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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationFri, 22 Dec 2017 18:23:35 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/22/t1513963443xbr7czn55z0tz50.htm/, Retrieved Wed, 15 May 2024 21:59:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310814, Retrieved Wed, 15 May 2024 21:59:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [] [2017-12-22 17:23:35] [ec772448347bb766a411d58621b503be] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
53,1
64,1
75,3
66
73,6
73,2
53,5
60,6
73
72,4
75,8
79,6
77,8
75,7
88,5
72,9
80,8
86,6
63,8
69,2
76,5
77,1
75,3
69,5
64,3
66,7
77,3
75,3
73,4
78
61
58,4
73,4
82,3
72,2
76
64,3
70,8
74
71,4
70,1
77,6
61,2
52,1
74,4
73,1
70,9
80,7
62,9
69,3
82,3
76,2
70,8
87,3
62
66,9
84,4
82,6
77,7
87
76
76,3
88,8
81,2
74,5
98,1
63,3
67,7
85,8
78,6
87,2
106,4
75
80,4
94,8
77
91
96,7
69,2
69,5
93,7
98,5
93,3
100,4
87,4
89
106,1
92,5
96,6
113,3
87,6
89,2
115,6
133,2
111,1
113,1
102
109,3
111,1
116,8
107,5
120,5
95,5
87,9
118,6
116,3
98,8
102,9
80,4
87
97,4
87,2
110,6
101,1
69,1
77,4
95
93,2
96,3
93,9
78,5
90
109,2
94,3
93,1
114,5
78,5
88,3
114,8
112,2
106,9
119,7
97,1
106,3
131,7
106,7
124
117,2
87,8
91,9
125,1
115,4
117,7
124,3
104,8
109,6
139,5
105,3
112,4
128,9
91,6
98,7
117,8
117,4
110,5
103,1
95,8
98,2
117,2
108,5
113,2
120,2
102,8
89,4
119,8
126,9
114,4
117,4
109,4
111,1
121
116,6
119,5
121,2
101
92,7
125,5
123,4
110,3
118,8
97,1
107,6
131
117,9
111
131,4
101,8
93,9
138,5
131,1
124,9
126,6
102,7
121,6
132,8
123
116
135
93,7
98,4
129,8
121,9
124,8
126,9
102
117,7
144,8
113,3
129,3
135,7
94,3
106

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310814&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]2 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310814&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310814&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.347618454292133
beta0.00855146527303489
gamma0.304774992407583

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.347618454292133 \tabularnewline
beta & 0.00855146527303489 \tabularnewline
gamma & 0.304774992407583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310814&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]0.347618454292133[/C][/ROW]
[ROW][C]beta[/C][C]0.00855146527303489[/C][/ROW]
[ROW][C]gamma[/C][C]0.304774992407583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310814&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310814&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.347618454292133
beta0.00855146527303489
gamma0.304774992407583






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
1377.872.53023110580895.26976889419109
1475.772.29904751700333.40095248299673
1588.586.15766539514972.34233460485029
1672.971.99902204916040.900977950839646
1780.880.71728350087570.0827164991243166
1886.687.8511169099747-1.25111690997468
1963.860.14517825670763.65482174329239
2069.268.70310475373040.496895246269574
2176.582.5165169930587-6.01651699305869
2277.179.5531804355462-2.45318043554622
2375.382.4632685369584-7.16326853695837
2469.583.7434589978017-14.2434589978017
2564.377.7449845962815-13.4449845962815
2666.770.6314472308958-3.93144723089577
2777.380.8268011322036-3.52680113220364
2875.365.64716570372269.65283429627735
2973.476.7786899702403-3.37868997024025
307881.9328375654829-3.93283756548291
316156.19802924381124.80197075618877
3258.464.0140932728195-5.61409327281954
3373.473.08125682275780.318743177242254
3482.372.97492695383799.32507304616215
3572.278.863713921616-6.66371392161604
367678.6117445696318-2.6117445696318
3764.376.5569378187737-12.2569378187737
3870.871.6556899498068-0.855689949806759
397483.4072499304132-9.40724993041317
4071.468.47173214581662.92826785418343
4170.174.4965267370744-4.39652673707444
4277.678.9434392965454-1.34343929654544
4361.256.14196239878925.05803760121084
4452.161.8279403345581-9.72794033455813
4574.470.08495242562594.31504757437413
4673.172.98669426931830.113305730681688
4770.972.4320922622694-1.53209226226943
4880.774.57764992331976.12235007668031
4962.973.4632485499874-10.5632485499874
5069.371.3610357798767-2.06103577987673
5182.380.77693667481671.52306332518334
5276.271.67635569163444.52364430836562
5370.876.8892793589604-6.0892793589604
5487.381.5413226117725.75867738822799
556260.97382627964781.02617372035223
5666.962.20216843349644.69783156650356
5784.480.07343857480174.32656142519828
5882.682.17290727961930.427092720380656
5977.781.2397838589347-3.5397838589347
608784.62160244657292.37839755342715
617678.092125096857-2.09212509685702
6276.381.0485573929021-4.74855739290207
6388.891.6110325432112-2.8110325432112
6481.280.53883797487280.661162025127226
6574.582.3542503951959-7.85425039519593
6698.189.42110964584048.67889035415955
6763.366.7406733560785-3.44067335607853
6867.767.18763859545380.512361404546184
6985.884.1118933046111.68810669538902
7078.684.4630639621735-5.86306396217356
7187.280.49067146212926.7093285378708
72106.488.815259072766417.5847409272336
737585.7797978336219-10.7797978336219
7480.485.3341865496782-4.93418654967816
7594.896.9930186137364-2.19301861373638
767786.1415346062038-9.14153460620376
779182.75050894867138.24949105132872
7896.799.8666549841286-3.16665498412864
7969.269.2368499913809-0.036849991380862
8069.571.7831141675974-2.28311416759745
8193.788.80959121778444.89040878221559
8298.588.5902308418249.90976915817605
8393.392.60715401547550.692845984524467
84100.4101.562049023186-1.16204902318599
8587.485.81561045120221.58438954879782
868991.1364988456566-2.1364988456566
87106.1105.5794691276070.520530872392769
8892.592.9905113031285-0.490511303128471
8996.696.44794586529040.152054134709616
90113.3109.7073443475813.59265565241881
9187.678.2549444361789.34505556382199
9289.283.97550179987565.2244982001244
93115.6109.1766757100786.42332428992158
94133.2110.19611345418823.0038865458115
95111.1116.615889604878-5.51588960487771
96113.1124.993687056761-11.8936870567607
97102103.131639308962-1.13163930896212
98109.3107.501064179811.79893582019018
99111.1127.005026525171-15.9050265251712
100116.8106.57191490130410.228085098696
101107.5114.578692462169-7.07869246216929
102120.5128.223107809899-7.72310780989878
10395.589.95629860137265.54370139862741
10487.993.6530535958856-5.75305359588562
105118.6116.512169373692.08783062630977
106116.3119.028957360949-2.72895736094934
10798.8111.017402195623-12.2174021956234
108102.9115.092032889309-12.1920328893093
10980.496.1949358462308-15.7949358462308
1108795.3380724949111-8.33807249491112
11197.4105.286387748707-7.88638774870697
11287.293.9722172126679-6.77221721266794
113110.692.300048312685618.2999516873144
