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Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationSun, 23 Dec 2012 14:14:45 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/23/t13562901651yxuhezocgymvm9.htm/, Retrieved Tue, 07 Feb 2023 19:05:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204697, Retrieved Tue, 07 Feb 2023 19:05:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [] [2012-11-27 12:03:44] [73ff502f5cc0e8f9d4cad04b672b43dc]
- RMPD    [Exponential Smoothing] [] [2012-12-23 19:14:45] [76c30f62b7052b57088120e90a652e05] [Current]
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Dataseries X:
1,6
2
2,6
3
2,6
2,9
2,5
2,4
1,5
1,1
0,6
0,9
1,1
1,5
1,7
1,2
0,4
-0,7
-1,4
-1,6
-1,2
-0,4
-0,2
-0,3
-0,5
0
-0,5
0,2
0,7
1,6
2,6
3,3
3,3
3,2
3,5
3,9
4,5
4,6
6,6
7,1
8,9
8,8
8,5
7,6
7,5
7,5
6,1
6,3
8,4
7,1
5,6
4,2
2,1
1,2
0,9
1,4
1,7
1,7
1,9
1,3
-0,7
0,3
0,8
0,9
1,1
2,5
2,7
3,3
4,2
3,8
3,8
3,2
2,9
1,9
1,7
1,6
1,7
1,2
0,7
-0,2
-1,5
-1,2
-1
0
-0,6
0,7
1,3
0,8
1
0,5
0,3
1
1
1,1
1,5
1,5
2
1,7
0,6
1,2
1,5
2,1
3,2
3,9
4,6
4,2
4,4
3,7
3,7
2,8
2,9
3,9
3,1
3
2,8
2,4
2,1
3,1
3
3,1
3,3
3,3
3,8
3,1
3,9
4
4,4
3,7
3,6
3,4
2,8
2,8
2,6
3,3
2,4
1,6
0,7
0
-1,1
-1,2
-1,3
-1,6
-1,3
-1,6
-1,1
-1
0,3
1,2
0,7
1,1
2,1
2,5
2,3
2,3
2,6
3,2
2,2
2,7
2,2
1,4
2,4
2
1,3
1,1
1,4
1,8
1,9
1,6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204697&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204697&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204697&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.873774375374483
beta0.0138532313679703
gamma1

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.873774375374483 \tabularnewline
beta & 0.0138532313679703 \tabularnewline
gamma & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204697&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]0.873774375374483[/C][/ROW]
[ROW][C]beta[/C][C]0.0138532313679703[/C][/ROW]
[ROW][C]gamma[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204697&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204697&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
alpha0.873774375374483
beta0.0138532313679703
gamma1







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
131.12.16132478632479-1.06132478632479
141.51.74847961343726-0.248479613437262
151.71.79286997778837-0.0928699777883681
161.21.16793723386240.032062766137599
170.40.2733889604711350.126611039528865
18-0.7-0.8495128785849560.149512878584957
19-1.40.370239449187077-1.77023944918708
20-1.6-1.47136665204617-0.128633347953826
21-1.2-2.663469795938421.46346979593842
22-0.4-1.892552628656011.49255262865601
23-0.2-1.123990210277440.92399021027744
24-0.30.0339614667132991-0.333961466713299
25-0.5-0.0744283173584626-0.425571682641537
2600.185218805404061-0.185218805404061
27-0.50.319678147840291-0.819678147840291
280.2-0.9181975767960481.11819757679605
290.7-0.8322737832173651.53227378321736
301.6-0.6875368678727282.28753686787273
312.62.22043989073920.379560109260802
323.32.552904563844160.747095436155841
333.32.425973602950080.874026397049923
343.22.777404853402570.42259514659743
353.52.618230911122720.881769088877277
363.93.658926264534180.241073735465823
374.54.126805665208710.373194334791288
384.65.20978321686006-0.60978321686006
396.64.983095306173481.61690469382652
407.16.23825794017210.861742059827896
418.96.268665345631282.63133465436872
428.87.598671575680811.20132842431919
438.59.433168088583-0.933168088583002
447.68.76556324561258-1.16556324561258
457.57.060836482823860.439163517176143
467.57.047464063125620.452535936874376
476.17.04492411774028-0.944924117740281
486.36.45903118025075-0.159031180250747
498.46.639544649780821.76045535021918
507.18.8729490974383-1.7729490974383
515.67.95925278651166-2.35925278651166
524.25.64497130450139-1.44497130450139
532.13.85541905006195-1.75541905006195
541.21.091008418416620.108991581583382
550.91.60751808017742-0.70751808017742
561.41.016374858192080.383625141807923
571.70.7952278022016390.904772197798361
581.71.123397218045270.576602781954731
591.90.9873871732322970.912612826767703
601.32.08076572395014-0.780765723950141
61-0.71.90978949796266-2.60978949796266
620.3-0.22484262911780.5248426291178
630.80.7195974588530550.0804025411469449
640.90.6063525041340180.293647495865982
651.10.2717420941615510.828257905838449
662.50.006460698549723862.49353930145028
672.72.538568726860520.161431273139478
683.32.890045815306890.409954184693107
694.22.803629614731251.39637038526875
703.83.571815238743310.228184761256688
713.83.221455771506280.578544228493716
723.23.85281844805474-0.652818448054742
732.93.6079508271403-0.707950827140305
741.93.59896971094358-1.69896971094358
751.72.58548369311959-0.885483693119594
761.61.68478127013923-0.0847812701392259
771.71.112002501311290.587997498688711
781.20.8690921139231960.330907886076804
790.71.21310186143154-0.513101861431535
80-0.20.994319614365202-1.1943196143652
81-1.5-0.401017539140786-1.09898246085921
82-1.2-2.022526111480990.822526111480992
83-1-1.864010592422280.864010592422281
840-1.189858387635771.18985838763577
85-0.60.139489394397724-0.739489394397724
860.7-0.051432332212330.75143233221233
871.31.179233037583940.120766962416057
