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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 computationThu, 01 Dec 2011 13:44:52 -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/2011/Dec/01/t13227651132ovs8c6kzh5rrvw.htm/, Retrieved Fri, 26 Apr 2024 03:26:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=149945, Retrieved Fri, 26 Apr 2024 03:26:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [Brutoindex van de...] [2011-12-01 18:44:52] [22ff23eaefab118cb5de5c6132878e2b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101,5
100,7
110,6
96,8
100
104,8
86,8
92
100,2
106,6
102,1
93,7
97,6
96,9
105,6
102,8
101,7
104,2
92,7
91,9
106,5
112,3
102,8
96,5
101
98,9
105,1
103
99
104,3
94,6
90,4
108,9
111,4
100,8
102,5
98,2
98,7
113,3
104,6
99,3
111,8
97,3
97,7
115,6
111,9
107
107,1
100,6
99,2
108,4
103
99,8
115
90,8
95,9
114,4
108,2
112,6
109,1
105
105
118,5
103,7
112,5
116,6
96,6
101,9
116,5
119,3
115,4
108,5
111,5
108,8
121,8
109,6
112,2
119,6
104,1
105,3
115
124,1
116,8
107,5
115,6
116,2
116,3
119
111,9
118,6
106,9
103,2
118,6
118,7
102,8
100,6
94,9
94,5
102,9
95,3
92,5
102,7
91,5
89,5
104,2
105,2
99
95,5
90,5
96,1
113
101,9
101,4
113,6
96,6
97,8
114,9
112,5
108,4
107
103,5
107,5
122,3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149945&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149945&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149945&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.213256903449989
betaFALSE
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.213256903449989 \tabularnewline
beta & FALSE \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149945&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]0.213256903449989[/C][/ROW]
[ROW][C]beta[/C][C]FALSE[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149945&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149945&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
alpha0.213256903449989
betaFALSE
gammaFALSE






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
2100.7101.5-0.799999999999997
3110.6101.329394477249.27060552275998
496.8103.30641510413-6.50641510413018
5100101.918877166463-1.91887716646315
6104.8101.5096633638423.29033663615768
786.8102.211350366177-15.4113503661774
89298.9247735091035-6.92477350910352
9100.297.44801775345962.75198224654042
10106.698.03489696570618.56510303429386
11102.199.86146431652972.23853568347025
1293.7100.338847504649-6.63884750464891
1397.698.9230674433308-1.3230674433308
1496.998.6409141773106-1.74091417731057
15105.698.26965221068517.33034778931486
16102.899.83289948144592.9671005185541
17101.7100.4656541502581.23434584974241
18104.2100.728886923963.47111307604
1992.7101.469125750081-8.76912575008106
2091.999.5990491466552-7.69904914665521
21106.597.95717376613038.54282623386975
22112.399.778990435476612.5210095645234
23102.8102.4491821632750.350817836725412
2496.5102.52399648881-6.02399648880967
25101101.239337651213-0.239337651212509
2698.9101.188297244836-2.28829724483593
27105.1100.7003020602294.3996979397709
28103101.638568018981.36143198102008
2999101.92890278751-2.92890278751005
30104.3101.304294048542.99570595146038
3194.6101.943149023395-7.34314902339477
3290.4100.377171801094-9.97717180109377
33108.998.24947103760410.650528962396
34111.4100.52076986422910.879230135771
35100.8102.840840794903-2.0408407949033
36102.5102.4056174065480.0943825934521954
3798.2102.425745146167-4.225745146167
3898.7101.524575821527-2.82457582152661
39113.3100.92221552826812.3777844717319
40104.6103.5618635162811.03813648371896
4199.3103.783253288157-4.4832532881574
42111.8102.8271685745438.97283142545703
4397.3104.740686819515-7.44068681951468
4497.7103.153908988844-5.45390898884382
45115.6101.99082524618513.6091747538151
46111.9104.8930757126937.00692428730672
47107106.3873506889130.612649311087154
48107.1106.5180023838960.581997616103934
49100.6106.642117393322-6.04211739332166
5099.2105.353594147741-6.15359414774055
51108.4104.0412977147054.35870228529458
52103104.970821067128-1.97082106712773
5399.8104.550529869098-4.75052986909807
54115103.53744657946811.4625534205325
5590.8105.98191522756-15.1819152275604
5695.9102.744266997691-6.8442669976906