114101.1112.766495841657-11.6664958416571
11569.179.7297029309098-10.6297029309098
11677.475.52946627335871.87053372664131
1179598.3009084342013-3.30090843420132
11893.297.7204880177633-4.52048801776326
11996.388.63936516090097.66063483909907
12093.998.4129618447657-4.51296184476566
12178.582.8682718761251-4.36827187612508
1229086.91918763448053.08081236551955
123109.2100.4322608464388.76773915356198
12494.394.8571398061828-0.557139806182846
12593.1100.339391112921-7.23939111292059
126114.5105.467058626729.03294137327978
12778.579.1033031370663-0.603303137066291
12888.380.99444718852857.30555281147154
129114.8106.5411260915768.25887390842423
130112.2109.7805804668652.41941953313496
131106.9104.6886139747442.21138602525623
132119.7110.7866491387068.91335086129436
13397.197.3448088596709-0.244808859670869
134106.3105.8159477118780.484052288122214
135131.7122.2035921363469.49640786365359
136106.7113.04686644233-6.34686644233048
137124115.9243104040238.07568959597738
138117.2132.133509237866-14.9335092378663
13987.890.8310052931415-3.0310052931415
14091.993.9259722681532-2.02597226815317
141125.1118.6140272716336.48597272836729
142115.4119.993300594179-4.5933005941792
143117.7112.0080717303025.69192826969804
144124.3121.091334456743.20866554326007
145104.8102.7858518733632.01414812663693
146109.6112.730815744505-3.13081574450482
147139.5130.5189392995958.98106070040527
148105.3117.216203687084-11.9162036870841
149112.4121.204834907321-8.80483490732071
150128.9126.6272732624562.27272673754418
15191.692.6999769098307-1.09997690983073
15298.796.7904240698461.90957593015403
153117.8125.8413829559-8.04138295589968
154117.4119.886689282542-2.48668928254222
155110.5114.560870106929-4.06087010692939
156103.1119.582211454626-16.4822114546256
15795.895.55634970586960.243650294130347
15898.2103.1224319735-4.92243197350015
159117.2120.714658682367-3.5146586823665
160108.5101.1578914818837.34210851811673
161113.2111.7872765493081.41272345069157
162120.2122.5168855497-2.31688554969975
163102.887.973717211641714.8262827883583
16489.498.224442619882-8.82444261988195
165119.8120.749658178722-0.949658178722302
166126.9118.3330002789838.56699972101674
167114.4116.388710769593-1.98871076959254
168117.4119.586750542357-2.18675054235682
169109.4102.741648491946.65835150805954
170111.1112.139484814007-1.03948481400714
171121133.593446305272-12.5934463052721
172116.6111.5910107964395.00898920356103
173119.5120.76892382046-1.26892382046009
174121.2130.47629968166-9.27629968165989
17510195.2404195985265.75958040147401
17692.797.5568557064324-4.8568557064324
177125.5123.7171936454641.78280635453621
178123.4124.071491312643-0.671491312642601
179110.3116.731707071933-6.43170707193337
180118.8118.2713176006310.528682399368634
18197.1104.057005824711-6.95700582471089
182107.6106.8814260742190.718573925780944
183131125.7225259529765.27747404702431
184117.9113.2899263082634.61007369173718
185111121.075426378003-10.0754263780029
186131.4125.8508732040525.54912679594848
187101.898.16425332992053.63574667007951
18893.997.5709256099753-3.67092560997534
189138.5125.8608729158612.6391270841397
190131.1129.4586434488331.64135655116655
191124.9121.3255862982263.57441370177392
192126.6128.169830578679-1.56983057867933
193102.7110.498327196717-7.79832719671661
194121.6115.0750160791826.52498392081843
195132.8138.680081589094-5.88008158909361
196123121.3260460174981.67395398250217
197116125.276170271182-9.2761702711821
198135134.0552854719280.944714528071813
19993.7103.089212413512-9.38921241351248
20098.496.48373138015611.91626861984389
201129.8130.387484691923-0.587484691922754
202121.9127.203828348994-5.30382834899393
203124.8117.2907459166197.50925408338144
204126.9124.2971687703762.60283122962406
205102107.062709304342-5.06270930434151
206117.7115.2848291376522.41517086234754
207144.8134.5229785749310.2770214250702
208113.3123.996810451999-10.6968104519994
209129.3121.357273767587.94272623241955
210135.7138.597540806895-2.89754080689482
21194.3103.415284567036-9.11528456703566
21210699.110193617616.88980638239003

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 77.8 & 72.5302311058089 & 5.26976889419109 \tabularnewline
14 & 75.7 & 72.2990475170033 & 3.40095248299673 \tabularnewline
15 & 88.5 & 86.1576653951497 & 2.34233460485029 \tabularnewline
16 & 72.9 & 71.9990220491604 & 0.900977950839646 \tabularnewline
17 & 80.8 & 80.7172835008757 & 0.0827164991243166 \tabularnewline
18 & 86.6 & 87.8511169099747 & -1.25111690997468 \tabularnewline
19 & 63.8 & 60.1451782567076 & 3.65482174329239 \tabularnewline
20 & 69.2 & 68.7031047537304 & 0.496895246269574 \tabularnewline
21 & 76.5 & 82.5165169930587 & -6.01651699305869 \tabularnewline
22 & 77.1 & 79.5531804355462 & -2.45318043554622 \tabularnewline
23 & 75.3 & 82.4632685369584 & -7.16326853695837 \tabularnewline
24 & 69.5 & 83.7434589978017 & -14.2434589978017 \tabularnewline
25 & 64.3 & 77.7449845962815 & -13.4449845962815 \tabularnewline
26 & 66.7 & 70.6314472308958 & -3.93144723089577 \tabularnewline
27 & 77.3 & 80.8268011322036 & -3.52680113220364 \tabularnewline
28 & 75.3 & 65.6471657037226 & 9.65283429627735 \tabularnewline
29 & 73.4 & 76.7786899702403 & -3.37868997024025 \tabularnewline
30 & 78 & 81.9328375654829 & -3.93283756548291 \tabularnewline
31 & 61 & 56.1980292438112 & 4.80197075618877 \tabularnewline
32 & 58.4 & 64.0140932728195 & -5.61409327281954 \tabularnewline
33 & 73.4 & 73.0812568227578 & 0.318743177242254 \tabularnewline
34 & 82.3 & 72.9749269538379 & 9.32507304616215 \tabularnewline
35 & 72.2 & 78.863713921616 & -6.66371392161604 \tabularnewline
36 & 76 & 78.6117445696318 & -2.6117445696318 \tabularnewline
37 & 64.3 & 76.5569378187737 & -12.2569378187737 \tabularnewline
38 & 70.8 & 71.6556899498068 & -0.855689949806759 \tabularnewline
39 & 74 & 83.4072499304132 & -9.40724993041317 \tabularnewline
40 & 71.4 & 68.4717321458166 & 2.92826785418343 \tabularnewline
41 & 70.1 & 74.4965267370744 & -4.39652673707444 \tabularnewline
42 & 77.6 & 78.9434392965454 & -1.34343929654544 \tabularnewline
43 & 61.2 & 56.1419623987892 & 5.05803760121084 \tabularnewline
44 & 52.1 & 61.8279403345581 & -9.72794033455813 \tabularnewline
45 & 74.4 & 70.0849524256259 & 4.31504757437413 \tabularnewline
46 & 73.1 & 72.9866942693183 & 0.113305730681688 \tabularnewline
47 & 70.9 & 72.4320922622694 & -1.53209226226943 \tabularnewline
48 & 80.7 & 74.5776499233197 & 6.12235007668031 \tabularnewline
49 & 62.9 & 73.4632485499874 & -10.5632485499874 \tabularnewline
50 & 69.3 & 71.3610357798767 & -2.06103577987673 \tabularnewline
51 & 82.3 & 80.7769366748167 & 1.52306332518334 \tabularnewline