880.81.27138616857728-0.471386168577276
8910.4535945218062530.546405478193747
900.50.1492579964167340.350742003583266
910.30.411669914685628-0.111669914685628
9210.4701279370417670.529872062958233
9310.6267163865739080.373283613426092
941.10.585338098692190.51466190130781
951.50.5275178868906460.972482113109354
961.51.386324795175860.113675204824139
9721.567516121300970.432483878699031
981.72.63873136292786-0.938731362927865
990.62.3424143497889-1.7424143497889
1001.20.7387149070898990.461285092910101
1011.50.8825209192621020.617479080737898
1022.10.6346312833259821.46536871667402
1033.21.845141678402161.35485832159784
1043.93.316279716870460.583720283129544
1054.63.551091883219131.04890811678087
1064.24.177018696570560.0229813034294439
1074.43.800533783535620.599466216464382
1083.74.27365487745992-0.573654877459917
1093.73.93484611568112-0.234846115681116
1102.84.28213474764749-1.48213474764749
1112.93.43523445430734-0.535234454307336
1123.93.204787697320220.695212302679778
1133.13.61582707767208-0.515827077672078
11432.514108826754520.48589117324548
1152.82.87237128377707-0.0723712837770671
1162.42.99936292754749-0.599362927547494
1172.12.2450928208159-0.145092820815899
1183.11.66972795130081.4302720486992
11932.584193481397770.415806518602226
1203.12.73506505075870.364934949241295
1213.33.256805185270920.043194814729083
1223.33.69063145387787-0.390631453877872
1233.83.93122644248233-0.131226442482334
1243.14.22824039117842-1.12824039117842
1253.92.890192114835041.00980788516496
12643.263507095490480.736492904509524
1274.43.788835312684890.611164687315107
1283.74.47340066809534-0.773400668095335
1293.63.64913205916812-0.0491320591681181
1303.43.382358913261870.0176410867381334
1312.82.94324508536713-0.143245085367128
1322.82.601236221422780.19876377857722
1332.62.93718278869289-0.337182788692885
1343.32.979294937585220.320705062414783
1352.43.87820162383337-1.47820162383337
1361.62.8601303903348-1.2601303903348
1370.71.66283593999393-0.962835939993929
13800.240247327148995-0.240247327148995
139-1.1-0.153276335585294-0.946723664414706
140-1.2-1.07316080318054-0.126839196819462
141-1.3-1.301672216521610.00167221652160943
142-1.6-1.57562334843447-0.0243766515655313
143-1.3-2.132265707107350.832265707107351
144-1.6-1.627426338818330.0274263388183269
145-1.1-1.559612580713250.459612580713247
146-1-0.779366217117632-0.220633782882368
1470.3-0.6282158182611190.928215818261119
1481.20.4653535845620260.734646415437974
1490.71.05416470319435-0.354164703194354
1501.10.267588873893610.83241112610639
1512.10.7480976189817451.35190238101826
1522.51.993954417868790.50604558213121
1532.32.39609405693151-0.096094056931511
1542.32.093676920930690.206323079069307
1552.61.909784514805920.690215485194081
1563.22.250233448979570.949766551020427
1572.23.25100274804419-1.05100274804419
1582.72.679647551830040.0203524481699642
1592.23.24349677194525-1.04349677194525
1601.42.62305099887471-1.22305099887471
1612.41.373393457054721.02660654294528
16221.969343156236150.0306568437638501
1631.31.83143446858586-0.531434468585865
1641.11.3186757551219-0.218675755121897
1651.40.9965593172943090.403440682705691
1661.81.159834604730870.640165395269131
1671.91.412392582200110.48760741779989
1681.61.60240774845159-0.00240774845159075

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 1.1 & 2.16132478632479 & -1.06132478632479 \tabularnewline
14 & 1.5 & 1.74847961343726 & -0.248479613437262 \tabularnewline
15 & 1.7 & 1.79286997778837 & -0.0928699777883681 \tabularnewline
16 & 1.2 & 1.1679372338624 & 0.032062766137599 \tabularnewline
17 & 0.4 & 0.273388960471135 & 0.126611039528865 \tabularnewline
18 & -0.7 & -0.849512878584956 & 0.149512878584957 \tabularnewline
19 & -1.4 & 0.370239449187077 & -1.77023944918708 \tabularnewline
20 & -1.6 & -1.47136665204617 & -0.128633347953826 \tabularnewline
21 & -1.2 & -2.66346979593842 & 1.46346979593842 \tabularnewline
22 & -0.4 & -1.89255262865601 & 1.49255262865601 \tabularnewline
23 & -0.2 & -1.12399021027744 & 0.92399021027744 \tabularnewline
24 & -0.3 & 0.0339614667132991 & -0.333961466713299 \tabularnewline
25 & -0.5 & -0.0744283173584626 & -0.425571682641537 \tabularnewline
26 & 0 & 0.185218805404061 & -0.185218805404061 \tabularnewline
27 & -0.5 & 0.319678147840291 & -0.819678147840291 \tabularnewline
28 & 0.2 & -0.918197576796048 & 1.11819757679605 \tabularnewline
29 & 0.7 & -0.832273783217365 & 1.53227378321736 \tabularnewline
30 & 1.6 & -0.687536867872728 & 2.28753686787273 \tabularnewline
31 & 2.6 & 2.2204398907392 & 0.379560109260802 \tabularnewline
32 & 3.3 & 2.55290456384416 & 0.747095436155841 \tabularnewline
33 & 3.3 & 2.42597360295008 & 0.874026397049923 \tabularnewline
34 & 3.2 & 2.77740485340257 & 0.42259514659743 \tabularnewline
35 & 3.5 & 2.61823091112272 & 0.881769088877277 \tabularnewline
36 & 3.9 & 3.65892626453418 & 0.241073735465823 \tabularnewline
37 & 4.5 & 4.12680566520871 & 0.373194334791288 \tabularnewline
38 & 4.6 & 5.20978321686006 & -0.60978321686006 \tabularnewline
39 & 6.6 & 4.98309530617348 & 1.61690469382652 \tabularnewline
40 & 7.1 & 6.2382579401721 & 0.861742059827896 \tabularnewline
41 & 8.9 & 6.26866534563128 & 2.63133465436872 \tabularnewline