57114.4101.28467981137813.1153201886219
58108.2104.0816123825594.11838761744123
59112.6104.9598869730617.64011302693892
60109.1106.5891938191942.510806180806
61105107.124640570476-2.12464057047578
62105106.671546301472-1.67154630147189
63118.5106.31507751324712.1849224867533
64103.7108.91359635155-5.21359635154987
65112.5107.801760937784.69823906221981
66116.6108.8036928518577.79630714814303
6796.6110.466309172615-13.866309172615
68101.9107.509223016183-5.60922301618291
69116.5106.31301748499110.1869825150087
70119.3108.48546183164110.8145381683587
71115.4110.7917367536674.60826324633284
72108.5111.774480703863-3.27448070386251
73111.5111.076175088550.423824911449955
74108.8111.166558676771-2.36655867677084
75121.8110.6618737015311.13812629847
76109.6113.037156026177-3.43715602617659
77112.2112.30415877536-0.104158775359693
78119.6112.2819461974597.31805380254065
79104.1113.84257169067-9.74257169066959
80105.3111.764901020278-6.46490102027786
81115110.3862162475834.61378375241728
82124.1111.37013748381112.7298625161889
83116.8114.0848685453582.71513145464236
84107.5114.663889071834-7.16388907183433
85115.6113.1361402717162.46385972828426
86116.2113.6615753679052.53842463209524
87116.3114.2029119445872.09708805541342
88119114.6501304495464.349869550454
89111.9115.577770160287-3.67777016028721
90118.6114.7934602843043.8065397156964
91106.9115.605231156932-8.7052311569324
92103.2113.748780516589-10.5487805165886
93118.6111.4991802484477.10081975155262
94118.7113.013479080625.68652091938
95102.8114.226168923291-11.4261689232906
96100.6111.789459520413-11.1894595204131
9794.9109.403230031811-14.5032300318108
9894.5106.310316105204-11.810316105204
99102.9103.791684663843-0.891684663842625
10095.3103.601526753578-8.3015267535777
10192.5101.831168864202-9.33116886420248
102102.799.84123268665372.85876731334629
10391.5100.450884551582-8.95088455158199
10489.598.5420466289733-9.04204662897327
105104.296.6137677640287.58623223597199
106105.298.23158415952396.96841584047611
1079999.7176469436157-0.717646943615676
10895.599.5646037786499-4.06460377864985
10990.598.6977989630639-8.19779896306386
11096.196.9495617410953-0.849561741095343
11111396.768386834899816.2316131651002
112101.9100.2298903964871.6701096035129
113101.4100.5860527989540.81394720104565
114113.6100.75963265862112.8403673413789
11596.6103.497929637004-6.89792963700397
11697.8102.026898522401-4.22689852240059
117114.9101.12548323231613.7745167676839
118112.5104.0629940247128.43700597528768
119108.4105.8622437933912.53775620660878
120107106.4034378237240.596562176276393
121103.5106.530658826152-3.03065882615169
122107.5105.8843499094731.6156500905268
123122.3106.22889844483816.0711015551624

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
2 & 100.7 & 101.5 & -0.799999999999997 \tabularnewline
3 & 110.6 & 101.32939447724 & 9.27060552275998 \tabularnewline
4 & 96.8 & 103.30641510413 & -6.50641510413018 \tabularnewline
5 & 100 & 101.918877166463 & -1.91887716646315 \tabularnewline
6 & 104.8 & 101.509663363842 & 3.29033663615768 \tabularnewline
7 & 86.8 & 102.211350366177 & -15.4113503661774 \tabularnewline
8 & 92 & 98.9247735091035 & -6.92477350910352 \tabularnewline
9 & 100.2 & 97.4480177534596 & 2.75198224654042 \tabularnewline
10 & 106.6 & 98.0348969657061 & 8.56510303429386 \tabularnewline
11 & 102.1 & 99.8614643165297 & 2.23853568347025 \tabularnewline
12 & 93.7 & 100.338847504649 & -6.63884750464891 \tabularnewline
13 & 97.6 & 98.9230674433308 & -1.3230674433308 \tabularnewline
14 & 96.9 & 98.6409141773106 & -1.74091417731057 \tabularnewline
15 & 105.6 & 98.2696522106851 & 7.33034778931486 \tabularnewline
16 & 102.8 & 99.8328994814459 & 2.9671005185541 \tabularnewline
17 & 101.7 & 100.465654150258 & 1.23434584974241 \tabularnewline
18 & 104.2 & 100.72888692396 & 3.47111307604 \tabularnewline
19 & 92.7 & 101.469125750081 & -8.76912575008106 \tabularnewline
20 & 91.9 & 99.5990491466552 & -7.69904914665521 \tabularnewline
21 & 106.5 & 97.9571737661303 & 8.54282623386975 \tabularnewline
22 & 112.3 & 99.7789904354766 & 12.5210095645234 \tabularnewline
23 & 102.8 & 102.449182163275 & 0.350817836725412 \tabularnewline
