841107455 \tabularnewline
43 & 1520 & 1593.93855847725 & -73.9385584772474 \tabularnewline
44 & 1708 & 1512.02680738276 & 195.973192617235 \tabularnewline
45 & 1151 & 1278.88506606663 & -127.88506606663 \tabularnewline
46 & 934 & 1101.55325635511 & -167.553256355113 \tabularnewline
47 & 1159 & 1121.70002051595 & 37.2999794840518 \tabularnewline
48 & 1209 & 1125.00450634842 & 83.9954936515812 \tabularnewline
49 & 699 & 638.412885070325 & 60.5871149296751 \tabularnewline
50 & 830 & 909.400985151323 & -79.4009851513226 \tabularnewline
51 & 996 & 990.093850771437 & 5.90614922856298 \tabularnewline
52 & 1124 & 1179.04487730169 & -55.044877301686 \tabularnewline
53 & 1458 & 1375.82991843032 & 82.1700815696847 \tabularnewline
54 & 1270 & 1506.27911116514 & -236.279111165137 \tabularnewline
55 & 1753 & 1718.31390418861 & 34.6860958113941 \tabularnewline
56 & 2258 & 1723.10131469528 & 534.898685304722 \tabularnewline
57 & 1208 & 1397.79915781359 & -189.799157813591 \tabularnewline
58 & 1241 & 1178.68476644001 & 62.3152335599891 \tabularnewline
59 & 1265 & 1298.32903611964 & -33.3290361196446 \tabularnewline
60 & 1828 & 1305.30176605924 & 522.698233940756 \tabularnewline
61 & 809 & 774.561182046743 & 34.4388179532569 \tabularnewline
62 & 997 & 1040.77010167047 & -43.7701016704652 \tabularnewline
63 & 1164 & 1168.750736122 & -4.75073612200322 \tabularnewline
64 & 1205 & 1367.69835490318 & -162.698354903181 \tabularnewline
65 & 1538 & 1626.18580375516 & -88.1858037551608 \tabularnewline
66 & 1513 & 1647.32610180476 & -134.326101804765 \tabularnewline
67 & 1378 & 1994.84739817591 & -616.847398175911 \tabularnewline
68 & 2083 & 2061.21782094063 & 21.7821790593721 \tabularnewline
69 & 1357 & 1428.82192211009 & -71.8219221100858 \tabularnewline
70 & 1536 & 1284.72546884146 & 251.274531158537 \tabularnewline
71 & 1526 & 1409.0793621541 & 116.920637845899 \tabularnewline
72 & 1376 & 1598.65940088833 & -222.659400888332 \tabularnewline
73 & 779 & 810.737142731786 & -31.7371427317856 \tabularnewline
74 & 1005 & 1051.93266238054 & -46.9326623805407 \tabularnewline
75 & 1193 & 1193.37607240146 & -0.376072401463716 \tabularnewline
76 & 1522 & 1351.58581532865 & 170.414184671352 \tabularnewline
77 & 1539 & 1689.45800268104 & -150.458002681037 \tabularnewline
78 & 1546 & 1689.68549575823 & -143.685495758233 \tabularnewline
79 & 2116 & 1905.13461397818 & 210.865386021819 \tabularnewline
80 & 2326 & 2288.75654810259 & 37.2434518974096 \tabularnewline
81 & 1596 & 1559.59203705077 & 36.4079629492337 \tabularnewline
82 & 1356 & 1510.71249582127 & -154.712495821273 \tabularnewline
83 & 1553 & 1548.27143626207 & 4.72856373793252 \tabularnewline
84 & 1613 & 1632.16622149011 & -19.1662214901114 \tabularnewline
85 & 814 & 866.034135880248 & -52.0341358802476 \tabularnewline
86 & 1150 & 1118.27300435745 & 31.7269956425484 \tabularnewline
87 & 1225 & 1295.90142116044 & -70.9014211604399 \tabularnewline
88 & 1691 & 1506.94089288937 & 184.059107110631 \tabularnewline
89 & 1759 & 1773.46513372944 & -14.4651337294404 \tabularnewline
90 & 1754 & 1793.43097722 & -39.4309772200031 \tabularnewline
91 & 2100 & 2150.52505344622 & -50.5250534462157 \tabularnewline
92 & 2062 & 2474.58202317733 & -412.582023177332 \tabularnewline
93 & 2012 & 1649.1346153853 & 362.865384614701 \tabularnewline
94 & 1897 & 1579.57425173296 & 317.425748267038 \tabularnewline
95 & 1964 & 1733.10680944282 & 230.893190557183 \tabularnewline
96 & 2186 & 1848.4195148665 & 337.580485133498 \tabularnewline
97 & 966 & 990.716000674304 & -24.7160006743045 \tabularnewline
98 & 1549 & 1316.0008105535 & 232.999189446502 \tabularnewline
99 & 1538 & 1516.72552640782 & 21.2744735921835 \tabularnewline
100 & 1612 & 1865.30779150914 & -253.30779150914 \tabularnewline
101 & 2078 & 2048.08749988088 & 29.912500119121 \tabularnewline
102 & 2137 & 2067.22079795436 & 69.7792020456363 \tabularnewline
103 & 2907 & 2493.16925096452 & 413.830749035475 \tabularnewline
104 & 2249 & 2815.31344148215 & -566.313441482155 \tabularnewline
105 & 1883 & 2074.76570147992 & -191.765701479922 \tabularnewline
106 & 1739 & 1898.77284610268 & -159.772846102682 \tabularnewline
107 & 1828 & 1973.17415285425 & -145.174152854252 \tabularnewline
108 & 1868 & 2074.50887478497 & -206.508874784973 \tabularnewline