52 & 76.2 & 71.6763556916344 & 4.52364430836562 \tabularnewline
53 & 70.8 & 76.8892793589604 & -6.0892793589604 \tabularnewline
54 & 87.3 & 81.541322611772 & 5.75867738822799 \tabularnewline
55 & 62 & 60.9738262796478 & 1.02617372035223 \tabularnewline
56 & 66.9 & 62.2021684334964 & 4.69783156650356 \tabularnewline
57 & 84.4 & 80.0734385748017 & 4.32656142519828 \tabularnewline
58 & 82.6 & 82.1729072796193 & 0.427092720380656 \tabularnewline
59 & 77.7 & 81.2397838589347 & -3.5397838589347 \tabularnewline
60 & 87 & 84.6216024465729 & 2.37839755342715 \tabularnewline
61 & 76 & 78.092125096857 & -2.09212509685702 \tabularnewline
62 & 76.3 & 81.0485573929021 & -4.74855739290207 \tabularnewline
63 & 88.8 & 91.6110325432112 & -2.8110325432112 \tabularnewline
64 & 81.2 & 80.5388379748728 & 0.661162025127226 \tabularnewline
65 & 74.5 & 82.3542503951959 & -7.85425039519593 \tabularnewline
66 & 98.1 & 89.4211096458404 & 8.67889035415955 \tabularnewline
67 & 63.3 & 66.7406733560785 & -3.44067335607853 \tabularnewline
68 & 67.7 & 67.1876385954538 & 0.512361404546184 \tabularnewline
69 & 85.8 & 84.111893304611 & 1.68810669538902 \tabularnewline
70 & 78.6 & 84.4630639621735 & -5.86306396217356 \tabularnewline
71 & 87.2 & 80.4906714621292 & 6.7093285378708 \tabularnewline
72 & 106.4 & 88.8152590727664 & 17.5847409272336 \tabularnewline
73 & 75 & 85.7797978336219 & -10.7797978336219 \tabularnewline
74 & 80.4 & 85.3341865496782 & -4.93418654967816 \tabularnewline
75 & 94.8 & 96.9930186137364 & -2.19301861373638 \tabularnewline
76 & 77 & 86.1415346062038 & -9.14153460620376 \tabularnewline
77 & 91 & 82.7505089486713 & 8.24949105132872 \tabularnewline
78 & 96.7 & 99.8666549841286 & -3.16665498412864 \tabularnewline
79 & 69.2 & 69.2368499913809 & -0.036849991380862 \tabularnewline
80 & 69.5 & 71.7831141675974 & -2.28311416759745 \tabularnewline
81 & 93.7 & 88.8095912177844 & 4.89040878221559 \tabularnewline
82 & 98.5 & 88.590230841824 & 9.90976915817605 \tabularnewline
83 & 93.3 & 92.6071540154755 & 0.692845984524467 \tabularnewline
84 & 100.4 & 101.562049023186 & -1.16204902318599 \tabularnewline
85 & 87.4 & 85.8156104512022 & 1.58438954879782 \tabularnewline
86 & 89 & 91.1364988456566 & -2.1364988456566 \tabularnewline
87 & 106.1 & 105.579469127607 & 0.520530872392769 \tabularnewline
88 & 92.5 & 92.9905113031285 & -0.490511303128471 \tabularnewline
89 & 96.6 & 96.4479458652904 & 0.152054134709616 \tabularnewline
90 & 113.3 & 109.707344347581 & 3.59265565241881 \tabularnewline
91 & 87.6 & 78.254944436178 & 9.34505556382199 \tabularnewline
92 & 89.2 & 83.9755017998756 & 5.2244982001244 \tabularnewline
93 & 115.6 & 109.176675710078 & 6.42332428992158 \tabularnewline
94 & 133.2 & 110.196113454188 & 23.0038865458115 \tabularnewline
95 & 111.1 & 116.615889604878 & -5.51588960487771 \tabularnewline
96 & 113.1 & 124.993687056761 & -11.8936870567607 \tabularnewline
97 & 102 & 103.131639308962 & -1.13163930896212 \tabularnewline
98 & 109.3 & 107.50106417981 & 1.79893582019018 \tabularnewline
99 & 111.1 & 127.005026525171 & -15.9050265251712 \tabularnewline
100 & 116.8 & 106.571914901304 & 10.228085098696 \tabularnewline
101 & 107.5 & 114.578692462169 & -7.07869246216929 \tabularnewline
102 & 120.5 & 128.223107809899 & -7.72310780989878 \tabularnewline
103 & 95.5 & 89.9562986013726 & 5.54370139862741 \tabularnewline
104 & 87.9 & 93.6530535958856 & -5.75305359588562 \tabularnewline
105 & 118.6 & 116.51216937369 & 2.08783062630977 \tabularnewline
106 & 116.3 & 119.028957360949 & -2.72895736094934 \tabularnewline
107 & 98.8 & 111.017402195623 & -12.2174021956234 \tabularnewline
108 & 102.9 & 115.092032889309 & -12.1920328893093 \tabularnewline
109 & 80.4 & 96.1949358462308 & -15.7949358462308 \tabularnewline
110 & 87 & 95.3380724949111 & -8.33807249491112 \tabularnewline
111 & 97.4 & 105.286387748707 & -7.88638774870697 \tabularnewline
112 & 87.2 & 93.9722172126679 & -6.77221721266794 \tabularnewline
113 & 110.6 & 92.3000483126856 & 18.2999516873144 \tabularnewline
114 & 101.1 & 112.766495841657 & -11.6664958416571 \tabularnewline
115 & 69.1 & 79.7297029309098 & -10.6297029309098 \tabularnewline
116 & 77.4 & 75.5294662733587 & 1.87053372664131 \tabularnewline
117 & 95 & 98.3009084342013 & -3.30090843420132 \tabularnewline
118 & 93.2 & 97.7204880177633 & -4.52048801776326 \tabularnewline
119 & 96.3 & 88.6393651609009 & 7.66063483909907 \tabularnewline
120 & 93.9 & 98.4129618447657 & -4.51296184476566 \tabularnewline
121 & 78.5 & 82.8682718761251 & -4.36827187612508 \tabularnewline
122 & 90 & 86.9191876344805 & 3.08081236551955 \tabularnewline
123 & 109.2 & 100.432260846438 & 8.76773915356198 \tabularnewline
124 & 94.3 & 94.8571398061828 & -0.557139806182846 \tabularnewline
125 & 93.1 & 100.339391112921 & -7.23939111292059 \tabularnewline
126 & 114.5 & 105.46705862672 & 9.03294137327978 \tabularnewline
127 & 78.5 & 79.1033031370663 & -0.603303137066291 \tabularnewline
128 & 88.3 & 80.9944471885285 & 7.30555281147154 \tabularnewline
129 & 114.8 & 106.541126091576 & 8.25887390842423 \tabularnewline
130 & 112.2 & 109.780580466865 & 2.41941953313496 \tabularnewline
131 & 106.9 & 104.688613974744 & 2.21138602525623 \tabularnewline
132 & 119.7 & 110.786649138706 & 8.91335086129436 \tabularnewline
133 & 97.1 & 97.3448088596709 & -0.244808859670869 \tabularnewline
134 & 106.3 & 105.815947711878 & 0.484052288122214 \tabularnewline
135 & 131.7 & 122.203592136346 & 9.49640786365359 \tabularnewline
136 & 106.7 & 113.04686644233 & -6.34686644233048 \tabularnewline
137 & 124 & 115.924310404023 & 8.07568959597738 \tabularnewline
138 & 117.2 & 132.133509237866 & -14.9335092378663 \tabularnewline
139 & 87.8 & 90.8310052931415 & -3.0310052931415 \tabularnewline
140 & 91.9 & 93.9259722681532 & -2.02597226815317 \tabularnewline
141 & 125.1 & 118.614027271633 & 6.48597272836729 \tabularnewline
142 & 115.4 & 119.993300594179 & -4.5933005941792 \tabularnewline
143 & 117.7 & 112.008071730302 & 5.69192826969804 \tabularnewline
144 & 124.3 & 121.09133445674 & 3.20866554326007 \tabularnewline
145 & 104.8 & 102.785851873363 & 2.01414812663693 \tabularnewline
146 & 109.6 & 112.730815744505 & -3.13081574450482 \tabularnewline
147 & 139.5 & 130.518939299595 & 8.98106070040527 \tabularnewline
148 & 105.3 & 117.216203687084 & -11.9162036870841 \tabularnewline
149 & 112.4 & 121.204834907321 & -8.80483490732071 \tabularnewline
150 & 128.9 & 126.627273262456 & 2.27272673754418 \tabularnewline
151 & 91.6 & 92.6999769098307 & -1.09997690983073 \tabularnewline
152 & 98.7 & 96.790424069846 & 1.90957593015403 \tabularnewline
153 & 117.8 & 125.8413829559 & -8.04138295589968 \tabularnewline