24 & 96.5 & 102.52399648881 & -6.02399648880967 \tabularnewline
25 & 101 & 101.239337651213 & -0.239337651212509 \tabularnewline
26 & 98.9 & 101.188297244836 & -2.28829724483593 \tabularnewline
27 & 105.1 & 100.700302060229 & 4.3996979397709 \tabularnewline
28 & 103 & 101.63856801898 & 1.36143198102008 \tabularnewline
29 & 99 & 101.92890278751 & -2.92890278751005 \tabularnewline
30 & 104.3 & 101.30429404854 & 2.99570595146038 \tabularnewline
31 & 94.6 & 101.943149023395 & -7.34314902339477 \tabularnewline
32 & 90.4 & 100.377171801094 & -9.97717180109377 \tabularnewline
33 & 108.9 & 98.249471037604 & 10.650528962396 \tabularnewline
34 & 111.4 & 100.520769864229 & 10.879230135771 \tabularnewline
35 & 100.8 & 102.840840794903 & -2.0408407949033 \tabularnewline
36 & 102.5 & 102.405617406548 & 0.0943825934521954 \tabularnewline
37 & 98.2 & 102.425745146167 & -4.225745146167 \tabularnewline
38 & 98.7 & 101.524575821527 & -2.82457582152661 \tabularnewline
39 & 113.3 & 100.922215528268 & 12.3777844717319 \tabularnewline
40 & 104.6 & 103.561863516281 & 1.03813648371896 \tabularnewline
41 & 99.3 & 103.783253288157 & -4.4832532881574 \tabularnewline
42 & 111.8 & 102.827168574543 & 8.97283142545703 \tabularnewline
43 & 97.3 & 104.740686819515 & -7.44068681951468 \tabularnewline
44 & 97.7 & 103.153908988844 & -5.45390898884382 \tabularnewline
45 & 115.6 & 101.990825246185 & 13.6091747538151 \tabularnewline
46 & 111.9 & 104.893075712693 & 7.00692428730672 \tabularnewline
47 & 107 & 106.387350688913 & 0.612649311087154 \tabularnewline
48 & 107.1 & 106.518002383896 & 0.581997616103934 \tabularnewline
49 & 100.6 & 106.642117393322 & -6.04211739332166 \tabularnewline
50 & 99.2 & 105.353594147741 & -6.15359414774055 \tabularnewline
51 & 108.4 & 104.041297714705 & 4.35870228529458 \tabularnewline
52 & 103 & 104.970821067128 & -1.97082106712773 \tabularnewline
53 & 99.8 & 104.550529869098 & -4.75052986909807 \tabularnewline
54 & 115 & 103.537446579468 & 11.4625534205325 \tabularnewline
55 & 90.8 & 105.98191522756 & -15.1819152275604 \tabularnewline
56 & 95.9 & 102.744266997691 & -6.8442669976906 \tabularnewline
57 & 114.4 & 101.284679811378 & 13.1153201886219 \tabularnewline
58 & 108.2 & 104.081612382559 & 4.11838761744123 \tabularnewline
59 & 112.6 & 104.959886973061 & 7.64011302693892 \tabularnewline
60 & 109.1 & 106.589193819194 & 2.510806180806 \tabularnewline
61 & 105 & 107.124640570476 & -2.12464057047578 \tabularnewline
62 & 105 & 106.671546301472 & -1.67154630147189 \tabularnewline
63 & 118.5 & 106.315077513247 & 12.1849224867533 \tabularnewline
64 & 103.7 & 108.91359635155 & -5.21359635154987 \tabularnewline
65 & 112.5 & 107.80176093778 & 4.69823906221981 \tabularnewline
66 & 116.6 & 108.803692851857 & 7.79630714814303 \tabularnewline
67 & 96.6 & 110.466309172615 & -13.866309172615 \tabularnewline
68 & 101.9 & 107.509223016183 & -5.60922301618291 \tabularnewline
69 & 116.5 & 106.313017484991 & 10.1869825150087 \tabularnewline
70 & 119.3 & 108.485461831641 & 10.8145381683587 \tabularnewline
71 & 115.4 & 110.791736753667 & 4.60826324633284 \tabularnewline
72 & 108.5 & 111.774480703863 & -3.27448070386251 \tabularnewline
73 & 111.5 & 111.07617508855 & 0.423824911449955 \tabularnewline
74 & 108.8 & 111.166558676771 & -2.36655867677084 \tabularnewline
75 & 121.8 & 110.66187370153 & 11.13812629847 \tabularnewline
76 & 109.6 & 113.037156026177 & -3.43715602617659 \tabularnewline
77 & 112.2 & 112.30415877536 & -0.104158775359693 \tabularnewline
78 & 119.6 & 112.281946197459 & 7.31805380254065 \tabularnewline
79 & 104.1 & 113.84257169067 & -9.74257169066959 \tabularnewline
80 & 105.3 & 111.764901020278 & -6.46490102027786 \tabularnewline
81 & 115 & 110.386216247583 & 4.61378375241728 \tabularnewline
82 & 124.1 & 111.370137483811 & 12.7298625161889 \tabularnewline
83 & 116.8 & 114.084868545358 & 2.71513145464236 \tabularnewline
84 & 107.5 & 114.663889071834 & -7.16388907183433 \tabularnewline
85 & 115.6 & 113.136140271716 & 2.46385972828426 \tabularnewline
86 & 116.2 & 113.661575367905 & 2.53842463209524 \tabularnewline
87 & 116.3 & 114.202911944587 & 2.09708805541342 \tabularnewline
88 & 119 & 114.650130449546 & 4.349869550454 \tabularnewline