109 & 1138 & 1014.42752648855 & 123.572473511447 \tabularnewline
110 & 1430 & 1448.76169452103 & -18.761694521033 \tabularnewline
111 & 1809 & 1560.78133262785 & 248.218667372154 \tabularnewline
112 & 1763 & 1872.46637985904 & -109.466379859042 \tabularnewline
113 & 2200 & 2167.82898868275 & 32.1710113172498 \tabularnewline
114 & 2067 & 2198.99757413638 & -131.997574136377 \tabularnewline
115 & 2503 & 2712.5061091222 & -209.506109122202 \tabularnewline
116 & 2141 & 2677.56580949326 & -536.565809493257 \tabularnewline
117 & 2103 & 2039.05393585696 & 63.9460641430376 \tabularnewline
118 & 1972 & 1900.54022236162 & 71.4597776383785 \tabularnewline
119 & 2181 & 2012.03876025337 & 168.961239746629 \tabularnewline
120 & 2344 & 2141.73511193064 & 202.26488806936 \tabularnewline
121 & 970 & 1141.04354334108 & -171.043543341078 \tabularnewline
122 & 1199 & 1516.72816114098 & -317.728161140977 \tabularnewline
123 & 1718 & 1667.26490517322 & 50.7350948267826 \tabularnewline
124 & 1683 & 1855.10955042205 & -172.109550422053 \tabularnewline
125 & 2025 & 2182.6473164392 & -157.647316439195 \tabularnewline
126 & 2051 & 2143.70912748381 & -92.7091274838067 \tabularnewline
127 & 2439 & 2637.04671442342 & -198.046714423421 \tabularnewline
128 & 2353 & 2509.46343944918 & -156.463439449184 \tabularnewline
129 & 2230 & 2081.6786838611 & 148.321316138897 \tabularnewline
130 & 1852 & 1953.3393933984 & -101.339393398398 \tabularnewline
131 & 2147 & 2069.7932570341 & 77.2067429658987 \tabularnewline
132 & 2286 & 2196.18554403942 & 89.8144559605767 \tabularnewline
133 & 1007 & 1084.64792832793 & -77.6479283279318 \tabularnewline
134 & 1665 & 1430.19404726692 & 234.805952733082 \tabularnewline
135 & 1642 & 1769.23922025064 & -127.239220250645 \tabularnewline
136 & 1518 & 1875.82278453686 & -357.82278453686 \tabularnewline
137 & 1831 & 2191.54539780047 & -360.545397800471 \tabularnewline
138 & 2207 & 2144.1852889921 & 62.8147110079021 \tabularnewline
139 & 2822 & 2637.89742440654 & 184.102575593462 \tabularnewline
140 & 2393 & 2566.47025484257 & -173.470254842569 \tabularnewline
141 & 2306 & 2207.3178541476 & 98.6821458523982 \tabularnewline
142 & 1785 & 1994.72252902757 & -209.722529027573 \tabularnewline
143 & 2047 & 2151.5657479414 & -104.565747941396 \tabularnewline
144 & 2171 & 2259.77921774681 & -88.7792177468114 \tabularnewline
145 & 1212 & 1070.26508143284 & 141.734918567159 \tabularnewline
146 & 1335 & 1544.13335047299 & -209.133350472994 \tabularnewline
147 & 2011 & 1723.90986670662 & 287.090133293384 \tabularnewline
148 & 1860 & 1818.52528118565 & 41.4747188143513 \tabularnewline
149 & 1954 & 2201.59379662451 & -247.593796624513 \tabularnewline
150 & 2152 & 2292.73539533376 & -140.735395333763 \tabularnewline
151 & 2835 & 2817.79631950021 & 17.2036804997865 \tabularnewline
152 & 2224 & 2616.82823281662 & -392.828232816615 \tabularnewline
153 & 2182 & 2297.14668489139 & -115.146684891395 \tabularnewline
154 & 1992 & 1965.49774545083 & 26.5022545491749 \tabularnewline
155 & 2389 & 2188.10276913206 & 200.897230867938 \tabularnewline
156 & 2724 & 2345.61843415765 & 378.381565842351 \tabularnewline
157 & 891 & 1193.66533165031 & -302.665331650305 \tabularnewline
158 & 1247 & 1519.99089775424 & -272.990897754237 \tabularnewline
159 & 2017 & 1833.6017823691 & 183.398217630903 \tabularnewline
160 & 2257 & 1846.38991421296 & 410.610085787036 \tabularnewline
161 & 2255 & 2205.65988299307 & 49.3401170069328 \tabularnewline
162 & 2255 & 2371.89557184747 & -116.895571847472 \tabularnewline
163 & 3057 & 2974.95897107682 & 82.0410289231831 \tabularnewline
164 & 3330 & 2647.35367654447 & 682.646323455534 \tabularnewline
165 & 1896 & 2520.14220101209 & -624.142201012089 \tabularnewline
166 & 2096 & 2137.97226036437 & -41.9722603643736 \tabularnewline
167 & 2374 & 2419.71801410653 & -45.7180141065269 \tabularnewline
168 & 2535 & 2603.13644850773 & -68.1364485077324 \tabularnewline
169 & 1041 & 1148.50094503261 & -107.500945032608 \tabularnewline
170 & 1728 & 1528.24102368982 & 199.75897631018 \tabularnewline
171 & 2201 & 2076.81793642793 & 124.182063572066 \tabularnewline
172 & 2455 & 2145.67557931211 & 309.324420687888 \tabularnewline
173 & 2204 & 2408.11543436068 & -204.115434360685 \tabularnewline