154 & 117.4 & 119.886689282542 & -2.48668928254222 \tabularnewline
155 & 110.5 & 114.560870106929 & -4.06087010692939 \tabularnewline
156 & 103.1 & 119.582211454626 & -16.4822114546256 \tabularnewline
157 & 95.8 & 95.5563497058696 & 0.243650294130347 \tabularnewline
158 & 98.2 & 103.1224319735 & -4.92243197350015 \tabularnewline
159 & 117.2 & 120.714658682367 & -3.5146586823665 \tabularnewline
160 & 108.5 & 101.157891481883 & 7.34210851811673 \tabularnewline
161 & 113.2 & 111.787276549308 & 1.41272345069157 \tabularnewline
162 & 120.2 & 122.5168855497 & -2.31688554969975 \tabularnewline
163 & 102.8 & 87.9737172116417 & 14.8262827883583 \tabularnewline
164 & 89.4 & 98.224442619882 & -8.82444261988195 \tabularnewline
165 & 119.8 & 120.749658178722 & -0.949658178722302 \tabularnewline
166 & 126.9 & 118.333000278983 & 8.56699972101674 \tabularnewline
167 & 114.4 & 116.388710769593 & -1.98871076959254 \tabularnewline
168 & 117.4 & 119.586750542357 & -2.18675054235682 \tabularnewline
169 & 109.4 & 102.74164849194 & 6.65835150805954 \tabularnewline
170 & 111.1 & 112.139484814007 & -1.03948481400714 \tabularnewline
171 & 121 & 133.593446305272 & -12.5934463052721 \tabularnewline
172 & 116.6 & 111.591010796439 & 5.00898920356103 \tabularnewline
173 & 119.5 & 120.76892382046 & -1.26892382046009 \tabularnewline
174 & 121.2 & 130.47629968166 & -9.27629968165989 \tabularnewline
175 & 101 & 95.240419598526 & 5.75958040147401 \tabularnewline
176 & 92.7 & 97.5568557064324 & -4.8568557064324 \tabularnewline
177 & 125.5 & 123.717193645464 & 1.78280635453621 \tabularnewline
178 & 123.4 & 124.071491312643 & -0.671491312642601 \tabularnewline
179 & 110.3 & 116.731707071933 & -6.43170707193337 \tabularnewline
180 & 118.8 & 118.271317600631 & 0.528682399368634 \tabularnewline
181 & 97.1 & 104.057005824711 & -6.95700582471089 \tabularnewline
182 & 107.6 & 106.881426074219 & 0.718573925780944 \tabularnewline
183 & 131 & 125.722525952976 & 5.27747404702431 \tabularnewline
184 & 117.9 & 113.289926308263 & 4.61007369173718 \tabularnewline
185 & 111 & 121.075426378003 & -10.0754263780029 \tabularnewline
186 & 131.4 & 125.850873204052 & 5.54912679594848 \tabularnewline
187 & 101.8 & 98.1642533299205 & 3.63574667007951 \tabularnewline
188 & 93.9 & 97.5709256099753 & -3.67092560997534 \tabularnewline
189 & 138.5 & 125.86087291586 & 12.6391270841397 \tabularnewline
190 & 131.1 & 129.458643448833 & 1.64135655116655 \tabularnewline
191 & 124.9 & 121.325586298226 & 3.57441370177392 \tabularnewline
192 & 126.6 & 128.169830578679 & -1.56983057867933 \tabularnewline
193 & 102.7 & 110.498327196717 & -7.79832719671661 \tabularnewline
194 & 121.6 & 115.075016079182 & 6.52498392081843 \tabularnewline
195 & 132.8 & 138.680081589094 & -5.88008158909361 \tabularnewline
196 & 123 & 121.326046017498 & 1.67395398250217 \tabularnewline
197 & 116 & 125.276170271182 & -9.2761702711821 \tabularnewline
198 & 135 & 134.055285471928 & 0.944714528071813 \tabularnewline
199 & 93.7 & 103.089212413512 & -9.38921241351248 \tabularnewline
200 & 98.4 & 96.4837313801561 & 1.91626861984389 \tabularnewline
201 & 129.8 & 130.387484691923 & -0.587484691922754 \tabularnewline
202 & 121.9 & 127.203828348994 & -5.30382834899393 \tabularnewline
203 & 124.8 & 117.290745916619 & 7.50925408338144 \tabularnewline
204 & 126.9 & 124.297168770376 & 2.60283122962406 \tabularnewline
205 & 102 & 107.062709304342 & -5.06270930434151 \tabularnewline
206 & 117.7 & 115.284829137652 & 2.41517086234754 \tabularnewline
207 & 144.8 & 134.52297857493 & 10.2770214250702 \tabularnewline
208 & 113.3 & 123.996810451999 & -10.6968104519994 \tabularnewline
209 & 129.3 & 121.35727376758 & 7.94272623241955 \tabularnewline
210 & 135.7 & 138.597540806895 & -2.89754080689482 \tabularnewline
211 & 94.3 & 103.415284567036 & -9.11528456703566 \tabularnewline
212 & 106 & 99.11019361761 & 6.88980638239003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310814&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]77.8[/C][C]72.5302311058089[/C][C]5.26976889419109[/C][/ROW]
[ROW][C]14[/C][C]75.7[/C][C]72.2990475170033[/C][C]3.40095248299673[/C][/ROW]
[ROW][C]15[/C][C]88.5[/C][C]86.1576653951497[/C][C]2.34233460485029[/C][/ROW]
[ROW][C]16[/C][C]72.9[/C][C]71.9990220491604[/C][C]0.900977950839646[/C][/ROW]
[ROW][C]17[/C][C]80.8[/C][C]80.7172835008757[/C][C]0.0827164991243166[/C][/ROW]
[ROW][C]18[/C][C]86.6[/C][C]87.8511169099747[/C][C]-1.25111690997468[/C][/ROW]
[ROW][C]19[/C][C]63.8[/C][C]60.1451782567076[/C][C]3.65482174329239[/C][/ROW]
[ROW][C]20[/C][C]69.2[/C][C]68.7031047537304[/C][C]0.496895246269574[/C][/ROW]
[ROW][C]21[/C][C]76.5[/C][C]82.5165169930587[/C][C]-6.01651699305869[/C][/ROW]
[ROW][C]22[/C][C]77.1[/C][C]79.5531804355462[/C][C]-2.45318043554622[/C][/ROW]
[ROW][C]23[/C][C]75.3[/C][C]82.4632685369584[/C][C]-7.16326853695837[/C][/ROW]
[ROW][C]24[/C][C]69.5[/C][C]83.7434589978017[/C][C]-14.2434589978017[/C][/ROW]
[ROW][C]25[/C][C]64.3[/C][C]77.7449845962815[/C][C]-13.4449845962815[/C][/ROW]
[ROW][C]26[/C][C]66.7[/C][C]70.6314472308958[/C][C]-3.93144723089577[/C][/ROW]
[ROW][C]27[/C][C]77.3[/C][C]80.8268011322036[/C][C]-3.52680113220364[/C][/ROW]
[ROW][C]28[/C][C]75.3[/C][C]65.6471657037226[/C][C]9.65283429627735[/C][/ROW]
[ROW][C]29[/C][C]73.4[/C][C]76.7786899702403[/C][C]-3.37868997024025[/C][/ROW]
[ROW][C]30[/C][C]78[/C][C]81.9328375654829[/C][C]-3.93283756548291[/C][/ROW]
[ROW][C]31[/C][C]61[/C][C]56.1980292438112[/C][C]4.80197075618877[/C][/ROW]
[ROW][C]32[/C][C]58.4[/C][C]64.0140932728195[/C][C]-5.61409327281954[/C][/ROW]
[ROW][C]33[/C][C]73.4[/C][C]73.0812568227578[/C][C]0.318743177242254[/C][/ROW]
[ROW][C]34[/C][C]82.3[/C][C]72.9749269538379[/C][C]9.32507304616215[/C][/ROW]
[ROW][C]35[/C][C]72.2[/C][C]78.863713921616[/C][C]-6.66371392161604[/C][/ROW]
[ROW][C]36[/C][C]76[/C][C]78.6117445696318[/C][C]-2.6117445696318[/C][/ROW]
[ROW][C]37[/C][C]64.3[/C][C]76.5569378187737[/C][C]-12.2569378187737[/C][/ROW]
[ROW][C]38[/C][C]70.8[/C][C]71.6556899498068[/C][C]-0.855689949806759[/C][/ROW]
[ROW][C]39[/C][C]74[/C][C]83.4072499304132[/C][C]-9.40724993041317[/C][/ROW]
[ROW][C]40[/C][C]71.4[/C][C]68.4717321458166[/C][C]2.92826785418343[/C][/ROW]
[ROW][C]41[/C][C]70.1[/C][C]74.4965267370744[/C][C]-4.39652673707444[/C][/ROW]
[ROW][C]42[/C][C]77.6[/C][C]78.9434392965454[/C][C]-1.34343929654544[/C][/ROW]
[ROW][C]43[/C][C]61.2[/C][C]56.1419623987892[/C][C]5.05803760121084[/C][/ROW]
[ROW][C]44[/C][C]52.1[/C][C]61.8279403345581[/C][C]-9.72794033455813[/C][/ROW]
[ROW][C]45[/C][C]74.4[/C][C]70.0849524256259[/C][C]4.31504757437413[/C][/ROW]
[ROW][C]46[/C][C]73.1[/C][C]72.9866942693183[/C][C]0.113305730681688[/C][/ROW]