89 & 111.9 & 115.577770160287 & -3.67777016028721 \tabularnewline
90 & 118.6 & 114.793460284304 & 3.8065397156964 \tabularnewline
91 & 106.9 & 115.605231156932 & -8.7052311569324 \tabularnewline
92 & 103.2 & 113.748780516589 & -10.5487805165886 \tabularnewline
93 & 118.6 & 111.499180248447 & 7.10081975155262 \tabularnewline
94 & 118.7 & 113.01347908062 & 5.68652091938 \tabularnewline
95 & 102.8 & 114.226168923291 & -11.4261689232906 \tabularnewline
96 & 100.6 & 111.789459520413 & -11.1894595204131 \tabularnewline
97 & 94.9 & 109.403230031811 & -14.5032300318108 \tabularnewline
98 & 94.5 & 106.310316105204 & -11.810316105204 \tabularnewline
99 & 102.9 & 103.791684663843 & -0.891684663842625 \tabularnewline
100 & 95.3 & 103.601526753578 & -8.3015267535777 \tabularnewline
101 & 92.5 & 101.831168864202 & -9.33116886420248 \tabularnewline
102 & 102.7 & 99.8412326866537 & 2.85876731334629 \tabularnewline
103 & 91.5 & 100.450884551582 & -8.95088455158199 \tabularnewline
104 & 89.5 & 98.5420466289733 & -9.04204662897327 \tabularnewline
105 & 104.2 & 96.613767764028 & 7.58623223597199 \tabularnewline
106 & 105.2 & 98.2315841595239 & 6.96841584047611 \tabularnewline
107 & 99 & 99.7176469436157 & -0.717646943615676 \tabularnewline
108 & 95.5 & 99.5646037786499 & -4.06460377864985 \tabularnewline
109 & 90.5 & 98.6977989630639 & -8.19779896306386 \tabularnewline
110 & 96.1 & 96.9495617410953 & -0.849561741095343 \tabularnewline
111 & 113 & 96.7683868348998 & 16.2316131651002 \tabularnewline
112 & 101.9 & 100.229890396487 & 1.6701096035129 \tabularnewline
113 & 101.4 & 100.586052798954 & 0.81394720104565 \tabularnewline
114 & 113.6 & 100.759632658621 & 12.8403673413789 \tabularnewline
115 & 96.6 & 103.497929637004 & -6.89792963700397 \tabularnewline
116 & 97.8 & 102.026898522401 & -4.22689852240059 \tabularnewline
117 & 114.9 & 101.125483232316 & 13.7745167676839 \tabularnewline
118 & 112.5 & 104.062994024712 & 8.43700597528768 \tabularnewline
119 & 108.4 & 105.862243793391 & 2.53775620660878 \tabularnewline
120 & 107 & 106.403437823724 & 0.596562176276393 \tabularnewline
121 & 103.5 & 106.530658826152 & -3.03065882615169 \tabularnewline
122 & 107.5 & 105.884349909473 & 1.6156500905268 \tabularnewline
123 & 122.3 & 106.228898444838 & 16.0711015551624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149945&T=2

[TABLE]
[ROW][C]Interpolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Residuals[/C][/ROW]
[ROW][C]2[/C][C]100.7[/C][C]101.5[/C][C]-0.799999999999997[/C][/ROW]
[ROW][C]3[/C][C]110.6[/C][C]101.32939447724[/C][C]9.27060552275998[/C][/ROW]
[ROW][C]4[/C][C]96.8[/C][C]103.30641510413[/C][C]-6.50641510413018[/C][/ROW]
[ROW][C]5[/C][C]100[/C][C]101.918877166463[/C][C]-1.91887716646315[/C][/ROW]
[ROW][C]6[/C][C]104.8[/C][C]101.509663363842[/C][C]3.29033663615768[/C][/ROW]
[ROW][C]7[/C][C]86.8[/C][C]102.211350366177[/C][C]-15.4113503661774[/C][/ROW]
[ROW][C]8[/C][C]92[/C][C]98.9247735091035[/C][C]-6.92477350910352[/C][/ROW]
[ROW][C]9[/C][C]100.2[/C][C]97.4480177534596[/C][C]2.75198224654042[/C][/ROW]
[ROW][C]10[/C][C]106.6[/C][C]98.0348969657061[/C][C]8.56510303429386[/C][/ROW]
[ROW][C]11[/C][C]102.1[/C][C]99.8614643165297[/C][C]2.23853568347025[/C][/ROW]
[ROW][C]12[/C][C]93.7[/C][C]100.338847504649[/C][C]-6.63884750464891[/C][/ROW]
[ROW][C]13[/C][C]97.6[/C][C]98.9230674433308[/C][C]-1.3230674433308[/C][/ROW]
[ROW][C]14[/C][C]96.9[/C][C]98.6409141773106[/C][C]-1.74091417731057[/C][/ROW]
[ROW][C]15[/C][C]105.6[/C][C]98.2696522106851[/C][C]7.33034778931486[/C][/ROW]
[ROW][C]16[/C][C]102.8[/C][C]99.8328994814459[/C][C]2.9671005185541[/C][/ROW]
[ROW][C]17[/C][C]101.7[/C][C]100.465654150258[/C][C]1.23434584974241[/C][/ROW]
[ROW][C]18[/C][C]104.2[/C][C]100.72888692396[/C][C]3.47111307604[/C][/ROW]
[ROW][C]19[/C][C]92.7[/C][C]101.469125750081[/C][C]-8.76912575008106[/C][/ROW]
[ROW][C]20[/C][C]91.9[/C][C]99.5990491466552[/C][C]-7.69904914665521[/C][/ROW]
[ROW][C]21[/C][C]106.5[/C][C]97.9571737661303[/C][C]8.54282623386975[/C][/ROW]
[ROW][C]22[/C][C]112.3[/C][C]99.7789904354766[/C][C]12.5210095645234[/C][/ROW]
[ROW][C]23[/C][C]102.8[/C][C]102.449182163275[/C][C]0.350817836725412[/C][/ROW]