174 & 2660 & 2502.4100837042 & 157.589916295803 \tabularnewline
175 & 3670 & 3252.23288577993 & 417.767114220065 \tabularnewline
176 & 2665 & 3109.72008884198 & -444.720088841978 \tabularnewline
177 & 2639 & 2442.44363392995 & 196.556366070055 \tabularnewline
178 & 2226 & 2314.42316529854 & -88.4231652985432 \tabularnewline
179 & 2586 & 2612.65435638818 & -26.6543563881764 \tabularnewline
180 & 2684 & 2806.99681258428 & -122.996812584276 \tabularnewline
181 & 1185 & 1211.47427616189 & -26.4742761618943 \tabularnewline
182 & 1749 & 1730.07097698978 & 18.929023010221 \tabularnewline
183 & 2459 & 2271.62383173616 & 187.37616826384 \tabularnewline
184 & 2618 & 2405.78894534092 & 212.211054659084 \tabularnewline
185 & 2585 & 2517.28777215458 & 67.7122278454153 \tabularnewline
186 & 3310 & 2764.81400431411 & 545.185995685893 \tabularnewline
187 & 3923 & 3717.0807290368 & 205.919270963203 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76766&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]530[/C][C]488.836959000463[/C][C]41.163040999537[/C][/ROW]
[ROW][C]14[/C][C]883[/C][C]814.755238932398[/C][C]68.2447610676024[/C][/ROW]
[ROW][C]15[/C][C]894[/C][C]834.618524038146[/C][C]59.3814759618537[/C][/ROW]
[ROW][C]16[/C][C]1045[/C][C]999.136032009431[/C][C]45.8639679905687[/C][/ROW]
[ROW][C]17[/C][C]1199[/C][C]1167.51744281063[/C][C]31.482557189365[/C][/ROW]
[ROW][C]18[/C][C]1287[/C][C]1265.76789251401[/C][C]21.2321074859897[/C][/ROW]
[ROW][C]19[/C][C]1565[/C][C]1471.54411690621[/C][C]93.4558830937872[/C][/ROW]
[ROW][C]20[/C][C]1577[/C][C]1416.67395034437[/C][C]160.326049655626[/C][/ROW]
[ROW][C]21[/C][C]1076[/C][C]1269.32628721627[/C][C]-193.326287216273[/C][/ROW]
[ROW][C]22[/C][C]918[/C][C]1065.59758763115[/C][C]-147.59758763115[/C][/ROW]
[ROW][C]23[/C][C]1008[/C][C]1084.10736657534[/C][C]-76.1073665753393[/C][/ROW]
[ROW][C]24[/C][C]1063[/C][C]1034.95187934667[/C][C]28.0481206533341[/C][/ROW]
[ROW][C]25[/C][C]544[/C][C]581.982552876673[/C][C]-37.9825528766735[/C][/ROW]
[ROW][C]26[/C][C]635[/C][C]950.01704297119[/C][C]-315.01704297119[/C][/ROW]
[ROW][C]27[/C][C]804[/C][C]917.750504008931[/C][C]-113.750504008931[/C][/ROW]
[ROW][C]28[/C][C]980[/C][C]1063.42543153308[/C][C]-83.425431533082[/C][/ROW]
[ROW][C]29[/C][C]1018[/C][C]1216.09603740949[/C][C]-198.096037409493[/C][/ROW]
[ROW][C]30[/C][C]1064[/C][C]1282.55780969[/C][C]-218.557809689996[/C][/ROW]
[ROW][C]31[/C][C]1404[/C][C]1474.02347356886[/C][C]-70.0234735688623[/C][/ROW]
[ROW][C]32[/C][C]1286[/C][C]1417.91458147564[/C][C]-131.914581475643[/C][/ROW]
[ROW][C]33[/C][C]1104[/C][C]1148.84690724577[/C][C]-44.8469072457665[/C][/ROW]
[ROW][C]34[/C][C]999[/C][C]983.319274434598[/C][C]15.6807255654018[/C][/ROW]
[ROW][C]35[/C][C]996[/C][C]1041.62335100716[/C][C]-45.6233510071593[/C][/ROW]
[ROW][C]36[/C][C]1015[/C][C]1025.66177783381[/C][C]-10.661777833812[/C][/ROW]
[ROW][C]37[/C][C]615[/C][C]559.41124784028[/C][C]55.58875215972[/C][/ROW]
[ROW][C]38[/C][C]722[/C][C]858.653333515284[/C][C]-136.653333515284[/C][/ROW]
[ROW][C]39[/C][C]832[/C][C]906.056318391444[/C][C]-74.0563183914444[/C][/ROW]
[ROW][C]40[/C][C]977[/C][C]1069.55862086222[/C][C]-92.558620862217[/C][/ROW]
[ROW][C]41[/C][C]1270[/C][C]1191.23602602605[/C][C]78.7639739739534[/C][/ROW]
[ROW][C]42[/C][C]1437[/C][C]1290.50915889254[/C][C]146.490841107455[/C][/ROW]
[ROW][C]43[/C][C]1520[/C][C]1593.93855847725[/C][C]-73.9385584772474[/C][/ROW]
[ROW][C]44[/C][C]1708[/C][C]1512.02680738276[/C][C]195.973192617235[/C][/ROW]
[ROW][C]45[/C][C]1151[/C][C]1278.88506606663[/C][C]-127.88506606663[/C][/ROW]
[ROW][C]46[/C][C]934[/C][C]1101.55325635511[/C][C]-167.553256355113[/C][/ROW]
[ROW][C]47[/C][C]1159[/C][C]1121.70002051595[/C][C]37.2999794840518[/C][/ROW]
[ROW][C]48[/C][C]1209[/C][C]1125.00450634842[/C][C]83.9954936515812[/C][/ROW]
[ROW][C]49[/C][C]699[/C][C]638.412885070325[/C][C]60.5871149296751[/C][/ROW]
[ROW][C]50[/C][C]830[/C][C]909.400985151323[/C][C]-79.4009851513226[/C][/ROW]
[ROW][C]51[/C][C]996[/C][C]990.093850771437[/C][C]5.90614922856298[/C][/ROW]
[ROW][C]52[/C][C]1124[/C][C]1179.04487730169[/C][C]-55.044877301686[/C][/ROW]
[ROW][C]53[/C][C]1458[/C][C]1375.82991843032[/C][C]82.1700815696847[/C][/ROW]
[ROW][C]54[/C][C]1270[/C][C]1506.27911116514[/C][C]-236.279111165137[/C][/ROW]