[ROW][C]47[/C][C]70.9[/C][C]72.4320922622694[/C][C]-1.53209226226943[/C][/ROW]
[ROW][C]48[/C][C]80.7[/C][C]74.5776499233197[/C][C]6.12235007668031[/C][/ROW]
[ROW][C]49[/C][C]62.9[/C][C]73.4632485499874[/C][C]-10.5632485499874[/C][/ROW]
[ROW][C]50[/C][C]69.3[/C][C]71.3610357798767[/C][C]-2.06103577987673[/C][/ROW]
[ROW][C]51[/C][C]82.3[/C][C]80.7769366748167[/C][C]1.52306332518334[/C][/ROW]
[ROW][C]52[/C][C]76.2[/C][C]71.6763556916344[/C][C]4.52364430836562[/C][/ROW]
[ROW][C]53[/C][C]70.8[/C][C]76.8892793589604[/C][C]-6.0892793589604[/C][/ROW]
[ROW][C]54[/C][C]87.3[/C][C]81.541322611772[/C][C]5.75867738822799[/C][/ROW]
[ROW][C]55[/C][C]62[/C][C]60.9738262796478[/C][C]1.02617372035223[/C][/ROW]
[ROW][C]56[/C][C]66.9[/C][C]62.2021684334964[/C][C]4.69783156650356[/C][/ROW]
[ROW][C]57[/C][C]84.4[/C][C]80.0734385748017[/C][C]4.32656142519828[/C][/ROW]
[ROW][C]58[/C][C]82.6[/C][C]82.1729072796193[/C][C]0.427092720380656[/C][/ROW]
[ROW][C]59[/C][C]77.7[/C][C]81.2397838589347[/C][C]-3.5397838589347[/C][/ROW]
[ROW][C]60[/C][C]87[/C][C]84.6216024465729[/C][C]2.37839755342715[/C][/ROW]
[ROW][C]61[/C][C]76[/C][C]78.092125096857[/C][C]-2.09212509685702[/C][/ROW]
[ROW][C]62[/C][C]76.3[/C][C]81.0485573929021[/C][C]-4.74855739290207[/C][/ROW]
[ROW][C]63[/C][C]88.8[/C][C]91.6110325432112[/C][C]-2.8110325432112[/C][/ROW]
[ROW][C]64[/C][C]81.2[/C][C]80.5388379748728[/C][C]0.661162025127226[/C][/ROW]
[ROW][C]65[/C][C]74.5[/C][C]82.3542503951959[/C][C]-7.85425039519593[/C][/ROW]
[ROW][C]66[/C][C]98.1[/C][C]89.4211096458404[/C][C]8.67889035415955[/C][/ROW]
[ROW][C]67[/C][C]63.3[/C][C]66.7406733560785[/C][C]-3.44067335607853[/C][/ROW]
[ROW][C]68[/C][C]67.7[/C][C]67.1876385954538[/C][C]0.512361404546184[/C][/ROW]
[ROW][C]69[/C][C]85.8[/C][C]84.111893304611[/C][C]1.68810669538902[/C][/ROW]
[ROW][C]70[/C][C]78.6[/C][C]84.4630639621735[/C][C]-5.86306396217356[/C][/ROW]
[ROW][C]71[/C][C]87.2[/C][C]80.4906714621292[/C][C]6.7093285378708[/C][/ROW]
[ROW][C]72[/C][C]106.4[/C][C]88.8152590727664[/C][C]17.5847409272336[/C][/ROW]
[ROW][C]73[/C][C]75[/C][C]85.7797978336219[/C][C]-10.7797978336219[/C][/ROW]
[ROW][C]74[/C][C]80.4[/C][C]85.3341865496782[/C][C]-4.93418654967816[/C][/ROW]
[ROW][C]75[/C][C]94.8[/C][C]96.9930186137364[/C][C]-2.19301861373638[/C][/ROW]
[ROW][C]76[/C][C]77[/C][C]86.1415346062038[/C][C]-9.14153460620376[/C][/ROW]
[ROW][C]77[/C][C]91[/C][C]82.7505089486713[/C][C]8.24949105132872[/C][/ROW]
[ROW][C]78[/C][C]96.7[/C][C]99.8666549841286[/C][C]-3.16665498412864[/C][/ROW]
[ROW][C]79[/C][C]69.2[/C][C]69.2368499913809[/C][C]-0.036849991380862[/C][/ROW]
[ROW][C]80[/C][C]69.5[/C][C]71.7831141675974[/C][C]-2.28311416759745[/C][/ROW]
[ROW][C]81[/C][C]93.7[/C][C]88.8095912177844[/C][C]4.89040878221559[/C][/ROW]
[ROW][C]82[/C][C]98.5[/C][C]88.590230841824[/C][C]9.90976915817605[/C][/ROW]
[ROW][C]83[/C][C]93.3[/C][C]92.6071540154755[/C][C]0.692845984524467[/C][/ROW]
[ROW][C]84[/C][C]100.4[/C][C]101.562049023186[/C][C]-1.16204902318599[/C][/ROW]
[ROW][C]85[/C][C]87.4[/C][C]85.8156104512022[/C][C]1.58438954879782[/C][/ROW]
[ROW][C]86[/C][C]89[/C][C]91.1364988456566[/C][C]-2.1364988456566[/C][/ROW]
[ROW][C]87[/C][C]106.1[/C][C]105.579469127607[/C][C]0.520530872392769[/C][/ROW]
[ROW][C]88[/C][C]92.5[/C][C]92.9905113031285[/C][C]-0.490511303128471[/C][/ROW]
[ROW][C]89[/C][C]96.6[/C][C]96.4479458652904[/C][C]0.152054134709616[/C][/ROW]
[ROW][C]90[/C][C]113.3[/C][C]109.707344347581[/C][C]3.59265565241881[/C][/ROW]
[ROW][C]91[/C][C]87.6[/C][C]78.254944436178[/C][C]9.34505556382199[/C][/ROW]
[ROW][C]92[/C][C]89.2[/C][C]83.9755017998756[/C][C]5.2244982001244[/C][/ROW]
[ROW][C]93[/C][C]115.6[/C][C]109.176675710078[/C][C]6.42332428992158[/C][/ROW]
[ROW][C]94[/C][C]133.2[/C][C]110.196113454188[/C][C]23.0038865458115[/C][/ROW]
[ROW][C]95[/C][C]111.1[/C][C]116.615889604878[/C][C]-5.51588960487771[/C][/ROW]
[ROW][C]96[/C][C]113.1[/C][C]124.993687056761[/C][C]-11.8936870567607[/C][/ROW]
[ROW][C]97[/C][C]102[/C][C]103.131639308962[/C][C]-1.13163930896212[/C][/ROW]
[ROW][C]98[/C][C]109.3[/C][C]107.50106417981[/C][C]1.79893582019018[/C][/ROW]
[ROW][C]99[/C][C]111.1[/C][C]127.005026525171[/C][C]-15.9050265251712[/C][/ROW]
[ROW][C]100[/C][C]116.8[/C][C]106.571914901304[/C][C]10.228085098696[/C][/ROW]
[ROW][C]101[/C][C]107.5[/C][C]114.578692462169[/C][C]-7.07869246216929[/C][/ROW]
[ROW][C]102[/C][C]120.5[/C][C]128.223107809899[/C][C]-7.72310780989878[/C][/ROW]
[ROW][C]103[/C][C]95.5[/C][C]89.9562986013726[/C][C]5.54370139862741[/C][/ROW]
[ROW][C]104[/C][C]87.9[/C][C]93.6530535958856[/C][C]-5.75305359588562[/C][/ROW]
[ROW][C]105[/C][C]118.6[/C][C]116.51216937369[/C][C]2.08783062630977[/C][/ROW]
[ROW][C]106[/C][C]116.3[/C][C]119.028957360949[/C][C]-2.72895736094934[/C][/ROW]
[ROW][C]107[/C][C]98.8[/C][C]111.017402195623[/C][C]-12.2174021956234[/C][/ROW]
[ROW][C]108[/C][C]102.9[/C][C]115.092032889309[/C][C]-12.1920328893093[/C][/ROW]
[ROW][C]109[/C][C]80.4[/C][C]96.1949358462308[/C][C]-15.7949358462308[/C][/ROW]
[ROW][C]110[/C][C]87[/C][C]95.3380724949111[/C][C]-8.33807249491112[/C][/ROW]
[ROW][C]111[/C][C]97.4[/C][C]105.286387748707[/C][C]-7.88638774870697[/C][/ROW]
[ROW][C]112[/C][C]87.2[/C][C]93.9722172126679[/C][C]-6.77221721266794[/C][/ROW]
[ROW][C]113[/C][C]110.6[/C][C]92.3000483126856[/C][C]18.2999516873144[/C][/ROW]
[ROW][C]114[/C][C]101.1[/C][C]112.766495841657[/C][C]-11.6664958416571[/C][/ROW]
[ROW][C]115[/C][C]69.1[/C][C]79.7297029309098[/C][C]-10.6297029309098[/C][/ROW]
[ROW][C]116[/C][C]77.4[/C][C]75.5294662733587[/C][C]1.87053372664131[/C][/ROW]
[ROW][C]117[/C][C]95[/C][C]98.3009084342013[/C][C]-3.30090843420132[/C][/ROW]
[ROW][C]118[/C][C]93.2[/C][C]97.7204880177633[/C][C]-4.52048801776326[/C][/ROW]
[ROW][C]119[/C][C]96.3[/C][C]88.6393651609009[/C][C]7.66063483909907[/C][/ROW]
[ROW][C]120[/C][C]93.9[/C][C]98.4129618447657[/C][C]-4.51296184476566[/C][/ROW]
[ROW][C]121[/C][C]78.5[/C][C]82.8682718761251[/C][C]-4.36827187612508[/C][/ROW]
[ROW][C]122[/C][C]90[/C][C]86.9191876344805[/C][C]3.08081236551955[/C][/ROW]
[ROW][C]123[/C][C]109.2[/C][C]100.432260846438[/C][C]8.76773915356198[/C][/ROW]
[ROW][C]124[/C][C]94.3[/C][C]94.8571398061828[/C][C]-0.557139806182846[/C][/ROW]
[ROW][C]125[/C][C]93.1[/C][C]100.339391112921[/C][C]-7.23939111292059[/C][/ROW]
[ROW][C]126[/C][C]114.5[/C][C]105.46705862672[/C][C]9.03294137327978[/C][/ROW]
[ROW][C]127[/C][C]78.5[/C][C]79.1033031370663[/C][C]-0.603303137066291[/C][/ROW]
[ROW][C]128[/C][C]88.3[/C][C]80.9944471885285[/C][C]7.30555281147154[/C][/ROW]
[ROW][C]129[/C][C]114.8[/C][C]106.541126091576[/C][C]8.25887390842423[/C][/ROW]
[ROW][C]130[/C][C]112.2[/C][C]109.780580466865[/C][C]2.41941953313496[/C][/ROW]