42 & 8.8 & 7.59867157568081 & 1.20132842431919 \tabularnewline
43 & 8.5 & 9.433168088583 & -0.933168088583002 \tabularnewline
44 & 7.6 & 8.76556324561258 & -1.16556324561258 \tabularnewline
45 & 7.5 & 7.06083648282386 & 0.439163517176143 \tabularnewline
46 & 7.5 & 7.04746406312562 & 0.452535936874376 \tabularnewline
47 & 6.1 & 7.04492411774028 & -0.944924117740281 \tabularnewline
48 & 6.3 & 6.45903118025075 & -0.159031180250747 \tabularnewline
49 & 8.4 & 6.63954464978082 & 1.76045535021918 \tabularnewline
50 & 7.1 & 8.8729490974383 & -1.7729490974383 \tabularnewline
51 & 5.6 & 7.95925278651166 & -2.35925278651166 \tabularnewline
52 & 4.2 & 5.64497130450139 & -1.44497130450139 \tabularnewline
53 & 2.1 & 3.85541905006195 & -1.75541905006195 \tabularnewline
54 & 1.2 & 1.09100841841662 & 0.108991581583382 \tabularnewline
55 & 0.9 & 1.60751808017742 & -0.70751808017742 \tabularnewline
56 & 1.4 & 1.01637485819208 & 0.383625141807923 \tabularnewline
57 & 1.7 & 0.795227802201639 & 0.904772197798361 \tabularnewline
58 & 1.7 & 1.12339721804527 & 0.576602781954731 \tabularnewline
59 & 1.9 & 0.987387173232297 & 0.912612826767703 \tabularnewline
60 & 1.3 & 2.08076572395014 & -0.780765723950141 \tabularnewline
61 & -0.7 & 1.90978949796266 & -2.60978949796266 \tabularnewline
62 & 0.3 & -0.2248426291178 & 0.5248426291178 \tabularnewline
63 & 0.8 & 0.719597458853055 & 0.0804025411469449 \tabularnewline
64 & 0.9 & 0.606352504134018 & 0.293647495865982 \tabularnewline
65 & 1.1 & 0.271742094161551 & 0.828257905838449 \tabularnewline
66 & 2.5 & 0.00646069854972386 & 2.49353930145028 \tabularnewline
67 & 2.7 & 2.53856872686052 & 0.161431273139478 \tabularnewline
68 & 3.3 & 2.89004581530689 & 0.409954184693107 \tabularnewline
69 & 4.2 & 2.80362961473125 & 1.39637038526875 \tabularnewline
70 & 3.8 & 3.57181523874331 & 0.228184761256688 \tabularnewline
71 & 3.8 & 3.22145577150628 & 0.578544228493716 \tabularnewline
72 & 3.2 & 3.85281844805474 & -0.652818448054742 \tabularnewline
73 & 2.9 & 3.6079508271403 & -0.707950827140305 \tabularnewline
74 & 1.9 & 3.59896971094358 & -1.69896971094358 \tabularnewline
75 & 1.7 & 2.58548369311959 & -0.885483693119594 \tabularnewline
76 & 1.6 & 1.68478127013923 & -0.0847812701392259 \tabularnewline
77 & 1.7 & 1.11200250131129 & 0.587997498688711 \tabularnewline
78 & 1.2 & 0.869092113923196 & 0.330907886076804 \tabularnewline
79 & 0.7 & 1.21310186143154 & -0.513101861431535 \tabularnewline
80 & -0.2 & 0.994319614365202 & -1.1943196143652 \tabularnewline
81 & -1.5 & -0.401017539140786 & -1.09898246085921 \tabularnewline
82 & -1.2 & -2.02252611148099 & 0.822526111480992 \tabularnewline
83 & -1 & -1.86401059242228 & 0.864010592422281 \tabularnewline
84 & 0 & -1.18985838763577 & 1.18985838763577 \tabularnewline
85 & -0.6 & 0.139489394397724 & -0.739489394397724 \tabularnewline
86 & 0.7 & -0.05143233221233 & 0.75143233221233 \tabularnewline
87 & 1.3 & 1.17923303758394 & 0.120766962416057 \tabularnewline
88 & 0.8 & 1.27138616857728 & -0.471386168577276 \tabularnewline
89 & 1 & 0.453594521806253 & 0.546405478193747 \tabularnewline
90 & 0.5 & 0.149257996416734 & 0.350742003583266 \tabularnewline
91 & 0.3 & 0.411669914685628 & -0.111669914685628 \tabularnewline
92 & 1 & 0.470127937041767 & 0.529872062958233 \tabularnewline
93 & 1 & 0.626716386573908 & 0.373283613426092 \tabularnewline
94 & 1.1 & 0.58533809869219 & 0.51466190130781 \tabularnewline
95 & 1.5 & 0.527517886890646 & 0.972482113109354 \tabularnewline
96 & 1.5 & 1.38632479517586 & 0.113675204824139 \tabularnewline
97 & 2 & 1.56751612130097 & 0.432483878699031 \tabularnewline
98 & 1.7 & 2.63873136292786 & -0.938731362927865 \tabularnewline
99 & 0.6 & 2.3424143497889 & -1.7424143497889 \tabularnewline
100 & 1.2 & 0.738714907089899 & 0.461285092910101 \tabularnewline
101 & 1.5 & 0.882520919262102 & 0.617479080737898 \tabularnewline
102 & 2.1 & 0.634631283325982 & 1.46536871667402 \tabularnewline
103 & 3.2 & 1.84514167840216 & 1.35485832159784 \tabularnewline
104 & 3.9 & 3.31627971687046 & 0.583720283129544 \tabularnewline
105 & 4.6 & 3.55109188321913 & 1.04890811678087 \tabularnewline
106 & 4.2 & 4.17701869657056 & 0.0229813034294439 \tabularnewline
107 & 4.4 & 3.80053378353562 & 0.599466216464382 \tabularnewline
108 & 3.7 & 4.27365487745992 & -0.573654877459917 \tabularnewline
109 & 3.7 & 3.93484611568112 & -0.234846115681116 \tabularnewline
110 & 2.8 & 4.28213474764749 & -1.48213474764749 \tabularnewline
111 & 2.9 & 3.43523445430734 & -0.535234454307336 \tabularnewline
112 & 3.9 & 3.20478769732022 & 0.695212302679778 \tabularnewline
113 & 3.1 & 3.61582707767208 & -0.515827077672078 \tabularnewline
114 & 3 & 2.51410882675452 & 0.48589117324548 \tabularnewline
115 & 2.8 & 2.87237128377707 & -0.0723712837770671 \tabularnewline
116 & 2.4 & 2.99936292754749 & -0.599362927547494 \tabularnewline
117 & 2.1 & 2.2450928208159 & -0.145092820815899 \tabularnewline
118 & 3.1 & 1.6697279513008 & 1.4302720486992 \tabularnewline
119 & 3 & 2.58419348139777 & 0.415806518602226 \tabularnewline
120 & 3.1 & 2.7350650507587 & 0.364934949241295 \tabularnewline
121 & 3.3 & 3.25680518527092 & 0.043194814729083 \tabularnewline