[ROW][C]55[/C][C]1753[/C][C]1718.31390418861[/C][C]34.6860958113941[/C][/ROW]
[ROW][C]56[/C][C]2258[/C][C]1723.10131469528[/C][C]534.898685304722[/C][/ROW]
[ROW][C]57[/C][C]1208[/C][C]1397.79915781359[/C][C]-189.799157813591[/C][/ROW]
[ROW][C]58[/C][C]1241[/C][C]1178.68476644001[/C][C]62.3152335599891[/C][/ROW]
[ROW][C]59[/C][C]1265[/C][C]1298.32903611964[/C][C]-33.3290361196446[/C][/ROW]
[ROW][C]60[/C][C]1828[/C][C]1305.30176605924[/C][C]522.698233940756[/C][/ROW]
[ROW][C]61[/C][C]809[/C][C]774.561182046743[/C][C]34.4388179532569[/C][/ROW]
[ROW][C]62[/C][C]997[/C][C]1040.77010167047[/C][C]-43.7701016704652[/C][/ROW]
[ROW][C]63[/C][C]1164[/C][C]1168.750736122[/C][C]-4.75073612200322[/C][/ROW]
[ROW][C]64[/C][C]1205[/C][C]1367.69835490318[/C][C]-162.698354903181[/C][/ROW]
[ROW][C]65[/C][C]1538[/C][C]1626.18580375516[/C][C]-88.1858037551608[/C][/ROW]
[ROW][C]66[/C][C]1513[/C][C]1647.32610180476[/C][C]-134.326101804765[/C][/ROW]
[ROW][C]67[/C][C]1378[/C][C]1994.84739817591[/C][C]-616.847398175911[/C][/ROW]
[ROW][C]68[/C][C]2083[/C][C]2061.21782094063[/C][C]21.7821790593721[/C][/ROW]
[ROW][C]69[/C][C]1357[/C][C]1428.82192211009[/C][C]-71.8219221100858[/C][/ROW]
[ROW][C]70[/C][C]1536[/C][C]1284.72546884146[/C][C]251.274531158537[/C][/ROW]
[ROW][C]71[/C][C]1526[/C][C]1409.0793621541[/C][C]116.920637845899[/C][/ROW]
[ROW][C]72[/C][C]1376[/C][C]1598.65940088833[/C][C]-222.659400888332[/C][/ROW]
[ROW][C]73[/C][C]779[/C][C]810.737142731786[/C][C]-31.7371427317856[/C][/ROW]
[ROW][C]74[/C][C]1005[/C][C]1051.93266238054[/C][C]-46.9326623805407[/C][/ROW]
[ROW][C]75[/C][C]1193[/C][C]1193.37607240146[/C][C]-0.376072401463716[/C][/ROW]
[ROW][C]76[/C][C]1522[/C][C]1351.58581532865[/C][C]170.414184671352[/C][/ROW]
[ROW][C]77[/C][C]1539[/C][C]1689.45800268104[/C][C]-150.458002681037[/C][/ROW]
[ROW][C]78[/C][C]1546[/C][C]1689.68549575823[/C][C]-143.685495758233[/C][/ROW]
[ROW][C]79[/C][C]2116[/C][C]1905.13461397818[/C][C]210.865386021819[/C][/ROW]
[ROW][C]80[/C][C]2326[/C][C]2288.75654810259[/C][C]37.2434518974096[/C][/ROW]
[ROW][C]81[/C][C]1596[/C][C]1559.59203705077[/C][C]36.4079629492337[/C][/ROW]
[ROW][C]82[/C][C]1356[/C][C]1510.71249582127[/C][C]-154.712495821273[/C][/ROW]
[ROW][C]83[/C][C]1553[/C][C]1548.27143626207[/C][C]4.72856373793252[/C][/ROW]
[ROW][C]84[/C][C]1613[/C][C]1632.16622149011[/C][C]-19.1662214901114[/C][/ROW]
[ROW][C]85[/C][C]814[/C][C]866.034135880248[/C][C]-52.0341358802476[/C][/ROW]
[ROW][C]86[/C][C]1150[/C][C]1118.27300435745[/C][C]31.7269956425484[/C][/ROW]
[ROW][C]87[/C][C]1225[/C][C]1295.90142116044[/C][C]-70.9014211604399[/C][/ROW]
[ROW][C]88[/C][C]1691[/C][C]1506.94089288937[/C][C]184.059107110631[/C][/ROW]
[ROW][C]89[/C][C]1759[/C][C]1773.46513372944[/C][C]-14.4651337294404[/C][/ROW]
[ROW][C]90[/C][C]1754[/C][C]1793.43097722[/C][C]-39.4309772200031[/C][/ROW]
[ROW][C]91[/C][C]2100[/C][C]2150.52505344622[/C][C]-50.5250534462157[/C][/ROW]
[ROW][C]92[/C][C]2062[/C][C]2474.58202317733[/C][C]-412.582023177332[/C][/ROW]
[ROW][C]93[/C][C]2012[/C][C]1649.1346153853[/C][C]362.865384614701[/C][/ROW]
[ROW][C]94[/C][C]1897[/C][C]1579.57425173296[/C][C]317.425748267038[/C][/ROW]
[ROW][C]95[/C][C]1964[/C][C]1733.10680944282[/C][C]230.893190557183[/C][/ROW]
[ROW][C]96[/C][C]2186[/C][C]1848.4195148665[/C][C]337.580485133498[/C][/ROW]
[ROW][C]97[/C][C]966[/C][C]990.716000674304[/C][C]-24.7160006743045[/C][/ROW]
[ROW][C]98[/C][C]1549[/C][C]1316.0008105535[/C][C]232.999189446502[/C][/ROW]
[ROW][C]99[/C][C]1538[/C][C]1516.72552640782[/C][C]21.2744735921835[/C][/ROW]
[ROW][C]100[/C][C]1612[/C][C]1865.30779150914[/C][C]-253.30779150914[/C][/ROW]
[ROW][C]101[/C][C]2078[/C][C]2048.08749988088[/C][C]29.912500119121[/C][/ROW]
[ROW][C]102[/C][C]2137[/C][C]2067.22079795436[/C][C]69.7792020456363[/C][/ROW]
[ROW][C]103[/C][C]2907[/C][C]2493.16925096452[/C][C]413.830749035475[/C][/ROW]
[ROW][C]104[/C][C]2249[/C][C]2815.31344148215[/C][C]-566.313441482155[/C][/ROW]
[ROW][C]105[/C][C]1883[/C][C]2074.76570147992[/C][C]-191.765701479922[/C][/ROW]
[ROW][C]106[/C][C]1739[/C][C]1898.77284610268[/C][C]-159.772846102682[/C][/ROW]
[ROW][C]107[/C][C]1828[/C][C]1973.17415285425[/C][C]-145.174152854252[/C][/ROW]
[ROW][C]108[/C][C]1868[/C][C]2074.50887478497[/C][C]-206.508874784973[/C][/ROW]