[ROW][C]131[/C][C]106.9[/C][C]104.688613974744[/C][C]2.21138602525623[/C][/ROW]
[ROW][C]132[/C][C]119.7[/C][C]110.786649138706[/C][C]8.91335086129436[/C][/ROW]
[ROW][C]133[/C][C]97.1[/C][C]97.3448088596709[/C][C]-0.244808859670869[/C][/ROW]
[ROW][C]134[/C][C]106.3[/C][C]105.815947711878[/C][C]0.484052288122214[/C][/ROW]
[ROW][C]135[/C][C]131.7[/C][C]122.203592136346[/C][C]9.49640786365359[/C][/ROW]
[ROW][C]136[/C][C]106.7[/C][C]113.04686644233[/C][C]-6.34686644233048[/C][/ROW]
[ROW][C]137[/C][C]124[/C][C]115.924310404023[/C][C]8.07568959597738[/C][/ROW]
[ROW][C]138[/C][C]117.2[/C][C]132.133509237866[/C][C]-14.9335092378663[/C][/ROW]
[ROW][C]139[/C][C]87.8[/C][C]90.8310052931415[/C][C]-3.0310052931415[/C][/ROW]
[ROW][C]140[/C][C]91.9[/C][C]93.9259722681532[/C][C]-2.02597226815317[/C][/ROW]
[ROW][C]141[/C][C]125.1[/C][C]118.614027271633[/C][C]6.48597272836729[/C][/ROW]
[ROW][C]142[/C][C]115.4[/C][C]119.993300594179[/C][C]-4.5933005941792[/C][/ROW]
[ROW][C]143[/C][C]117.7[/C][C]112.008071730302[/C][C]5.69192826969804[/C][/ROW]
[ROW][C]144[/C][C]124.3[/C][C]121.09133445674[/C][C]3.20866554326007[/C][/ROW]
[ROW][C]145[/C][C]104.8[/C][C]102.785851873363[/C][C]2.01414812663693[/C][/ROW]
[ROW][C]146[/C][C]109.6[/C][C]112.730815744505[/C][C]-3.13081574450482[/C][/ROW]
[ROW][C]147[/C][C]139.5[/C][C]130.518939299595[/C][C]8.98106070040527[/C][/ROW]
[ROW][C]148[/C][C]105.3[/C][C]117.216203687084[/C][C]-11.9162036870841[/C][/ROW]
[ROW][C]149[/C][C]112.4[/C][C]121.204834907321[/C][C]-8.80483490732071[/C][/ROW]
[ROW][C]150[/C][C]128.9[/C][C]126.627273262456[/C][C]2.27272673754418[/C][/ROW]
[ROW][C]151[/C][C]91.6[/C][C]92.6999769098307[/C][C]-1.09997690983073[/C][/ROW]
[ROW][C]152[/C][C]98.7[/C][C]96.790424069846[/C][C]1.90957593015403[/C][/ROW]
[ROW][C]153[/C][C]117.8[/C][C]125.8413829559[/C][C]-8.04138295589968[/C][/ROW]
[ROW][C]154[/C][C]117.4[/C][C]119.886689282542[/C][C]-2.48668928254222[/C][/ROW]
[ROW][C]155[/C][C]110.5[/C][C]114.560870106929[/C][C]-4.06087010692939[/C][/ROW]
[ROW][C]156[/C][C]103.1[/C][C]119.582211454626[/C][C]-16.4822114546256[/C][/ROW]
[ROW][C]157[/C][C]95.8[/C][C]95.5563497058696[/C][C]0.243650294130347[/C][/ROW]
[ROW][C]158[/C][C]98.2[/C][C]103.1224319735[/C][C]-4.92243197350015[/C][/ROW]
[ROW][C]159[/C][C]117.2[/C][C]120.714658682367[/C][C]-3.5146586823665[/C][/ROW]
[ROW][C]160[/C][C]108.5[/C][C]101.157891481883[/C][C]7.34210851811673[/C][/ROW]
[ROW][C]161[/C][C]113.2[/C][C]111.787276549308[/C][C]1.41272345069157[/C][/ROW]
[ROW][C]162[/C][C]120.2[/C][C]122.5168855497[/C][C]-2.31688554969975[/C][/ROW]
[ROW][C]163[/C][C]102.8[/C][C]87.9737172116417[/C][C]14.8262827883583[/C][/ROW]
[ROW][C]164[/C][C]89.4[/C][C]98.224442619882[/C][C]-8.82444261988195[/C][/ROW]
[ROW][C]165[/C][C]119.8[/C][C]120.749658178722[/C][C]-0.949658178722302[/C][/ROW]
[ROW][C]166[/C][C]126.9[/C][C]118.333000278983[/C][C]8.56699972101674[/C][/ROW]
[ROW][C]167[/C][C]114.4[/C][C]116.388710769593[/C][C]-1.98871076959254[/C][/ROW]
[ROW][C]168[/C][C]117.4[/C][C]119.586750542357[/C][C]-2.18675054235682[/C][/ROW]
[ROW][C]169[/C][C]109.4[/C][C]102.74164849194[/C][C]6.65835150805954[/C][/ROW]
[ROW][C]170[/C][C]111.1[/C][C]112.139484814007[/C][C]-1.03948481400714[/C][/ROW]
[ROW][C]171[/C][C]121[/C][C]133.593446305272[/C][C]-12.5934463052721[/C][/ROW]
[ROW][C]172[/C][C]116.6[/C][C]111.591010796439[/C][C]5.00898920356103[/C][/ROW]
[ROW][C]173[/C][C]119.5[/C][C]120.76892382046[/C][C]-1.26892382046009[/C][/ROW]
[ROW][C]174[/C][C]121.2[/C][C]130.47629968166[/C][C]-9.27629968165989[/C][/ROW]
[ROW][C]175[/C][C]101[/C][C]95.240419598526[/C][C]5.75958040147401[/C][/ROW]
[ROW][C]176[/C][C]92.7[/C][C]97.5568557064324[/C][C]-4.8568557064324[/C][/ROW]
[ROW][C]177[/C][C]125.5[/C][C]123.717193645464[/C][C]1.78280635453621[/C][/ROW]
[ROW][C]178[/C][C]123.4[/C][C]124.071491312643[/C][C]-0.671491312642601[/C][/ROW]
[ROW][C]179[/C][C]110.3[/C][C]116.731707071933[/C][C]-6.43170707193337[/C][/ROW]
[ROW][C]180[/C][C]118.8[/C][C]118.271317600631[/C][C]0.528682399368634[/C][/ROW]
[ROW][C]181[/C][C]97.1[/C][C]104.057005824711[/C][C]-6.95700582471089[/C][/ROW]
[ROW][C]182[/C][C]107.6[/C][C]106.881426074219[/C][C]0.718573925780944[/C][/ROW]
[ROW][C]183[/C][C]131[/C][C]125.722525952976[/C][C]5.27747404702431[/C][/ROW]
[ROW][C]184[/C][C]117.9[/C][C]113.289926308263[/C][C]4.61007369173718[/C][/ROW]
[ROW][C]185[/C][C]111[/C][C]121.075426378003[/C][C]-10.0754263780029[/C][/ROW]
[ROW][C]186[/C][C]131.4[/C][C]125.850873204052[/C][C]5.54912679594848[/C][/ROW]
[ROW][C]187[/C][C]101.8[/C][C]98.1642533299205[/C][C]3.63574667007951[/C][/ROW]
[ROW][C]188[/C][C]93.9[/C][C]97.5709256099753[/C][C]-3.67092560997534[/C][/ROW]
[ROW][C]189[/C][C]138.5[/C][C]125.86087291586[/C][C]12.6391270841397[/C][/ROW]
[ROW][C]190[/C][C]131.1[/C][C]129.458643448833[/C][C]1.64135655116655[/C][/ROW]
[ROW][C]191[/C][C]124.9[/C][C]121.325586298226[/C][C]3.57441370177392[/C][/ROW]
[ROW][C]192[/C][C]126.6[/C][C]128.169830578679[/C][C]-1.56983057867933[/C][/ROW]
[ROW][C]193[/C][C]102.7[/C][C]110.498327196717[/C][C]-7.79832719671661[/C][/ROW]
[ROW][C]194[/C][C]121.6[/C][C]115.075016079182[/C][C]6.52498392081843[/C][/ROW]
[ROW][C]195[/C][C]132.8[/C][C]138.680081589094[/C][C]-5.88008158909361[/C][/ROW]
[ROW][C]196[/C][C]123[/C][C]121.326046017498[/C][C]1.67395398250217[/C][/ROW]
[ROW][C]197[/C][C]116[/C][C]125.276170271182[/C][C]-9.2761702711821[/C][/ROW]
[ROW][C]198[/C][C]135[/C][C]134.055285471928[/C][C]0.944714528071813[/C][/ROW]
[ROW][C]199[/C][C]93.7[/C][C]103.089212413512[/C][C]-9.38921241351248[/C][/ROW]
[ROW][C]200[/C][C]98.4[/C][C]96.4837313801561[/C][C]1.91626861984389[/C][/ROW]
[ROW][C]201[/C][C]129.8[/C][C]130.387484691923[/C][C]-0.587484691922754[/C][/ROW]
[ROW][C]202[/C][C]121.9[/C][C]127.203828348994[/C][C]-5.30382834899393[/C][/ROW]
[ROW][C]203[/C][C]124.8[/C][C]117.290745916619[/C][C]7.50925408338144[/C][/ROW]
[ROW][C]204[/C][C]126.9[/C][C]124.297168770376[/C][C]2.60283122962406[/C][/ROW]
[ROW][C]205[/C][C]102[/C][C]107.062709304342[/C][C]-5.06270930434151[/C][/ROW]
[ROW][C]206[/C][C]117.7[/C][C]115.284829137652[/C][C]2.41517086234754[/C][/ROW]
[ROW][C]207[/C][C]144.8[/C][C]134.52297857493[/C][C]10.2770214250702[/C][/ROW]
[ROW][C]208[/C][C]113.3[/C][C]123.996810451999[/C][C]-10.6968104519994[/C][/ROW]
[ROW][C]209[/C][C]129.3[/C][C]121.35727376758[/C][C]7.94272623241955[/C][/ROW]
[ROW][C]210[/C][C]135.7[/C][C]138.597540806895[/C][C]-2.89754080689482[/C][/ROW]
[ROW][C]211[/C][C]94.3[/C][C]103.415284567036[/C][C]-9.11528456703566[/C][/ROW]
[ROW][C]212[/C][C]106[/C][C]99.11019361761[/C][C]6.88980638239003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310814&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310814&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