[ROW][C]24[/C][C]96.5[/C][C]102.52399648881[/C][C]-6.02399648880967[/C][/ROW]
[ROW][C]25[/C][C]101[/C][C]101.239337651213[/C][C]-0.239337651212509[/C][/ROW]
[ROW][C]26[/C][C]98.9[/C][C]101.188297244836[/C][C]-2.28829724483593[/C][/ROW]
[ROW][C]27[/C][C]105.1[/C][C]100.700302060229[/C][C]4.3996979397709[/C][/ROW]
[ROW][C]28[/C][C]103[/C][C]101.63856801898[/C][C]1.36143198102008[/C][/ROW]
[ROW][C]29[/C][C]99[/C][C]101.92890278751[/C][C]-2.92890278751005[/C][/ROW]
[ROW][C]30[/C][C]104.3[/C][C]101.30429404854[/C][C]2.99570595146038[/C][/ROW]
[ROW][C]31[/C][C]94.6[/C][C]101.943149023395[/C][C]-7.34314902339477[/C][/ROW]
[ROW][C]32[/C][C]90.4[/C][C]100.377171801094[/C][C]-9.97717180109377[/C][/ROW]
[ROW][C]33[/C][C]108.9[/C][C]98.249471037604[/C][C]10.650528962396[/C][/ROW]
[ROW][C]34[/C][C]111.4[/C][C]100.520769864229[/C][C]10.879230135771[/C][/ROW]
[ROW][C]35[/C][C]100.8[/C][C]102.840840794903[/C][C]-2.0408407949033[/C][/ROW]
[ROW][C]36[/C][C]102.5[/C][C]102.405617406548[/C][C]0.0943825934521954[/C][/ROW]
[ROW][C]37[/C][C]98.2[/C][C]102.425745146167[/C][C]-4.225745146167[/C][/ROW]
[ROW][C]38[/C][C]98.7[/C][C]101.524575821527[/C][C]-2.82457582152661[/C][/ROW]
[ROW][C]39[/C][C]113.3[/C][C]100.922215528268[/C][C]12.3777844717319[/C][/ROW]
[ROW][C]40[/C][C]104.6[/C][C]103.561863516281[/C][C]1.03813648371896[/C][/ROW]
[ROW][C]41[/C][C]99.3[/C][C]103.783253288157[/C][C]-4.4832532881574[/C][/ROW]
[ROW][C]42[/C][C]111.8[/C][C]102.827168574543[/C][C]8.97283142545703[/C][/ROW]
[ROW][C]43[/C][C]97.3[/C][C]104.740686819515[/C][C]-7.44068681951468[/C][/ROW]
[ROW][C]44[/C][C]97.7[/C][C]103.153908988844[/C][C]-5.45390898884382[/C][/ROW]
[ROW][C]45[/C][C]115.6[/C][C]101.990825246185[/C][C]13.6091747538151[/C][/ROW]
[ROW][C]46[/C][C]111.9[/C][C]104.893075712693[/C][C]7.00692428730672[/C][/ROW]
[ROW][C]47[/C][C]107[/C][C]106.387350688913[/C][C]0.612649311087154[/C][/ROW]
[ROW][C]48[/C][C]107.1[/C][C]106.518002383896[/C][C]0.581997616103934[/C][/ROW]
[ROW][C]49[/C][C]100.6[/C][C]106.642117393322[/C][C]-6.04211739332166[/C][/ROW]
[ROW][C]50[/C][C]99.2[/C][C]105.353594147741[/C][C]-6.15359414774055[/C][/ROW]
[ROW][C]51[/C][C]108.4[/C][C]104.041297714705[/C][C]4.35870228529458[/C][/ROW]
[ROW][C]52[/C][C]103[/C][C]104.970821067128[/C][C]-1.97082106712773[/C][/ROW]
[ROW][C]53[/C][C]99.8[/C][C]104.550529869098[/C][C]-4.75052986909807[/C][/ROW]
[ROW][C]54[/C][C]115[/C][C]103.537446579468[/C][C]11.4625534205325[/C][/ROW]
[ROW][C]55[/C][C]90.8[/C][C]105.98191522756[/C][C]-15.1819152275604[/C][/ROW]
[ROW][C]56[/C][C]95.9[/C][C]102.744266997691[/C][C]-6.8442669976906[/C][/ROW]
[ROW][C]57[/C][C]114.4[/C][C]101.284679811378[/C][C]13.1153201886219[/C][/ROW]
[ROW][C]58[/C][C]108.2[/C][C]104.081612382559[/C][C]4.11838761744123[/C][/ROW]
[ROW][C]59[/C][C]112.6[/C][C]104.959886973061[/C][C]7.64011302693892[/C][/ROW]
[ROW][C]60[/C][C]109.1[/C][C]106.589193819194[/C][C]2.510806180806[/C][/ROW]
[ROW][C]61[/C][C]105[/C][C]107.124640570476[/C][C]-2.12464057047578[/C][/ROW]
[ROW][C]62[/C][C]105[/C][C]106.671546301472[/C][C]-1.67154630147189[/C][/ROW]
[ROW][C]63[/C][C]118.5[/C][C]106.315077513247[/C][C]12.1849224867533[/C][/ROW]
[ROW][C]64[/C][C]103.7[/C][C]108.91359635155[/C][C]-5.21359635154987[/C][/ROW]
[ROW][C]65[/C][C]112.5[/C][C]107.80176093778[/C][C]4.69823906221981[/C][/ROW]
[ROW][C]66[/C][C]116.6[/C][C]108.803692851857[/C][C]7.79630714814303[/C][/ROW]
[ROW][C]67[/C][C]96.6[/C][C]110.466309172615[/C][C]-13.866309172615[/C][/ROW]
[ROW][C]68[/C][C]101.9[/C][C]107.509223016183[/C][C]-5.60922301618291[/C][/ROW]
[ROW][C]69[/C][C]116.5[/C][C]106.313017484991[/C][C]10.1869825150087[/C][/ROW]
[ROW][C]70[/C][C]119.3[/C][C]108.485461831641[/C][C]10.8145381683587[/C][/ROW]
[ROW][C]71[/C][C]115.4[/C][C]110.791736753667[/C][C]4.60826324633284[/C][/ROW]
[ROW][C]72[/C][C]108.5[/C][C]111.774480703863[/C][C]-3.27448070386251[/C][/ROW]
[ROW][C]73[/C][C]111.5[/C][C]111.07617508855[/C][C]0.423824911449955[/C][/ROW]
[ROW][C]74[/C][C]108.8[/C][C]111.166558676771[/C][C]-2.36655867677084[/C][/ROW]
[ROW][C]75[/C][C]121.8[/C][C]110.66187370153[/C][C]11.13812629847[/C][/ROW]
[ROW][C]76[/C][C]109.6[/C][C]113.037156026177[/C][C]-3.43715602617659[/C][/ROW]
[ROW][C]77[/C][C]112.2[/C][C]112.30415877536[/C][C]-0.104158775359693[/C][/ROW]