[ROW][C]109[/C][C]1138[/C][C]1014.42752648855[/C][C]123.572473511447[/C][/ROW]
[ROW][C]110[/C][C]1430[/C][C]1448.76169452103[/C][C]-18.761694521033[/C][/ROW]
[ROW][C]111[/C][C]1809[/C][C]1560.78133262785[/C][C]248.218667372154[/C][/ROW]
[ROW][C]112[/C][C]1763[/C][C]1872.46637985904[/C][C]-109.466379859042[/C][/ROW]
[ROW][C]113[/C][C]2200[/C][C]2167.82898868275[/C][C]32.1710113172498[/C][/ROW]
[ROW][C]114[/C][C]2067[/C][C]2198.99757413638[/C][C]-131.997574136377[/C][/ROW]
[ROW][C]115[/C][C]2503[/C][C]2712.5061091222[/C][C]-209.506109122202[/C][/ROW]
[ROW][C]116[/C][C]2141[/C][C]2677.56580949326[/C][C]-536.565809493257[/C][/ROW]
[ROW][C]117[/C][C]2103[/C][C]2039.05393585696[/C][C]63.9460641430376[/C][/ROW]
[ROW][C]118[/C][C]1972[/C][C]1900.54022236162[/C][C]71.4597776383785[/C][/ROW]
[ROW][C]119[/C][C]2181[/C][C]2012.03876025337[/C][C]168.961239746629[/C][/ROW]
[ROW][C]120[/C][C]2344[/C][C]2141.73511193064[/C][C]202.26488806936[/C][/ROW]
[ROW][C]121[/C][C]970[/C][C]1141.04354334108[/C][C]-171.043543341078[/C][/ROW]
[ROW][C]122[/C][C]1199[/C][C]1516.72816114098[/C][C]-317.728161140977[/C][/ROW]
[ROW][C]123[/C][C]1718[/C][C]1667.26490517322[/C][C]50.7350948267826[/C][/ROW]
[ROW][C]124[/C][C]1683[/C][C]1855.10955042205[/C][C]-172.109550422053[/C][/ROW]
[ROW][C]125[/C][C]2025[/C][C]2182.6473164392[/C][C]-157.647316439195[/C][/ROW]
[ROW][C]126[/C][C]2051[/C][C]2143.70912748381[/C][C]-92.7091274838067[/C][/ROW]
[ROW][C]127[/C][C]2439[/C][C]2637.04671442342[/C][C]-198.046714423421[/C][/ROW]
[ROW][C]128[/C][C]2353[/C][C]2509.46343944918[/C][C]-156.463439449184[/C][/ROW]
[ROW][C]129[/C][C]2230[/C][C]2081.6786838611[/C][C]148.321316138897[/C][/ROW]
[ROW][C]130[/C][C]1852[/C][C]1953.3393933984[/C][C]-101.339393398398[/C][/ROW]
[ROW][C]131[/C][C]2147[/C][C]2069.7932570341[/C][C]77.2067429658987[/C][/ROW]
[ROW][C]132[/C][C]2286[/C][C]2196.18554403942[/C][C]89.8144559605767[/C][/ROW]
[ROW][C]133[/C][C]1007[/C][C]1084.64792832793[/C][C]-77.6479283279318[/C][/ROW]
[ROW][C]134[/C][C]1665[/C][C]1430.19404726692[/C][C]234.805952733082[/C][/ROW]
[ROW][C]135[/C][C]1642[/C][C]1769.23922025064[/C][C]-127.239220250645[/C][/ROW]
[ROW][C]136[/C][C]1518[/C][C]1875.82278453686[/C][C]-357.82278453686[/C][/ROW]
[ROW][C]137[/C][C]1831[/C][C]2191.54539780047[/C][C]-360.545397800471[/C][/ROW]
[ROW][C]138[/C][C]2207[/C][C]2144.1852889921[/C][C]62.8147110079021[/C][/ROW]
[ROW][C]139[/C][C]2822[/C][C]2637.89742440654[/C][C]184.102575593462[/C][/ROW]
[ROW][C]140[/C][C]2393[/C][C]2566.47025484257[/C][C]-173.470254842569[/C][/ROW]
[ROW][C]141[/C][C]2306[/C][C]2207.3178541476[/C][C]98.6821458523982[/C][/ROW]
[ROW][C]142[/C][C]1785[/C][C]1994.72252902757[/C][C]-209.722529027573[/C][/ROW]
[ROW][C]143[/C][C]2047[/C][C]2151.5657479414[/C][C]-104.565747941396[/C][/ROW]
[ROW][C]144[/C][C]2171[/C][C]2259.77921774681[/C][C]-88.7792177468114[/C][/ROW]
[ROW][C]145[/C][C]1212[/C][C]1070.26508143284[/C][C]141.734918567159[/C][/ROW]
[ROW][C]146[/C][C]1335[/C][C]1544.13335047299[/C][C]-209.133350472994[/C][/ROW]
[ROW][C]147[/C][C]2011[/C][C]1723.90986670662[/C][C]287.090133293384[/C][/ROW]
[ROW][C]148[/C][C]1860[/C][C]1818.52528118565[/C][C]41.4747188143513[/C][/ROW]
[ROW][C]149[/C][C]1954[/C][C]2201.59379662451[/C][C]-247.593796624513[/C][/ROW]
[ROW][C]150[/C][C]2152[/C][C]2292.73539533376[/C][C]-140.735395333763[/C][/ROW]
[ROW][C]151[/C][C]2835[/C][C]2817.79631950021[/C][C]17.2036804997865[/C][/ROW]
[ROW][C]152[/C][C]2224[/C][C]2616.82823281662[/C][C]-392.828232816615[/C][/ROW]
[ROW][C]153[/C][C]2182[/C][C]2297.14668489139[/C][C]-115.146684891395[/C][/ROW]
[ROW][C]154[/C][C]1992[/C][C]1965.49774545083[/C][C]26.5022545491749[/C][/ROW]
[ROW][C]155[/C][C]2389[/C][C]2188.10276913206[/C][C]200.897230867938[/C][/ROW]
[ROW][C]156[/C][C]2724[/C][C]2345.61843415765[/C][C]378.381565842351[/C][/ROW]
[ROW][C]157[/C][C]891[/C][C]1193.66533165031[/C][C]-302.665331650305[/C][/ROW]
[ROW][C]158[/C][C]1247[/C][C]1519.99089775424[/C][C]-272.990897754237[/C][/ROW]
[ROW][C]159[/C][C]2017[/C][C]1833.6017823691[/C][C]183.398217630903[/C][/ROW]
[ROW][C]160[/C][C]2257[/C][C]1846.38991421296[/C][C]410.610085787036[/C][/ROW]
[ROW][C]161[/C][C]2255[/C][C]2205.65988299307[/C][C]49.3401170069328[/C][/ROW]
[ROW][C]162[/C][C]2255[/C][C]2371.89557184747[/C][C]-116.895571847472[/C][/ROW]