122 & 3.3 & 3.69063145387787 & -0.390631453877872 \tabularnewline
123 & 3.8 & 3.93122644248233 & -0.131226442482334 \tabularnewline
124 & 3.1 & 4.22824039117842 & -1.12824039117842 \tabularnewline
125 & 3.9 & 2.89019211483504 & 1.00980788516496 \tabularnewline
126 & 4 & 3.26350709549048 & 0.736492904509524 \tabularnewline
127 & 4.4 & 3.78883531268489 & 0.611164687315107 \tabularnewline
128 & 3.7 & 4.47340066809534 & -0.773400668095335 \tabularnewline
129 & 3.6 & 3.64913205916812 & -0.0491320591681181 \tabularnewline
130 & 3.4 & 3.38235891326187 & 0.0176410867381334 \tabularnewline
131 & 2.8 & 2.94324508536713 & -0.143245085367128 \tabularnewline
132 & 2.8 & 2.60123622142278 & 0.19876377857722 \tabularnewline
133 & 2.6 & 2.93718278869289 & -0.337182788692885 \tabularnewline
134 & 3.3 & 2.97929493758522 & 0.320705062414783 \tabularnewline
135 & 2.4 & 3.87820162383337 & -1.47820162383337 \tabularnewline
136 & 1.6 & 2.8601303903348 & -1.2601303903348 \tabularnewline
137 & 0.7 & 1.66283593999393 & -0.962835939993929 \tabularnewline
138 & 0 & 0.240247327148995 & -0.240247327148995 \tabularnewline
139 & -1.1 & -0.153276335585294 & -0.946723664414706 \tabularnewline
140 & -1.2 & -1.07316080318054 & -0.126839196819462 \tabularnewline
141 & -1.3 & -1.30167221652161 & 0.00167221652160943 \tabularnewline
142 & -1.6 & -1.57562334843447 & -0.0243766515655313 \tabularnewline
143 & -1.3 & -2.13226570710735 & 0.832265707107351 \tabularnewline
144 & -1.6 & -1.62742633881833 & 0.0274263388183269 \tabularnewline
145 & -1.1 & -1.55961258071325 & 0.459612580713247 \tabularnewline
146 & -1 & -0.779366217117632 & -0.220633782882368 \tabularnewline
147 & 0.3 & -0.628215818261119 & 0.928215818261119 \tabularnewline
148 & 1.2 & 0.465353584562026 & 0.734646415437974 \tabularnewline
149 & 0.7 & 1.05416470319435 & -0.354164703194354 \tabularnewline
150 & 1.1 & 0.26758887389361 & 0.83241112610639 \tabularnewline
151 & 2.1 & 0.748097618981745 & 1.35190238101826 \tabularnewline
152 & 2.5 & 1.99395441786879 & 0.50604558213121 \tabularnewline
153 & 2.3 & 2.39609405693151 & -0.096094056931511 \tabularnewline
154 & 2.3 & 2.09367692093069 & 0.206323079069307 \tabularnewline
155 & 2.6 & 1.90978451480592 & 0.690215485194081 \tabularnewline
156 & 3.2 & 2.25023344897957 & 0.949766551020427 \tabularnewline
157 & 2.2 & 3.25100274804419 & -1.05100274804419 \tabularnewline
158 & 2.7 & 2.67964755183004 & 0.0203524481699642 \tabularnewline
159 & 2.2 & 3.24349677194525 & -1.04349677194525 \tabularnewline
160 & 1.4 & 2.62305099887471 & -1.22305099887471 \tabularnewline
161 & 2.4 & 1.37339345705472 & 1.02660654294528 \tabularnewline
162 & 2 & 1.96934315623615 & 0.0306568437638501 \tabularnewline
163 & 1.3 & 1.83143446858586 & -0.531434468585865 \tabularnewline
164 & 1.1 & 1.3186757551219 & -0.218675755121897 \tabularnewline
165 & 1.4 & 0.996559317294309 & 0.403440682705691 \tabularnewline
166 & 1.8 & 1.15983460473087 & 0.640165395269131 \tabularnewline
167 & 1.9 & 1.41239258220011 & 0.48760741779989 \tabularnewline
168 & 1.6 & 1.60240774845159 & -0.00240774845159075 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204697&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]1.1[/C][C]2.16132478632479[/C][C]-1.06132478632479[/C][/ROW]
[ROW][C]14[/C][C]1.5[/C][C]1.74847961343726[/C][C]-0.248479613437262[/C][/ROW]
[ROW][C]15[/C][C]1.7[/C][C]1.79286997778837[/C][C]-0.0928699777883681[/C][/ROW]
[ROW][C]16[/C][C]1.2[/C][C]1.1679372338624[/C][C]0.032062766137599[/C][/ROW]
[ROW][C]17[/C][C]0.4[/C][C]0.273388960471135[/C][C]0.126611039528865[/C][/ROW]
[ROW][C]18[/C][C]-0.7[/C][C]-0.849512878584956[/C][C]0.149512878584957[/C][/ROW]
[ROW][C]19[/C][C]-1.4[/C][C]0.370239449187077[/C][C]-1.77023944918708[/C][/ROW]
[ROW][C]20[/C][C]-1.6[/C][C]-1.47136665204617[/C][C]-0.128633347953826[/C][/ROW]
[ROW][C]21[/C][C]-1.2[/C][C]-2.66346979593842[/C][C]1.46346979593842[/C][/ROW]
[ROW][C]22[/C][C]-0.4[/C][C]-1.89255262865601[/C][C]1.49255262865601[/C][/ROW]
[ROW][C]23[/C][C]-0.2[/C][C]-1.12399021027744[/C][C]0.92399021027744[/C][/ROW]
[ROW][C]24[/C][C]-0.3[/C][C]0.0339614667132991[/C][C]-0.333961466713299[/C][/ROW]
[ROW][C]25[/C][C]-0.5[/C][C]-0.0744283173584626[/C][C]-0.425571682641537[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.185218805404061[/C][C]-0.185218805404061[/C][/ROW]
[ROW][C]27[/C][C]-0.5[/C][C]0.319678147840291[/C][C]-0.819678147840291[/C][/ROW]
[ROW][C]28[/C][C]0.2[/C][C]-0.918197576796048[/C][C]1.11819757679605[/C][/ROW]
[ROW][C]29[/C][C]0.7[/C][C]-0.832273783217365[/C][C]1.53227378321736[/C][/ROW]
[ROW][C]30[/C][C]1.6[/C][C]-0.687536867872728[/C][C]2.28753686787273[/C][/ROW]
[ROW][C]31[/C][C]2.6[/C][C]2.2204398907392[/C][C]0.379560109260802[/C][/ROW]
[ROW][C]32[/C][C]3.3[/C][C]2.55290456384416[/C][C]0.747095436155841[/C][/ROW]
[ROW][C]33[/C][C]3.3[/C][C]2.42597360295008[/C][C]0.874026397049923[/C][/ROW]
[ROW][C]34[/C][C]3.2[/C][C]2.77740485340257[/C][C]0.42259514659743[/C][/ROW]
[ROW][C]35[/C][C]3.5[/C][C]2.61823091112272[/C][C]0.881769088877277[/C][/ROW]
[ROW][C]36[/C][C]3.9[/C][C]3.65892626453418[/C][C]0.241073735465823[/C][/ROW]
[ROW][C]37[/C][C]4.5[/C][C]4.12680566520871[/C][C]0.373194334791288[/C][/ROW]
[ROW][C]38[/C][C]4.6[/C][C]5.20978321686006[/C][C]-0.60978321686006[/C][/ROW]