[ROW][C]78[/C][C]119.6[/C][C]112.281946197459[/C][C]7.31805380254065[/C][/ROW]
[ROW][C]79[/C][C]104.1[/C][C]113.84257169067[/C][C]-9.74257169066959[/C][/ROW]
[ROW][C]80[/C][C]105.3[/C][C]111.764901020278[/C][C]-6.46490102027786[/C][/ROW]
[ROW][C]81[/C][C]115[/C][C]110.386216247583[/C][C]4.61378375241728[/C][/ROW]
[ROW][C]82[/C][C]124.1[/C][C]111.370137483811[/C][C]12.7298625161889[/C][/ROW]
[ROW][C]83[/C][C]116.8[/C][C]114.084868545358[/C][C]2.71513145464236[/C][/ROW]
[ROW][C]84[/C][C]107.5[/C][C]114.663889071834[/C][C]-7.16388907183433[/C][/ROW]
[ROW][C]85[/C][C]115.6[/C][C]113.136140271716[/C][C]2.46385972828426[/C][/ROW]
[ROW][C]86[/C][C]116.2[/C][C]113.661575367905[/C][C]2.53842463209524[/C][/ROW]
[ROW][C]87[/C][C]116.3[/C][C]114.202911944587[/C][C]2.09708805541342[/C][/ROW]
[ROW][C]88[/C][C]119[/C][C]114.650130449546[/C][C]4.349869550454[/C][/ROW]
[ROW][C]89[/C][C]111.9[/C][C]115.577770160287[/C][C]-3.67777016028721[/C][/ROW]
[ROW][C]90[/C][C]118.6[/C][C]114.793460284304[/C][C]3.8065397156964[/C][/ROW]
[ROW][C]91[/C][C]106.9[/C][C]115.605231156932[/C][C]-8.7052311569324[/C][/ROW]
[ROW][C]92[/C][C]103.2[/C][C]113.748780516589[/C][C]-10.5487805165886[/C][/ROW]
[ROW][C]93[/C][C]118.6[/C][C]111.499180248447[/C][C]7.10081975155262[/C][/ROW]
[ROW][C]94[/C][C]118.7[/C][C]113.01347908062[/C][C]5.68652091938[/C][/ROW]
[ROW][C]95[/C][C]102.8[/C][C]114.226168923291[/C][C]-11.4261689232906[/C][/ROW]
[ROW][C]96[/C][C]100.6[/C][C]111.789459520413[/C][C]-11.1894595204131[/C][/ROW]
[ROW][C]97[/C][C]94.9[/C][C]109.403230031811[/C][C]-14.5032300318108[/C][/ROW]
[ROW][C]98[/C][C]94.5[/C][C]106.310316105204[/C][C]-11.810316105204[/C][/ROW]
[ROW][C]99[/C][C]102.9[/C][C]103.791684663843[/C][C]-0.891684663842625[/C][/ROW]
[ROW][C]100[/C][C]95.3[/C][C]103.601526753578[/C][C]-8.3015267535777[/C][/ROW]
[ROW][C]101[/C][C]92.5[/C][C]101.831168864202[/C][C]-9.33116886420248[/C][/ROW]
[ROW][C]102[/C][C]102.7[/C][C]99.8412326866537[/C][C]2.85876731334629[/C][/ROW]
[ROW][C]103[/C][C]91.5[/C][C]100.450884551582[/C][C]-8.95088455158199[/C][/ROW]
[ROW][C]104[/C][C]89.5[/C][C]98.5420466289733[/C][C]-9.04204662897327[/C][/ROW]
[ROW][C]105[/C][C]104.2[/C][C]96.613767764028[/C][C]7.58623223597199[/C][/ROW]
[ROW][C]106[/C][C]105.2[/C][C]98.2315841595239[/C][C]6.96841584047611[/C][/ROW]
[ROW][C]107[/C][C]99[/C][C]99.7176469436157[/C][C]-0.717646943615676[/C][/ROW]
[ROW][C]108[/C][C]95.5[/C][C]99.5646037786499[/C][C]-4.06460377864985[/C][/ROW]
[ROW][C]109[/C][C]90.5[/C][C]98.6977989630639[/C][C]-8.19779896306386[/C][/ROW]
[ROW][C]110[/C][C]96.1[/C][C]96.9495617410953[/C][C]-0.849561741095343[/C][/ROW]
[ROW][C]111[/C][C]113[/C][C]96.7683868348998[/C][C]16.2316131651002[/C][/ROW]
[ROW][C]112[/C][C]101.9[/C][C]100.229890396487[/C][C]1.6701096035129[/C][/ROW]
[ROW][C]113[/C][C]101.4[/C][C]100.586052798954[/C][C]0.81394720104565[/C][/ROW]
[ROW][C]114[/C][C]113.6[/C][C]100.759632658621[/C][C]12.8403673413789[/C][/ROW]
[ROW][C]115[/C][C]96.6[/C][C]103.497929637004[/C][C]-6.89792963700397[/C][/ROW]
[ROW][C]116[/C][C]97.8[/C][C]102.026898522401[/C][C]-4.22689852240059[/C][/ROW]
[ROW][C]117[/C][C]114.9[/C][C]101.125483232316[/C][C]13.7745167676839[/C][/ROW]
[ROW][C]118[/C][C]112.5[/C][C]104.062994024712[/C][C]8.43700597528768[/C][/ROW]
[ROW][C]119[/C][C]108.4[/C][C]105.862243793391[/C][C]2.53775620660878[/C][/ROW]
[ROW][C]120[/C][C]107[/C][C]106.403437823724[/C][C]0.596562176276393[/C][/ROW]
[ROW][C]121[/C][C]103.5[/C][C]106.530658826152[/C][C]-3.03065882615169[/C][/ROW]
[ROW][C]122[/C][C]107.5[/C][C]105.884349909473[/C][C]1.6156500905268[/C][/ROW]
[ROW][C]123[/C][C]122.3[/C][C]106.228898444838[/C][C]16.0711015551624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149945&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149945&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
2100.7101.5-0.799999999999997
3110.6101.329394477249.27060552275998
496.8103.30641510413-6.50641510413018
5100101.918877166463-1.91887716646315
6104.8101.5096633638423.29033663615768
786.8102.211350366177-15.4113503661774
89298.9247735091035-6.92477350910352
9100.297.44801775345962.75198224654042
10106.698.03489696570618.56510303429386