1377.872.53023110580895.26976889419109
1475.772.29904751700333.40095248299673
1588.586.15766539514972.34233460485029
1672.971.99902204916040.900977950839646
1780.880.71728350087570.0827164991243166
1886.687.8511169099747-1.25111690997468
1963.860.14517825670763.65482174329239
2069.268.70310475373040.496895246269574
2176.582.5165169930587-6.01651699305869
2277.179.5531804355462-2.45318043554622
2375.382.4632685369584-7.16326853695837
2469.583.7434589978017-14.2434589978017
2564.377.7449845962815-13.4449845962815
2666.770.6314472308958-3.93144723089577
2777.380.8268011322036-3.52680113220364
2875.365.64716570372269.65283429627735
2973.476.7786899702403-3.37868997024025
307881.9328375654829-3.93283756548291
316156.19802924381124.80197075618877
3258.464.0140932728195-5.61409327281954
3373.473.08125682275780.318743177242254
3482.372.97492695383799.32507304616215
3572.278.863713921616-6.66371392161604
367678.6117445696318-2.6117445696318
3764.376.5569378187737-12.2569378187737
3870.871.6556899498068-0.855689949806759
397483.4072499304132-9.40724993041317
4071.468.47173214581662.92826785418343
4170.174.4965267370744-4.39652673707444
4277.678.9434392965454-1.34343929654544
4361.256.14196239878925.05803760121084
4452.161.8279403345581-9.72794033455813
4574.470.08495242562594.31504757437413
4673.172.98669426931830.113305730681688
4770.972.4320922622694-1.53209226226943
4880.774.57764992331976.12235007668031
4962.973.4632485499874-10.5632485499874
5069.371.3610357798767-2.06103577987673
5182.380.77693667481671.52306332518334
5276.271.67635569163444.52364430836562
5370.876.8892793589604-6.0892793589604
5487.381.5413226117725.75867738822799
556260.97382627964781.02617372035223
5666.962.20216843349644.69783156650356
5784.480.07343857480174.32656142519828
5882.682.17290727961930.427092720380656
5977.781.2397838589347-3.5397838589347
608784.62160244657292.37839755342715
617678.092125096857-2.09212509685702
6276.381.0485573929021-4.74855739290207
6388.891.6110325432112-2.8110325432112
6481.280.53883797487280.661162025127226
6574.582.3542503951959-7.85425039519593
6698.189.42110964584048.67889035415955
6763.366.7406733560785-3.44067335607853
6867.767.18763859545380.512361404546184
6985.884.1118933046111.68810669538902
7078.684.4630639621735-5.86306396217356
7187.280.49067146212926.7093285378708
72106.488.815259072766417.5847409272336
737585.7797978336219-10.7797978336219
7480.485.3341865496782-4.93418654967816
7594.896.9930186137364-2.19301861373638
767786.1415346062038-9.14153460620376
779182.75050894867138.24949105132872
7896.799.8666549841286-3.16665498412864
7969.269.2368499913809-0.036849991380862
8069.571.7831141675974-2.28311416759745
8193.788.80959121778444.89040878221559
8298.588.5902308418249.90976915817605
8393.392.60715401547550.692845984524467
84100.4101.562049023186-1.16204902318599
8587.485.81561045120221.58438954879782
868991.1364988456566-2.1364988456566
87106.1105.5794691276070.520530872392769
8892.592.9905113031285-0.490511303128471
8996.696.44794586529040.152054134709616
90113.3109.7073443475813.59265565241881
9187.678.2549444361789.34505556382199
9289.283.97550179987565.2244982001244
93115.6109.1766757100786.42332428992158
94133.2110.19611345418823.0038865458115
95111.1116.615889604878-5.51588960487771
96113.1124.993687056761-11.8936870567607
97102103.131639308962-1.13163930896212
98109.3107.501064179811.79893582019018
99111.1127.005026525171-15.9050265251712
100116.8106.57191490130410.228085098696
101107.5114.578692462169-7.07869246216929
102120.5128.223107809899-7.72310780989878
10395.589.95629860137265.54370139862741
10487.993.6530535958856-5.75305359588562
105118.6116.512169373692.08783062630977
106116.3119.028957360949-2.72895736094934
10798.8111.017402195623-12.2174021956234
108102.9115.092032889309-12.1920328893093
10980.496.1949358462308-15.7949358462308
1108795.3380724949111-8.33807249491112
11197.4105.286387748707-7.88638774870697
11287.293.9722172126679-6.77221721266794
113110.692.300048312685618.2999516873144
114101.1112.766495841657-11.6664958416571
11569.179.7297029309098-10.6297029309098
11677.475.52946627335871.87053372664131
1179598.3009084342013-3.30090843420132
11893.297.7204880177633-4.52048801776326
11996.388.63936516090097.66063483909907
12093.998.4129618447657-4.51296184476566
12178.582.8682718761251-4.36827187612508
1229086.91918763448053.08081236551955
123109.2100.4322608464388.76773915356198
12494.394.8571398061828-0.557139806182846
12593.1100.339391112921-7.23939111292059
126114.5105.467058626729.03294137327978
12778.579.1033031370663-0.603303137066291
12888.380.99444718852857.30555281147154
129114.8106.5411260915768.25887390842423
130112.2109.7805804668652.41941953313496
131106.9104.6886139747442.21138602525623
132119.7110.7866491387068.91335086129436
13397.197.3448088596709-0.244808859670869
134106.3105.8159477118780.484052288122214
135131.7122.2035921363469.49640786365359
136106.7113.04686644233-6.34686644233048
137124115.9243104040238.07568959597738
138117.2132.133509237866-14.9335092378663
13987.890.8310052931415-3.0310052931415
14091.993.9259722681532-2.02597226815317
141125.1118.6140272716336.48597272836729
142115.4119.993300594179-4.5933005941792
143117.7112.0080717303025.69192826969804
144124.3121.091334456743.20866554326007
145104.8102.7858518733632.01414812663693
146109.6112.730815744505-3.13081574450482
147139.5130.5189392995958.98106070040527
148105.3117.216203687084-11.9162036870841
149112.4121.204834907321-8.80483490732071
150128.9126.6272732624562.27272673754418
15191.692.6999769098307-1.09997690983073
15298.796.7904240698461.90957593015403
153117.8125.8413829559-8.04138295589968
154117.4119.886689282542-2.48668928254222
155110.5114.560870106929-4.06087010692939
156103.1119.582211454626-16.4822114546256
15795.895.55634970586960.243650294130347
15898.2103.1224319735-4.92243197350015
159117.2120.714658682367-3.5146586823665
160108.5101.1578914818837.34210851811673
161113.2111.7872765493081.41272345069157
162120.2122.5168855497-2.31688554969975
163102.887.973717211641714.8262827883583
16489.498.224442619882-8.82444261988195
165119.8120.749658178722-0.949658178722302
166126.9118.3330002789838.56699972101674
167114.4116.388710769593-1.98871076959254
168117.4119.586750542357-2.18675054235682
169109.4102.741648491946.65835150805954
170111.1112.139484814007-1.03948481400714
171121133.593446305272-12.5934463052721
172116.6111.5910107964395.00898920356103
173119.5120.76892382046-1.26892382046009
174121.2130.47629968166-9.27629968165989
17510195.2404195985265.75958040147401