[ROW][C]39[/C][C]6.6[/C][C]4.98309530617348[/C][C]1.61690469382652[/C][/ROW]
[ROW][C]40[/C][C]7.1[/C][C]6.2382579401721[/C][C]0.861742059827896[/C][/ROW]
[ROW][C]41[/C][C]8.9[/C][C]6.26866534563128[/C][C]2.63133465436872[/C][/ROW]
[ROW][C]42[/C][C]8.8[/C][C]7.59867157568081[/C][C]1.20132842431919[/C][/ROW]
[ROW][C]43[/C][C]8.5[/C][C]9.433168088583[/C][C]-0.933168088583002[/C][/ROW]
[ROW][C]44[/C][C]7.6[/C][C]8.76556324561258[/C][C]-1.16556324561258[/C][/ROW]
[ROW][C]45[/C][C]7.5[/C][C]7.06083648282386[/C][C]0.439163517176143[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]7.04746406312562[/C][C]0.452535936874376[/C][/ROW]
[ROW][C]47[/C][C]6.1[/C][C]7.04492411774028[/C][C]-0.944924117740281[/C][/ROW]
[ROW][C]48[/C][C]6.3[/C][C]6.45903118025075[/C][C]-0.159031180250747[/C][/ROW]
[ROW][C]49[/C][C]8.4[/C][C]6.63954464978082[/C][C]1.76045535021918[/C][/ROW]
[ROW][C]50[/C][C]7.1[/C][C]8.8729490974383[/C][C]-1.7729490974383[/C][/ROW]
[ROW][C]51[/C][C]5.6[/C][C]7.95925278651166[/C][C]-2.35925278651166[/C][/ROW]
[ROW][C]52[/C][C]4.2[/C][C]5.64497130450139[/C][C]-1.44497130450139[/C][/ROW]
[ROW][C]53[/C][C]2.1[/C][C]3.85541905006195[/C][C]-1.75541905006195[/C][/ROW]
[ROW][C]54[/C][C]1.2[/C][C]1.09100841841662[/C][C]0.108991581583382[/C][/ROW]
[ROW][C]55[/C][C]0.9[/C][C]1.60751808017742[/C][C]-0.70751808017742[/C][/ROW]
[ROW][C]56[/C][C]1.4[/C][C]1.01637485819208[/C][C]0.383625141807923[/C][/ROW]
[ROW][C]57[/C][C]1.7[/C][C]0.795227802201639[/C][C]0.904772197798361[/C][/ROW]
[ROW][C]58[/C][C]1.7[/C][C]1.12339721804527[/C][C]0.576602781954731[/C][/ROW]
[ROW][C]59[/C][C]1.9[/C][C]0.987387173232297[/C][C]0.912612826767703[/C][/ROW]
[ROW][C]60[/C][C]1.3[/C][C]2.08076572395014[/C][C]-0.780765723950141[/C][/ROW]
[ROW][C]61[/C][C]-0.7[/C][C]1.90978949796266[/C][C]-2.60978949796266[/C][/ROW]
[ROW][C]62[/C][C]0.3[/C][C]-0.2248426291178[/C][C]0.5248426291178[/C][/ROW]
[ROW][C]63[/C][C]0.8[/C][C]0.719597458853055[/C][C]0.0804025411469449[/C][/ROW]
[ROW][C]64[/C][C]0.9[/C][C]0.606352504134018[/C][C]0.293647495865982[/C][/ROW]
[ROW][C]65[/C][C]1.1[/C][C]0.271742094161551[/C][C]0.828257905838449[/C][/ROW]
[ROW][C]66[/C][C]2.5[/C][C]0.00646069854972386[/C][C]2.49353930145028[/C][/ROW]
[ROW][C]67[/C][C]2.7[/C][C]2.53856872686052[/C][C]0.161431273139478[/C][/ROW]
[ROW][C]68[/C][C]3.3[/C][C]2.89004581530689[/C][C]0.409954184693107[/C][/ROW]
[ROW][C]69[/C][C]4.2[/C][C]2.80362961473125[/C][C]1.39637038526875[/C][/ROW]
[ROW][C]70[/C][C]3.8[/C][C]3.57181523874331[/C][C]0.228184761256688[/C][/ROW]
[ROW][C]71[/C][C]3.8[/C][C]3.22145577150628[/C][C]0.578544228493716[/C][/ROW]
[ROW][C]72[/C][C]3.2[/C][C]3.85281844805474[/C][C]-0.652818448054742[/C][/ROW]
[ROW][C]73[/C][C]2.9[/C][C]3.6079508271403[/C][C]-0.707950827140305[/C][/ROW]
[ROW][C]74[/C][C]1.9[/C][C]3.59896971094358[/C][C]-1.69896971094358[/C][/ROW]
[ROW][C]75[/C][C]1.7[/C][C]2.58548369311959[/C][C]-0.885483693119594[/C][/ROW]
[ROW][C]76[/C][C]1.6[/C][C]1.68478127013923[/C][C]-0.0847812701392259[/C][/ROW]
[ROW][C]77[/C][C]1.7[/C][C]1.11200250131129[/C][C]0.587997498688711[/C][/ROW]
[ROW][C]78[/C][C]1.2[/C][C]0.869092113923196[/C][C]0.330907886076804[/C][/ROW]
[ROW][C]79[/C][C]0.7[/C][C]1.21310186143154[/C][C]-0.513101861431535[/C][/ROW]
[ROW][C]80[/C][C]-0.2[/C][C]0.994319614365202[/C][C]-1.1943196143652[/C][/ROW]
[ROW][C]81[/C][C]-1.5[/C][C]-0.401017539140786[/C][C]-1.09898246085921[/C][/ROW]
[ROW][C]82[/C][C]-1.2[/C][C]-2.02252611148099[/C][C]0.822526111480992[/C][/ROW]
[ROW][C]83[/C][C]-1[/C][C]-1.86401059242228[/C][C]0.864010592422281[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]-1.18985838763577[/C][C]1.18985838763577[/C][/ROW]
[ROW][C]85[/C][C]-0.6[/C][C]0.139489394397724[/C][C]-0.739489394397724[/C][/ROW]
[ROW][C]86[/C][C]0.7[/C][C]-0.05143233221233[/C][C]0.75143233221233[/C][/ROW]
[ROW][C]87[/C][C]1.3[/C][C]1.17923303758394[/C][C]0.120766962416057[/C][/ROW]
[ROW][C]88[/C][C]0.8[/C][C]1.27138616857728[/C][C]-0.471386168577276[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0.453594521806253[/C][C]0.546405478193747[/C][/ROW]
[ROW][C]90[/C][C]0.5[/C][C]0.149257996416734[/C][C]0.350742003583266[/C][/ROW]
[ROW][C]91[/C][C]0.3[/C][C]0.411669914685628[/C][C]-0.111669914685628[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0.470127937041767[/C][C]0.529872062958233[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0.626716386573908[/C][C]0.373283613426092[/C][/ROW]
[ROW][C]94[/C][C]1.1[/C][C]0.58533809869219[/C][C]0.51466190130781[/C][/ROW]
[ROW][C]95[/C][C]1.5[/C][C]0.527517886890646[/C][C]0.972482113109354[/C][/ROW]
[ROW][C]96[/C][C]1.5[/C][C]1.38632479517586[/C][C]0.113675204824139[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.56751612130097[/C][C]0.432483878699031[/C][/ROW]
[ROW][C]98[/C][C]1.7[/C][C]2.63873136292786[/C][C]-0.938731362927865[/C][/ROW]
[ROW][C]99[/C][C]0.6[/C][C]2.3424143497889[/C][C]-1.7424143497889[/C][/ROW]
[ROW][C]100[/C][C]1.2[/C][C]0.738714907089899[/C][C]0.461285092910101[/C][/ROW]
[ROW][C]101[/C][C]1.5[/C][C]0.882520919262102[/C][C]0.617479080737898[/C][/ROW]
[ROW][C]102[/C][C]2.1[/C][C]0.634631283325982[/C][C]1.46536871667402[/C][/ROW]