11102.199.86146431652972.23853568347025
1293.7100.338847504649-6.63884750464891
1397.698.9230674433308-1.3230674433308
1496.998.6409141773106-1.74091417731057
15105.698.26965221068517.33034778931486
16102.899.83289948144592.9671005185541
17101.7100.4656541502581.23434584974241
18104.2100.728886923963.47111307604
1992.7101.469125750081-8.76912575008106
2091.999.5990491466552-7.69904914665521
21106.597.95717376613038.54282623386975
22112.399.778990435476612.5210095645234
23102.8102.4491821632750.350817836725412
2496.5102.52399648881-6.02399648880967
25101101.239337651213-0.239337651212509
2698.9101.188297244836-2.28829724483593
27105.1100.7003020602294.3996979397709
28103101.638568018981.36143198102008
2999101.92890278751-2.92890278751005
30104.3101.304294048542.99570595146038
3194.6101.943149023395-7.34314902339477
3290.4100.377171801094-9.97717180109377
33108.998.24947103760410.650528962396
34111.4100.52076986422910.879230135771
35100.8102.840840794903-2.0408407949033
36102.5102.4056174065480.0943825934521954
3798.2102.425745146167-4.225745146167
3898.7101.524575821527-2.82457582152661
39113.3100.92221552826812.3777844717319
40104.6103.5618635162811.03813648371896
4199.3103.783253288157-4.4832532881574
42111.8102.8271685745438.97283142545703
4397.3104.740686819515-7.44068681951468
4497.7103.153908988844-5.45390898884382
45115.6101.99082524618513.6091747538151
46111.9104.8930757126937.00692428730672
47107106.3873506889130.612649311087154
48107.1106.5180023838960.581997616103934
49100.6106.642117393322-6.04211739332166
5099.2105.353594147741-6.15359414774055
51108.4104.0412977147054.35870228529458
52103104.970821067128-1.97082106712773
5399.8104.550529869098-4.75052986909807
54115103.53744657946811.4625534205325
5590.8105.98191522756-15.1819152275604
5695.9102.744266997691-6.8442669976906
57114.4101.28467981137813.1153201886219
58108.2104.0816123825594.11838761744123
59112.6104.9598869730617.64011302693892
60109.1106.5891938191942.510806180806
61105107.124640570476-2.12464057047578
62105106.671546301472-1.67154630147189
63118.5106.31507751324712.1849224867533
64103.7108.91359635155-5.21359635154987
65112.5107.801760937784.69823906221981
66116.6108.8036928518577.79630714814303
6796.6110.466309172615-13.866309172615
68101.9107.509223016183-5.60922301618291
69116.5106.31301748499110.1869825150087
70119.3108.48546183164110.8145381683587
71115.4110.7917367536674.60826324633284
72108.5111.774480703863-3.27448070386251
73111.5111.076175088550.423824911449955
74108.8111.166558676771-2.36655867677084
75121.8110.6618737015311.13812629847
76109.6113.037156026177-3.43715602617659
77112.2112.30415877536-0.104158775359693
78119.6112.2819461974597.31805380254065
79104.1113.84257169067-9.74257169066959
80105.3111.764901020278-6.46490102027786
81115110.3862162475834.61378375241728
82124.1111.37013748381112.7298625161889
83116.8114.0848685453582.71513145464236
84107.5114.663889071834-7.16388907183433
85115.6113.1361402717162.46385972828426
86116.2113.6615753679052.53842463209524
87116.3114.2029119445872.09708805541342
88119114.6501304495464.349869550454
89111.9115.577770160287-3.67777016028721
90118.6114.7934602843043.8065397156964
91106.9115.605231156932-8.7052311569324
92103.2113.748780516589-10.5487805165886
93118.6111.4991802484477.10081975155262
94118.7113.013479080625.68652091938
95102.8114.226168923291-11.4261689232906
96100.6111.789459520413-11.1894595204131
9794.9109.403230031811-14.5032300318108
9894.5106.310316105204-11.810316105204
99102.9103.791684663843-0.891684663842625
10095.3103.601526753578-8.3015267535777
10192.5101.831168864202-9.33116886420248
102102.799.84123268665372.85876731334629
10391.5100.450884551582-8.95088455158199
10489.598.5420466289733-9.04204662897327
105104.296.6137677640287.58623223597199
106105.298.23158415952396.96841584047611
1079999.7176469436157-0.717646943615676
10895.599.5646037786499-4.06460377864985
10990.598.6977989630639-8.19779896306386
11096.196.9495617410953-0.849561741095343
11111396.768386834899816.2316131651002
112101.9100.2298903964871.6701096035129
113101.4100.5860527989540.81394720104565
114113.6100.75963265862112.8403673413789
11596.6103.497929637004-6.89792963700397