[ROW][C]163[/C][C]3057[/C][C]2974.95897107682[/C][C]82.0410289231831[/C][/ROW]
[ROW][C]164[/C][C]3330[/C][C]2647.35367654447[/C][C]682.646323455534[/C][/ROW]
[ROW][C]165[/C][C]1896[/C][C]2520.14220101209[/C][C]-624.142201012089[/C][/ROW]
[ROW][C]166[/C][C]2096[/C][C]2137.97226036437[/C][C]-41.9722603643736[/C][/ROW]
[ROW][C]167[/C][C]2374[/C][C]2419.71801410653[/C][C]-45.7180141065269[/C][/ROW]
[ROW][C]168[/C][C]2535[/C][C]2603.13644850773[/C][C]-68.1364485077324[/C][/ROW]
[ROW][C]169[/C][C]1041[/C][C]1148.50094503261[/C][C]-107.500945032608[/C][/ROW]
[ROW][C]170[/C][C]1728[/C][C]1528.24102368982[/C][C]199.75897631018[/C][/ROW]
[ROW][C]171[/C][C]2201[/C][C]2076.81793642793[/C][C]124.182063572066[/C][/ROW]
[ROW][C]172[/C][C]2455[/C][C]2145.67557931211[/C][C]309.324420687888[/C][/ROW]
[ROW][C]173[/C][C]2204[/C][C]2408.11543436068[/C][C]-204.115434360685[/C][/ROW]
[ROW][C]174[/C][C]2660[/C][C]2502.4100837042[/C][C]157.589916295803[/C][/ROW]
[ROW][C]175[/C][C]3670[/C][C]3252.23288577993[/C][C]417.767114220065[/C][/ROW]
[ROW][C]176[/C][C]2665[/C][C]3109.72008884198[/C][C]-444.720088841978[/C][/ROW]
[ROW][C]177[/C][C]2639[/C][C]2442.44363392995[/C][C]196.556366070055[/C][/ROW]
[ROW][C]178[/C][C]2226[/C][C]2314.42316529854[/C][C]-88.4231652985432[/C][/ROW]
[ROW][C]179[/C][C]2586[/C][C]2612.65435638818[/C][C]-26.6543563881764[/C][/ROW]
[ROW][C]180[/C][C]2684[/C][C]2806.99681258428[/C][C]-122.996812584276[/C][/ROW]
[ROW][C]181[/C][C]1185[/C][C]1211.47427616189[/C][C]-26.4742761618943[/C][/ROW]
[ROW][C]182[/C][C]1749[/C][C]1730.07097698978[/C][C]18.929023010221[/C][/ROW]
[ROW][C]183[/C][C]2459[/C][C]2271.62383173616[/C][C]187.37616826384[/C][/ROW]
[ROW][C]184[/C][C]2618[/C][C]2405.78894534092[/C][C]212.211054659084[/C][/ROW]
[ROW][C]185[/C][C]2585[/C][C]2517.28777215458[/C][C]67.7122278454153[/C][/ROW]
[ROW][C]186[/C][C]3310[/C][C]2764.81400431411[/C][C]545.185995685893[/C][/ROW]
[ROW][C]187[/C][C]3923[/C][C]3717.0807290368[/C][C]205.919270963203[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76766&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76766&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
13530488.83695900046341.163040999537
14883814.75523893239868.2447610676024
15894834.61852403814659.3814759618537
161045999.13603200943145.8639679905687
1711991167.5174428106331.482557189365
1812871265.7678925140121.2321074859897
1915651471.5441169062193.4558830937872
2015771416.67395034437160.326049655626
2110761269.32628721627-193.326287216273
229181065.59758763115-147.59758763115
2310081084.10736657534-76.1073665753393
2410631034.9518793466728.0481206533341
25544581.982552876673-37.9825528766735
26635950.01704297119-315.01704297119
27804917.750504008931-113.750504008931
289801063.42543153308-83.425431533082
2910181216.09603740949-198.096037409493
3010641282.55780969-218.557809689996
3114041474.02347356886-70.0234735688623
3212861417.91458147564-131.914581475643
3311041148.84690724577-44.8469072457665
34999983.31927443459815.6807255654018
359961041.62335100716-45.6233510071593
3610151025.66177783381-10.661777833812
37615559.4112478402855.58875215972
38722858.653333515284-136.653333515284
39832906.056318391444-74.0563183914444
409771069.55862086222-92.558620862217
4112701191.2360260260578.7639739739534
4214371290.50915889254146.490841107455
4315201593.93855847725-73.9385584772474
4417081512.02680738276195.973192617235
4511511278.88506606663-127.88506606663
469341101.55325635511-167.553256355113
4711591121.7000205159537.2999794840518
4812091125.0045063484283.9954936515812
49699638.41288507032560.5871149296751
50830909.400985151323-79.4009851513226
51996990.0938507714375.90614922856298
5211241179.04487730169-55.044877301686
5314581375.8299184303282.1700815696847
5412701506.27911116514-236.279111165137
5517531718.3139041886134.6860958113941
5622581723.10131469528534.898685304722
5712081397.79915781359-189.799157813591
5812411178.6847664400162.3152335599891
5912651298.32903611964-33.3290361196446
6018281305.30176605924522.698233940756
61809774.56118204674334.4388179532569
629971040.77010167047-43.7701016704652
6311641168.750736122-4.75073612200322
6412051367.69835490318-162.698354903181
6515381626.18580375516-88.1858037551608
6615131647.32610180476-134.326101804765