[ROW][C]103[/C][C]3.2[/C][C]1.84514167840216[/C][C]1.35485832159784[/C][/ROW]
[ROW][C]104[/C][C]3.9[/C][C]3.31627971687046[/C][C]0.583720283129544[/C][/ROW]
[ROW][C]105[/C][C]4.6[/C][C]3.55109188321913[/C][C]1.04890811678087[/C][/ROW]
[ROW][C]106[/C][C]4.2[/C][C]4.17701869657056[/C][C]0.0229813034294439[/C][/ROW]
[ROW][C]107[/C][C]4.4[/C][C]3.80053378353562[/C][C]0.599466216464382[/C][/ROW]
[ROW][C]108[/C][C]3.7[/C][C]4.27365487745992[/C][C]-0.573654877459917[/C][/ROW]
[ROW][C]109[/C][C]3.7[/C][C]3.93484611568112[/C][C]-0.234846115681116[/C][/ROW]
[ROW][C]110[/C][C]2.8[/C][C]4.28213474764749[/C][C]-1.48213474764749[/C][/ROW]
[ROW][C]111[/C][C]2.9[/C][C]3.43523445430734[/C][C]-0.535234454307336[/C][/ROW]
[ROW][C]112[/C][C]3.9[/C][C]3.20478769732022[/C][C]0.695212302679778[/C][/ROW]
[ROW][C]113[/C][C]3.1[/C][C]3.61582707767208[/C][C]-0.515827077672078[/C][/ROW]
[ROW][C]114[/C][C]3[/C][C]2.51410882675452[/C][C]0.48589117324548[/C][/ROW]
[ROW][C]115[/C][C]2.8[/C][C]2.87237128377707[/C][C]-0.0723712837770671[/C][/ROW]
[ROW][C]116[/C][C]2.4[/C][C]2.99936292754749[/C][C]-0.599362927547494[/C][/ROW]
[ROW][C]117[/C][C]2.1[/C][C]2.2450928208159[/C][C]-0.145092820815899[/C][/ROW]
[ROW][C]118[/C][C]3.1[/C][C]1.6697279513008[/C][C]1.4302720486992[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]2.58419348139777[/C][C]0.415806518602226[/C][/ROW]
[ROW][C]120[/C][C]3.1[/C][C]2.7350650507587[/C][C]0.364934949241295[/C][/ROW]
[ROW][C]121[/C][C]3.3[/C][C]3.25680518527092[/C][C]0.043194814729083[/C][/ROW]
[ROW][C]122[/C][C]3.3[/C][C]3.69063145387787[/C][C]-0.390631453877872[/C][/ROW]
[ROW][C]123[/C][C]3.8[/C][C]3.93122644248233[/C][C]-0.131226442482334[/C][/ROW]
[ROW][C]124[/C][C]3.1[/C][C]4.22824039117842[/C][C]-1.12824039117842[/C][/ROW]
[ROW][C]125[/C][C]3.9[/C][C]2.89019211483504[/C][C]1.00980788516496[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]3.26350709549048[/C][C]0.736492904509524[/C][/ROW]
[ROW][C]127[/C][C]4.4[/C][C]3.78883531268489[/C][C]0.611164687315107[/C][/ROW]
[ROW][C]128[/C][C]3.7[/C][C]4.47340066809534[/C][C]-0.773400668095335[/C][/ROW]
[ROW][C]129[/C][C]3.6[/C][C]3.64913205916812[/C][C]-0.0491320591681181[/C][/ROW]
[ROW][C]130[/C][C]3.4[/C][C]3.38235891326187[/C][C]0.0176410867381334[/C][/ROW]
[ROW][C]131[/C][C]2.8[/C][C]2.94324508536713[/C][C]-0.143245085367128[/C][/ROW]
[ROW][C]132[/C][C]2.8[/C][C]2.60123622142278[/C][C]0.19876377857722[/C][/ROW]
[ROW][C]133[/C][C]2.6[/C][C]2.93718278869289[/C][C]-0.337182788692885[/C][/ROW]
[ROW][C]134[/C][C]3.3[/C][C]2.97929493758522[/C][C]0.320705062414783[/C][/ROW]
[ROW][C]135[/C][C]2.4[/C][C]3.87820162383337[/C][C]-1.47820162383337[/C][/ROW]
[ROW][C]136[/C][C]1.6[/C][C]2.8601303903348[/C][C]-1.2601303903348[/C][/ROW]
[ROW][C]137[/C][C]0.7[/C][C]1.66283593999393[/C][C]-0.962835939993929[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.240247327148995[/C][C]-0.240247327148995[/C][/ROW]
[ROW][C]139[/C][C]-1.1[/C][C]-0.153276335585294[/C][C]-0.946723664414706[/C][/ROW]
[ROW][C]140[/C][C]-1.2[/C][C]-1.07316080318054[/C][C]-0.126839196819462[/C][/ROW]
[ROW][C]141[/C][C]-1.3[/C][C]-1.30167221652161[/C][C]0.00167221652160943[/C][/ROW]
[ROW][C]142[/C][C]-1.6[/C][C]-1.57562334843447[/C][C]-0.0243766515655313[/C][/ROW]
[ROW][C]143[/C][C]-1.3[/C][C]-2.13226570710735[/C][C]0.832265707107351[/C][/ROW]
[ROW][C]144[/C][C]-1.6[/C][C]-1.62742633881833[/C][C]0.0274263388183269[/C][/ROW]
[ROW][C]145[/C][C]-1.1[/C][C]-1.55961258071325[/C][C]0.459612580713247[/C][/ROW]
[ROW][C]146[/C][C]-1[/C][C]-0.779366217117632[/C][C]-0.220633782882368[/C][/ROW]
[ROW][C]147[/C][C]0.3[/C][C]-0.628215818261119[/C][C]0.928215818261119[/C][/ROW]
[ROW][C]148[/C][C]1.2[/C][C]0.465353584562026[/C][C]0.734646415437974[/C][/ROW]
[ROW][C]149[/C][C]0.7[/C][C]1.05416470319435[/C][C]-0.354164703194354[/C][/ROW]
[ROW][C]150[/C][C]1.1[/C][C]0.26758887389361[/C][C]0.83241112610639[/C][/ROW]
[ROW][C]151[/C][C]2.1[/C][C]0.748097618981745[/C][C]1.35190238101826[/C][/ROW]
[ROW][C]152[/C][C]2.5[/C][C]1.99395441786879[/C][C]0.50604558213121[/C][/ROW]
[ROW][C]153[/C][C]2.3[/C][C]2.39609405693151[/C][C]-0.096094056931511[/C][/ROW]
[ROW][C]154[/C][C]2.3[/C][C]2.09367692093069[/C][C]0.206323079069307[/C][/ROW]
[ROW][C]155[/C][C]2.6[/C][C]1.90978451480592[/C][C]0.690215485194081[/C][/ROW]
[ROW][C]156[/C][C]3.2[/C][C]2.25023344897957[/C][C]0.949766551020427[/C][/ROW]
[ROW][C]157[/C][C]2.2[/C][C]3.25100274804419[/C][C]-1.05100274804419[/C][/ROW]
[ROW][C]158[/C][C]2.7[/C][C]2.67964755183004[/C][C]0.0203524481699642[/C][/ROW]
[ROW][C]159[/C][C]2.2[/C][C]3.24349677194525[/C][C]-1.04349677194525[/C][/ROW]
[ROW][C]160[/C][C]1.4[/C][C]2.62305099887471[/C][C]-1.22305099887471[/C][/ROW]
[ROW][C]161[/C][C]2.4[/C][C]1.37339345705472[/C][C]1.02660654294528[/C][/ROW]
[ROW][C]162[/C][C]2[/C][C]1.96934315623615[/C][C]0.0306568437638501[/C][/ROW]
[ROW][C]163[/C][C]1.3[/C][C]1.83143446858586[/C][C]-0.531434468585865[/C][/ROW]
[ROW][C]164[/C][C]1.1[/C][C]1.3186757551219[/C][C]-0.218675755121897[/C][/ROW]
[ROW][C]165[/C][C]1.4[/C][C]0.996559317294309[/C][C]0.403440682705691[/C][/ROW]
[ROW][C]166[/C][C]1.8[/C][C]1.15983460473087[/C][C]0.640165395269131[/C][/ROW]
[ROW][C]167[/C][C]1.9[/C][C]1.41239258220011[/C][C]0.48760741779989[/C][/ROW]