11697.8102.026898522401-4.22689852240059
117114.9101.12548323231613.7745167676839
118112.5104.0629940247128.43700597528768
119108.4105.8622437933912.53775620660878
120107106.4034378237240.596562176276393
121103.5106.530658826152-3.03065882615169
122107.5105.8843499094731.6156500905268
123122.3106.22889844483816.0711015551624






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
124109.65617179752295.1205937236524124.191749871391
125109.65617179752294.7937404126484124.518603182395
126109.65617179752294.4739221816518124.838421413392
127109.65617179752294.1607034236035125.15164017144
128109.65617179752293.8536917119271125.458651883117
129109.65617179752293.5525320355259125.759811559518
130109.65617179752293.256901987172126.055441607872
131109.65617179752292.9665077193815126.345835875662
132109.65617179752292.6810805232551126.631263071789
133109.65617179752292.4003739168615126.911969678182
134109.65617179752292.1241611533681127.188182441676
135109.65617179752291.8522330772489127.460110517795

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
124 & 109.656171797522 & 95.1205937236524 & 124.191749871391 \tabularnewline
125 & 109.656171797522 & 94.7937404126484 & 124.518603182395 \tabularnewline
126 & 109.656171797522 & 94.4739221816518 & 124.838421413392 \tabularnewline
127 & 109.656171797522 & 94.1607034236035 & 125.15164017144 \tabularnewline
128 & 109.656171797522 & 93.8536917119271 & 125.458651883117 \tabularnewline
129 & 109.656171797522 & 93.5525320355259 & 125.759811559518 \tabularnewline
130 & 109.656171797522 & 93.256901987172 & 126.055441607872 \tabularnewline
131 & 109.656171797522 & 92.9665077193815 & 126.345835875662 \tabularnewline
132 & 109.656171797522 & 92.6810805232551 & 126.631263071789 \tabularnewline
133 & 109.656171797522 & 92.4003739168615 & 126.911969678182 \tabularnewline
134 & 109.656171797522 & 92.1241611533681 & 127.188182441676 \tabularnewline
135 & 109.656171797522 & 91.8522330772489 & 127.460110517795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149945&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]124[/C][C]109.656171797522[/C][C]95.1205937236524[/C][C]124.191749871391[/C][/ROW]
[ROW][C]125[/C][C]109.656171797522[/C][C]94.7937404126484[/C][C]124.518603182395[/C][/ROW]
[ROW][C]126[/C][C]109.656171797522[/C][C]94.4739221816518[/C][C]124.838421413392[/C][/ROW]
[ROW][C]127[/C][C]109.656171797522[/C][C]94.1607034236035[/C][C]125.15164017144[/C][/ROW]
[ROW][C]128[/C][C]109.656171797522[/C][C]93.8536917119271[/C][C]125.458651883117[/C][/ROW]
[ROW][C]129[/C][C]109.656171797522[/C][C]93.5525320355259[/C][C]125.759811559518[/C][/ROW]
[ROW][C]130[/C][C]109.656171797522[/C][C]93.256901987172[/C][C]126.055441607872[/C][/ROW]
[ROW][C]131[/C][C]109.656171797522[/C][C]92.9665077193815[/C][C]126.345835875662[/C][/ROW]
[ROW][C]132[/C][C]109.656171797522[/C][C]92.6810805232551[/C][C]126.631263071789[/C][/ROW]
[ROW][C]133[/C][C]109.656171797522[/C][C]92.4003739168615[/C][C]126.911969678182[/C][/ROW]
[ROW][C]134[/C][C]109.656171797522[/C][C]92.1241611533681[/C][C]127.188182441676[/C][/ROW]
[ROW][C]135[/C][C]109.656171797522[/C][C]91.8522330772489[/C][C]127.460110517795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149945&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149945&T=3

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


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

Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
124109.65617179752295.1205937236524124.191749871391
125109.65617179752294.7937404126484124.518603182395
126109.65617179752294.4739221816518124.838421413392
127109.65617179752294.1607034236035125.15164017144
128109.65617179752293.8536917119271125.458651883117
129109.65617179752293.5525320355259125.759811559518
130109.65617179752293.256901987172126.055441607872
131109.65617179752292.9665077193815126.345835875662
132109.65617179752292.6810805232551126.631263071789
133109.65617179752292.4003739168615126.911969678182
134109.65617179752292.1241611533681127.188182441676
135109.65617179752291.8522330772489127.460110517795


Figures (Output of Computation)

Input Parameters & R Code

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