6713781994.84739817591-616.847398175911
6820832061.2178209406321.7821790593721
6913571428.82192211009-71.8219221100858
7015361284.72546884146251.274531158537
7115261409.0793621541116.920637845899
7213761598.65940088833-222.659400888332
73779810.737142731786-31.7371427317856
7410051051.93266238054-46.9326623805407
7511931193.37607240146-0.376072401463716
7615221351.58581532865170.414184671352
7715391689.45800268104-150.458002681037
7815461689.68549575823-143.685495758233
7921161905.13461397818210.865386021819
8023262288.7565481025937.2434518974096
8115961559.5920370507736.4079629492337
8213561510.71249582127-154.712495821273
8315531548.271436262074.72856373793252
8416131632.16622149011-19.1662214901114
85814866.034135880248-52.0341358802476
8611501118.2730043574531.7269956425484
8712251295.90142116044-70.9014211604399
8816911506.94089288937184.059107110631
8917591773.46513372944-14.4651337294404
9017541793.43097722-39.4309772200031
9121002150.52505344622-50.5250534462157
9220622474.58202317733-412.582023177332
9320121649.1346153853362.865384614701
9418971579.57425173296317.425748267038
9519641733.10680944282230.893190557183
9621861848.4195148665337.580485133498
97966990.716000674304-24.7160006743045
9815491316.0008105535232.999189446502
9915381516.7255264078221.2744735921835
10016121865.30779150914-253.30779150914
10120782048.0874998808829.912500119121
10221372067.2207979543669.7792020456363
10329072493.16925096452413.830749035475
10422492815.31344148215-566.313441482155
10518832074.76570147992-191.765701479922
10617391898.77284610268-159.772846102682
10718281973.17415285425-145.174152854252
10818682074.50887478497-206.508874784973
10911381014.42752648855123.572473511447
11014301448.76169452103-18.761694521033
11118091560.78133262785248.218667372154
11217631872.46637985904-109.466379859042
11322002167.8289886827532.1710113172498
11420672198.99757413638-131.997574136377
11525032712.5061091222-209.506109122202
11621412677.56580949326-536.565809493257
11721032039.0539358569663.9460641430376
11819721900.5402223616271.4597776383785
11921812012.03876025337168.961239746629
12023442141.73511193064202.26488806936
1219701141.04354334108-171.043543341078
12211991516.72816114098-317.728161140977
12317181667.2649051732250.7350948267826
12416831855.10955042205-172.109550422053
12520252182.6473164392-157.647316439195
12620512143.70912748381-92.7091274838067
12724392637.04671442342-198.046714423421
12823532509.46343944918-156.463439449184
12922302081.6786838611148.321316138897
13018521953.3393933984-101.339393398398
13121472069.793257034177.2067429658987
13222862196.1855440394289.8144559605767
13310071084.64792832793-77.6479283279318
13416651430.19404726692234.805952733082
13516421769.23922025064-127.239220250645
13615181875.82278453686-357.82278453686
13718312191.54539780047-360.545397800471
13822072144.185288992162.8147110079021
13928222637.89742440654184.102575593462
14023932566.47025484257-173.470254842569
14123062207.317854147698.6821458523982
14217851994.72252902757-209.722529027573
14320472151.5657479414-104.565747941396
14421712259.77921774681-88.7792177468114
14512121070.26508143284141.734918567159
14613351544.13335047299-209.133350472994
14720111723.90986670662287.090133293384
14818601818.5252811856541.4747188143513
14919542201.59379662451-247.593796624513
15021522292.73539533376-140.735395333763
15128352817.7963195002117.2036804997865
15222242616.82823281662-392.828232816615
15321822297.14668489139-115.146684891395
15419921965.4977454508326.5022545491749
15523892188.10276913206200.897230867938
15627242345.61843415765378.381565842351
1578911193.66533165031-302.665331650305
15812471519.99089775424-272.990897754237
15920171833.6017823691183.398217630903
16022571846.38991421296410.610085787036
16122552205.6598829930749.3401170069328
16222552371.89557184747-116.895571847472
16330572974.9589710768282.0410289231831
16433302647.35367654447682.646323455534
16518962520.14220101209-624.142201012089
16620962137.97226036437-41.9722603643736
16723742419.71801410653-45.7180141065269
16825352603.13644850773-68.1364485077324
16910411148.50094503261-107.500945032608
17017281528.24102368982199.75897631018
17122012076.81793642793124.182063572066
17224552145.67557931211309.324420687888
17322042408.11543436068-204.115434360685