[ROW][C]168[/C][C]1.6[/C][C]1.60240774845159[/C][C]-0.00240774845159075[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204697&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204697&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
131.12.16132478632479-1.06132478632479
141.51.74847961343726-0.248479613437262
151.71.79286997778837-0.0928699777883681
161.21.16793723386240.032062766137599
170.40.2733889604711350.126611039528865
18-0.7-0.8495128785849560.149512878584957
19-1.40.370239449187077-1.77023944918708
20-1.6-1.47136665204617-0.128633347953826
21-1.2-2.663469795938421.46346979593842
22-0.4-1.892552628656011.49255262865601
23-0.2-1.123990210277440.92399021027744
24-0.30.0339614667132991-0.333961466713299
25-0.5-0.0744283173584626-0.425571682641537
2600.185218805404061-0.185218805404061
27-0.50.319678147840291-0.819678147840291
280.2-0.9181975767960481.11819757679605
290.7-0.8322737832173651.53227378321736
301.6-0.6875368678727282.28753686787273
312.62.22043989073920.379560109260802
323.32.552904563844160.747095436155841
333.32.425973602950080.874026397049923
343.22.777404853402570.42259514659743
353.52.618230911122720.881769088877277
363.93.658926264534180.241073735465823
374.54.126805665208710.373194334791288
384.65.20978321686006-0.60978321686006
396.64.983095306173481.61690469382652
407.16.23825794017210.861742059827896
418.96.268665345631282.63133465436872
428.87.598671575680811.20132842431919
438.59.433168088583-0.933168088583002
447.68.76556324561258-1.16556324561258
457.57.060836482823860.439163517176143
467.57.047464063125620.452535936874376
476.17.04492411774028-0.944924117740281
486.36.45903118025075-0.159031180250747
498.46.639544649780821.76045535021918
507.18.8729490974383-1.7729490974383
515.67.95925278651166-2.35925278651166
524.25.64497130450139-1.44497130450139
532.13.85541905006195-1.75541905006195
541.21.091008418416620.108991581583382
550.91.60751808017742-0.70751808017742
561.41.016374858192080.383625141807923
571.70.7952278022016390.904772197798361
581.71.123397218045270.576602781954731
591.90.9873871732322970.912612826767703
601.32.08076572395014-0.780765723950141
61-0.71.90978949796266-2.60978949796266
620.3-0.22484262911780.5248426291178
630.80.7195974588530550.0804025411469449
640.90.6063525041340180.293647495865982
651.10.2717420941615510.828257905838449
662.50.006460698549723862.49353930145028
672.72.538568726860520.161431273139478
683.32.890045815306890.409954184693107
694.22.803629614731251.39637038526875
703.83.571815238743310.228184761256688
713.83.221455771506280.578544228493716
723.23.85281844805474-0.652818448054742
732.93.6079508271403-0.707950827140305
741.93.59896971094358-1.69896971094358
751.72.58548369311959-0.885483693119594
761.61.68478127013923-0.0847812701392259
771.71.112002501311290.587997498688711
781.20.8690921139231960.330907886076804
790.71.21310186143154-0.513101861431535
80-0.20.994319614365202-1.1943196143652
81-1.5-0.401017539140786-1.09898246085921
82-1.2-2.022526111480990.822526111480992
83-1-1.864010592422280.864010592422281
840-1.189858387635771.18985838763577
85-0.60.139489394397724-0.739489394397724
860.7-0.051432332212330.75143233221233
871.31.179233037583940.120766962416057
880.81.27138616857728-0.471386168577276
8910.4535945218062530.546405478193747
900.50.1492579964167340.350742003583266
910.30.411669914685628-0.111669914685628
9210.4701279370417670.529872062958233
9310.6267163865739080.373283613426092
941.10.585338098692190.51466190130781
951.50.5275178868906460.972482113109354
961.51.386324795175860.113675204824139
9721.567516121300970.432483878699031
981.72.63873136292786-0.938731362927865
990.62.3424143497889-1.7424143497889
1001.20.7387149070898990.461285092910101
1011.50.8825209192621020.617479080737898
1022.10.6346312833259821.46536871667402
1033.21.845141678402161.35485832159784
1043.93.316279716870460.583720283129544
1054.63.551091883219131.04890811678087
1064.24.177018696570560.0229813034294439
1074.43.800533783535620.599466216464382
1083.74.27365487745992-0.573654877459917
1093.73.93484611568112-0.234846115681116
1102.84.28213474764749-1.48213474764749
1112.93.43523445430734-0.535234454307336
1123.93.204787697320220.695212302679778
1133.13.61582707767208-0.515827077672078
11432.514108826754520.48589117324548
1152.82.87237128377707-0.0723712837770671
1162.42.99936292754749-0.599362927547494
1172.12.2450928208159-0.145092820815899
1183.11.66972795130081.4302720486992
11932.584193481397770.415806518602226
1203.12.73506505075870.364934949241295
1213.33.256805185270920.043194814729083
1223.33.69063145387787-0.390631453877872
1233.83.93122644248233-0.131226442482334
1243.14.22824039117842-1.12824039117842
1253.92.890192114835041.00980788516496
12643.263507095490480.736492904509524
1274.43.788835312684890.611164687315107
1283.74.47340066809534-0.773400668095335
1293.63.64913205916812-0.0491320591681181
1303.43.382358913261870.0176410867381334
1312.82.94324508536713-0.143245085367128
1322.82.601236221422780.19876377857722
1332.62.93718278869289-0.337182788692885
1343.32.979294937585220.320705062414783
1352.43.87820162383337-1.47820162383337
1361.62.8601303903348-1.2601303903348