17692.797.5568557064324-4.8568557064324
177125.5123.7171936454641.78280635453621
178123.4124.071491312643-0.671491312642601
179110.3116.731707071933-6.43170707193337
180118.8118.2713176006310.528682399368634
18197.1104.057005824711-6.95700582471089
182107.6106.8814260742190.718573925780944
183131125.7225259529765.27747404702431
184117.9113.2899263082634.61007369173718
185111121.075426378003-10.0754263780029
186131.4125.8508732040525.54912679594848
187101.898.16425332992053.63574667007951
18893.997.5709256099753-3.67092560997534
189138.5125.8608729158612.6391270841397
190131.1129.4586434488331.64135655116655
191124.9121.3255862982263.57441370177392
192126.6128.169830578679-1.56983057867933
193102.7110.498327196717-7.79832719671661
194121.6115.0750160791826.52498392081843
195132.8138.680081589094-5.88008158909361
196123121.3260460174981.67395398250217
197116125.276170271182-9.2761702711821
198135134.0552854719280.944714528071813
19993.7103.089212413512-9.38921241351248
20098.496.48373138015611.91626861984389
201129.8130.387484691923-0.587484691922754
202121.9127.203828348994-5.30382834899393
203124.8117.2907459166197.50925408338144
204126.9124.2971687703762.60283122962406
205102107.062709304342-5.06270930434151
206117.7115.2848291376522.41517086234754
207144.8134.5229785749310.2770214250702
208113.3123.996810451999-10.6968104519994
209129.3121.357273767587.94272623241955
210135.7138.597540806895-2.89754080689482
21194.3103.415284567036-9.11528456703566
21210699.110193617616.88980638239003






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
213135.573017243639128.272740782558142.87329370472
214131.469291470462122.877809289161140.060773651762
215125.60141337917116.001567588531135.201259169808
216129.12656730655118.268029890938139.985104722161
217108.90000754414798.1621745810014119.637840507292
218120.868781733673108.321355255516133.416208211831
219141.496597714496126.411646915015156.581548513976
220122.957852398774108.714982676862137.200722120685
221127.896037111172112.386241424043143.405832798301
222140.407467966107122.86257054155157.952365390663
223104.04740774614789.6733249435769118.421490548717
224106.18196671888183.8434805257379128.520452912024

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
213 & 135.573017243639 & 128.272740782558 & 142.87329370472 \tabularnewline
214 & 131.469291470462 & 122.877809289161 & 140.060773651762 \tabularnewline
215 & 125.60141337917 & 116.001567588531 & 135.201259169808 \tabularnewline
216 & 129.12656730655 & 118.268029890938 & 139.985104722161 \tabularnewline
217 & 108.900007544147 & 98.1621745810014 & 119.637840507292 \tabularnewline
218 & 120.868781733673 & 108.321355255516 & 133.416208211831 \tabularnewline
219 & 141.496597714496 & 126.411646915015 & 156.581548513976 \tabularnewline
220 & 122.957852398774 & 108.714982676862 & 137.200722120685 \tabularnewline
221 & 127.896037111172 & 112.386241424043 & 143.405832798301 \tabularnewline
222 & 140.407467966107 & 122.86257054155 & 157.952365390663 \tabularnewline
223 & 104.047407746147 & 89.6733249435769 & 118.421490548717 \tabularnewline
224 & 106.181966718881 & 83.8434805257379 & 128.520452912024 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310814&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]213[/C][C]135.573017243639[/C][C]128.272740782558[/C][C]142.87329370472[/C][/ROW]
[ROW][C]214[/C][C]131.469291470462[/C][C]122.877809289161[/C][C]140.060773651762[/C][/ROW]
[ROW][C]215[/C][C]125.60141337917[/C][C]116.001567588531[/C][C]135.201259169808[/C][/ROW]
[ROW][C]216[/C][C]129.12656730655[/C][C]118.268029890938[/C][C]139.985104722161[/C][/ROW]
[ROW][C]217[/C][C]108.900007544147[/C][C]98.1621745810014[/C][C]119.637840507292[/C][/ROW]
[ROW][C]218[/C][C]120.868781733673[/C][C]108.321355255516[/C][C]133.416208211831[/C][/ROW]
[ROW][C]219[/C][C]141.496597714496[/C][C]126.411646915015[/C][C]156.581548513976[/C][/ROW]
[ROW][C]220[/C][C]122.957852398774[/C][C]108.714982676862[/C][C]137.200722120685[/C][/ROW]
[ROW][C]221[/C][C]127.896037111172[/C][C]112.386241424043[/C][C]143.405832798301[/C][/ROW]
[ROW][C]222[/C][C]140.407467966107[/C][C]122.86257054155[/C][C]157.952365390663[/C][/ROW]
[ROW][C]223[/C][C]104.047407746147[/C][C]89.6733249435769[/C][C]118.421490548717[/C][/ROW]
[ROW][C]224[/C][C]106.181966718881[/C][C]83.8434805257379[/C][C]128.520452912024[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310814&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310814&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
213135.573017243639128.272740782558142.87329370472
214131.469291470462122.877809289161140.060773651762
215125.60141337917116.001567588531135.201259169808
216129.12656730655118.268029890938139.985104722161
217108.90000754414798.1621745810014119.637840507292
218120.868781733673108.321355255516133.416208211831
219141.496597714496126.411646915015156.581548513976
220122.957852398774108.714982676862137.200722120685
221127.896037111172112.386241424043143.405832798301
222140.407467966107122.86257054155157.952365390663
223104.04740774614789.6733249435769118.421490548717
224106.18196671888183.8434805257379128.520452912024


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = Triple ; par3 = multiplicative ; par4 = 12 ;
Parameters (R input):
par1 = 12 ; par2 = Triple ; par3 = multiplicative ; par4 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par4 <- as.numeric(par4)
if (par2 == 'Single') K <- 1
if (par2 == 'Double') K <- 2
if (par2 == 'Triple') K <- par1
nx <- length(x)
nxmK <- nx - K
x <- ts(x, frequency = par1)
if (par2 == 'Single') fit <- HoltWinters(x, gamma=F, beta=F)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=F)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par4, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$alpha)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,fit$beta)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'gamma',header=TRUE)
a<-table.element(a,fit$gamma)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Interpolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nxmK) {
a<-table.row.start(a)
a<-table.element(a,i+K,header=TRUE)
a<-table.element(a,x[i+K])
a<-table.element(a,fit$fitted[i,'xhat'])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Extrapolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Forecast',header=TRUE)
a<-table.element(a,'95% Lower Bound',header=TRUE)
a<-table.element(a,'95% Upper Bound',header=TRUE)
a<-table.row.end(a)
for (i in 1:np) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,p[i,'fit'])
a<-table.element(a,p[i,'lwr'])
a<-table.element(a,p[i,'upr'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')