17426602502.4100837042157.589916295803
17536703252.23288577993417.767114220065
17626653109.72008884198-444.720088841978
17726392442.44363392995196.556366070055
17822262314.42316529854-88.4231652985432
17925862612.65435638818-26.6543563881764
18026842806.99681258428-122.996812584276
18111851211.47427616189-26.4742761618943
18217491730.0709769897818.929023010221
18324592271.62383173616187.37616826384
18426182405.78894534092212.211054659084
18525852517.2877721545867.7122278454153
18633102764.81400431411545.185995685893
18739233717.0807290368205.919270963203







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
1883262.851771364683097.721529041743427.98201368762
1892779.903028373292608.905649841752950.90040690483
1902522.426002142672346.03073013672698.82127414863
1912882.932062192462693.719101965713072.14502241921
1923070.847781003572870.962648679823270.73291332731
1931340.563108470271166.828380260631514.2978366799
1941936.536834646321739.119310279852133.95435901279
1952587.659677581382357.764237526132817.55511763663
1962714.660208545362473.435654124752955.88476296597
1972761.519808001632512.879398273643010.16021772961
1983159.455099884382884.328342920713434.58185684806
1993991.417302306393706.135974595574276.69863001721

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
188 & 3262.85177136468 & 3097.72152904174 & 3427.98201368762 \tabularnewline
189 & 2779.90302837329 & 2608.90564984175 & 2950.90040690483 \tabularnewline
190 & 2522.42600214267 & 2346.0307301367 & 2698.82127414863 \tabularnewline
191 & 2882.93206219246 & 2693.71910196571 & 3072.14502241921 \tabularnewline
192 & 3070.84778100357 & 2870.96264867982 & 3270.73291332731 \tabularnewline
193 & 1340.56310847027 & 1166.82838026063 & 1514.2978366799 \tabularnewline
194 & 1936.53683464632 & 1739.11931027985 & 2133.95435901279 \tabularnewline
195 & 2587.65967758138 & 2357.76423752613 & 2817.55511763663 \tabularnewline
196 & 2714.66020854536 & 2473.43565412475 & 2955.88476296597 \tabularnewline
197 & 2761.51980800163 & 2512.87939827364 & 3010.16021772961 \tabularnewline
198 & 3159.45509988438 & 2884.32834292071 & 3434.58185684806 \tabularnewline
199 & 3991.41730230639 & 3706.13597459557 & 4276.69863001721 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76766&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]188[/C][C]3262.85177136468[/C][C]3097.72152904174[/C][C]3427.98201368762[/C][/ROW]
[ROW][C]189[/C][C]2779.90302837329[/C][C]2608.90564984175[/C][C]2950.90040690483[/C][/ROW]
[ROW][C]190[/C][C]2522.42600214267[/C][C]2346.0307301367[/C][C]2698.82127414863[/C][/ROW]
[ROW][C]191[/C][C]2882.93206219246[/C][C]2693.71910196571[/C][C]3072.14502241921[/C][/ROW]
[ROW][C]192[/C][C]3070.84778100357[/C][C]2870.96264867982[/C][C]3270.73291332731[/C][/ROW]
[ROW][C]193[/C][C]1340.56310847027[/C][C]1166.82838026063[/C][C]1514.2978366799[/C][/ROW]
[ROW][C]194[/C][C]1936.53683464632[/C][C]1739.11931027985[/C][C]2133.95435901279[/C][/ROW]
[ROW][C]195[/C][C]2587.65967758138[/C][C]2357.76423752613[/C][C]2817.55511763663[/C][/ROW]
[ROW][C]196[/C][C]2714.66020854536[/C][C]2473.43565412475[/C][C]2955.88476296597[/C][/ROW]
[ROW][C]197[/C][C]2761.51980800163[/C][C]2512.87939827364[/C][C]3010.16021772961[/C][/ROW]
[ROW][C]198[/C][C]3159.45509988438[/C][C]2884.32834292071[/C][C]3434.58185684806[/C][/ROW]
[ROW][C]199[/C][C]3991.41730230639[/C][C]3706.13597459557[/C][C]4276.69863001721[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76766&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76766&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
1883262.851771364683097.721529041743427.98201368762
1892779.903028373292608.905649841752950.90040690483
1902522.426002142672346.03073013672698.82127414863
1912882.932062192462693.719101965713072.14502241921
1923070.847781003572870.962648679823270.73291332731
1931340.563108470271166.828380260631514.2978366799
1941936.536834646321739.119310279852133.95435901279
1952587.659677581382357.764237526132817.55511763663
1962714.660208545362473.435654124752955.88476296597
1972761.519808001632512.879398273643010.16021772961
1983159.455099884382884.328342920713434.58185684806
1993991.417302306393706.135974595574276.69863001721



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')