1370.71.66283593999393-0.962835939993929
13800.240247327148995-0.240247327148995
139-1.1-0.153276335585294-0.946723664414706
140-1.2-1.07316080318054-0.126839196819462
141-1.3-1.301672216521610.00167221652160943
142-1.6-1.57562334843447-0.0243766515655313
143-1.3-2.132265707107350.832265707107351
144-1.6-1.627426338818330.0274263388183269
145-1.1-1.559612580713250.459612580713247
146-1-0.779366217117632-0.220633782882368
1470.3-0.6282158182611190.928215818261119
1481.20.4653535845620260.734646415437974
1490.71.05416470319435-0.354164703194354
1501.10.267588873893610.83241112610639
1512.10.7480976189817451.35190238101826
1522.51.993954417868790.50604558213121
1532.32.39609405693151-0.096094056931511
1542.32.093676920930690.206323079069307
1552.61.909784514805920.690215485194081
1563.22.250233448979570.949766551020427
1572.23.25100274804419-1.05100274804419
1582.72.679647551830040.0203524481699642
1592.23.24349677194525-1.04349677194525
1601.42.62305099887471-1.22305099887471
1612.41.373393457054721.02660654294528
16221.969343156236150.0306568437638501
1631.31.83143446858586-0.531434468585865
1641.11.3186757551219-0.218675755121897
1651.40.9965593172943090.403440682705691
1661.81.159834604730870.640165395269131
1671.91.412392582200110.48760741779989
1681.61.60240774845159-0.00240774845159075







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
1691.50095547576778-0.2725074733275763.27441842486313
1701.97820628098438-0.3910642592192884.34747682118805
1712.38477491578446-0.4699804920886355.23953032365756
1722.6608645426153-0.6185636299792065.94029271520981
1732.78606559742683-0.8785526177541376.45068381260779
1742.36907531843744-1.652725606440736.3908762433156
1752.14285493601219-2.215119232937456.50082910496182
1762.14978680500374-2.527981558838346.82755516884582
1772.11577625435842-2.868653339288297.10020584800513
1781.97003822834008-3.310295572764887.25037202944504
1791.64985250865528-3.917426769956017.21713178726657
1801.35192719272612-4.494736863639017.19859124909125

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
169 & 1.50095547576778 & -0.272507473327576 & 3.27441842486313 \tabularnewline
170 & 1.97820628098438 & -0.391064259219288 & 4.34747682118805 \tabularnewline
171 & 2.38477491578446 & -0.469980492088635 & 5.23953032365756 \tabularnewline
172 & 2.6608645426153 & -0.618563629979206 & 5.94029271520981 \tabularnewline
173 & 2.78606559742683 & -0.878552617754137 & 6.45068381260779 \tabularnewline
174 & 2.36907531843744 & -1.65272560644073 & 6.3908762433156 \tabularnewline
175 & 2.14285493601219 & -2.21511923293745 & 6.50082910496182 \tabularnewline
176 & 2.14978680500374 & -2.52798155883834 & 6.82755516884582 \tabularnewline
177 & 2.11577625435842 & -2.86865333928829 & 7.10020584800513 \tabularnewline
178 & 1.97003822834008 & -3.31029557276488 & 7.25037202944504 \tabularnewline
179 & 1.64985250865528 & -3.91742676995601 & 7.21713178726657 \tabularnewline
180 & 1.35192719272612 & -4.49473686363901 & 7.19859124909125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204697&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]169[/C][C]1.50095547576778[/C][C]-0.272507473327576[/C][C]3.27441842486313[/C][/ROW]
[ROW][C]170[/C][C]1.97820628098438[/C][C]-0.391064259219288[/C][C]4.34747682118805[/C][/ROW]
[ROW][C]171[/C][C]2.38477491578446[/C][C]-0.469980492088635[/C][C]5.23953032365756[/C][/ROW]
[ROW][C]172[/C][C]2.6608645426153[/C][C]-0.618563629979206[/C][C]5.94029271520981[/C][/ROW]
[ROW][C]173[/C][C]2.78606559742683[/C][C]-0.878552617754137[/C][C]6.45068381260779[/C][/ROW]
[ROW][C]174[/C][C]2.36907531843744[/C][C]-1.65272560644073[/C][C]6.3908762433156[/C][/ROW]
[ROW][C]175[/C][C]2.14285493601219[/C][C]-2.21511923293745[/C][C]6.50082910496182[/C][/ROW]
[ROW][C]176[/C][C]2.14978680500374[/C][C]-2.52798155883834[/C][C]6.82755516884582[/C][/ROW]
[ROW][C]177[/C][C]2.11577625435842[/C][C]-2.86865333928829[/C][C]7.10020584800513[/C][/ROW]
[ROW][C]178[/C][C]1.97003822834008[/C][C]-3.31029557276488[/C][C]7.25037202944504[/C][/ROW]
[ROW][C]179[/C][C]1.64985250865528[/C][C]-3.91742676995601[/C][C]7.21713178726657[/C][/ROW]
[ROW][C]180[/C][C]1.35192719272612[/C][C]-4.49473686363901[/C][C]7.19859124909125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204697&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204697&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
1691.50095547576778-0.2725074733275763.27441842486313
1701.97820628098438-0.3910642592192884.34747682118805
1712.38477491578446-0.4699804920886355.23953032365756
1722.6608645426153-0.6185636299792065.94029271520981
1732.78606559742683-0.8785526177541376.45068381260779
1742.36907531843744-1.652725606440736.3908762433156
1752.14285493601219-2.215119232937456.50082910496182
1762.14978680500374-2.527981558838346.82755516884582
1772.11577625435842-2.868653339288297.10020584800513
1781.97003822834008-3.310295572764887.25037202944504
1791.64985250865528-3.917426769956017.21713178726657
1801.35192719272612-4.494736863639017.19859124909125



Parameters (Session):
par1 = 12 ; par2 = Triple ; par3 = additive ;
Parameters (R input):
par1 = 12 ; par2 = Triple ; par3 = additive ;
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')