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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationMon, 25 Apr 2016 10:16:50 +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/2016/Apr/25/t1461575862ie2cezz0lfvv38c.htm/, Retrieved Mon, 06 May 2024 01:09:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294670, Retrieved Mon, 06 May 2024 01:09:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Exponential Smoothing] [consumptieprijsin...] [2016-04-25 09:16:50] [567a9be58124adae7ccc8a0c8709ba48] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
84,97
85,57
85,74
85,88
85,88
85,96
85,96
85,99
86,02
86,14
86,3
86,32
86,32
86,77
87,47
87,39
87,3
87,31
87,31
87,38
87,4
87,32
87,37
87,4
87,4
87,89
87,7
87,89
88,02
88,08
88,08
88,15
88,21
88,41
88,39
88,41
88,41
89,1
90,35
90,61
91,18
91,22
91,22
91,4
91,52
91,68
91,71
91,77
91,77
92,16
93,64
93,78
93,96
93,82
93,82
93,89
94,05
94,46
94,62
94,72
94,72
95,76
96,14
97,11
97,19
97,43
97,43
97,56
97,66
97,75
97,82
97,82
97,82
98,35
98,19
98,19
98,21
98,22
98,26
98,23
98,26
98,5
98,51
98,51
98,51
98,89
99,55
99,9
100,12
100,09
100,09
100,09
100,46
100,71
100,79
100,79
100,93
101,15
101,53
101,91
102,18
102,24
102,2
102,32
102,43
102,45
102,84
102,96
102,96
103,1
103,4
103,74
103,97
104,29
104,33
104,46
104,9
105,31
105,63
105,68

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'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294670&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294670&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294670&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'Sir Maurice George Kendall' @ kendall.wessa.net






Estimated Parameters of Exponential Smoothing
ParameterValue
alpha1
beta0.200818967424103
gammaFALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 1 \tabularnewline
beta & 0.200818967424103 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294670&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]1[/C][/ROW]
[ROW][C]beta[/C][C]0.200818967424103[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294670&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294670&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
alpha1
beta0.200818967424103
gammaFALSE






Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
385.7486.17-0.429999999999993
485.8886.2536478440076-0.373647844007635
585.8886.3186122697938-0.438612269793779
685.9686.2305306066743-0.270530606674257
785.9686.2562029295853-0.296202929585306
885.9986.196719763118-0.206719763117988
986.0286.1852065137425-0.165206513742476
1086.1486.182029912241-0.0420299122409773
1186.386.29358950866380.00641049133616889
1286.3286.4548768569146-0.134876856914644
1386.3286.4477910257796-0.127791025779629
1486.7786.42212816393650.347871836063504
1587.4786.94198742685070.528012573149311
1687.3987.7480223665775-0.358022366577487
1787.387.5961246846067-0.296124684606667
1887.3187.4466572312152-0.136657231215153
1987.3187.4292138671515-0.119213867151501
2087.3887.4052734614475-0.0252734614475116
2187.487.4701980710164-0.0701980710163639
2287.3287.4761009668797-0.15610096687972
2387.3787.3647529318970.00524706810298881
2487.487.4158066426955-0.0158066426954662
2587.487.4426323690309-0.042632369030926
2687.8987.43407098070330.455929019296704
2787.788.0156301755771-0.315630175577141
2887.8987.76224564962980.127754350370154
2988.0287.97790114635510.0420988536448732
3088.0888.1163553946738-0.0363553946738193
3188.0888.1690545418551-0.0890545418551341
3288.1588.1511707007154-0.00117070071534897
3388.2188.2209356018065-0.0109356018065512
3488.4188.27873952554360.131260474456425
3588.3988.5050991184875-0.115099118487521
3688.4188.4619850323614-0.0519850323614435
3788.4188.4715454518411-0.0615454518410985
3889.188.45918595775270.640814042247271
3990.3589.27787357202771.07212642797232
4090.6190.7431768942412-0.133176894241174
4191.1890.97643244785490.203567552145088
4291.2291.5873126734778-0.367312673477755
4391.2291.5535493216682-0.333549321668158
4491.491.4865662913057-0.0865662913057434
4591.5291.649182138072-0.129182138072011
4691.6891.7432399144947-0.06323991449473
4791.7191.8905401401659-0.180540140165931
4891.7791.8842842556392-0.114284255639191
4991.7791.9213338094289-0.151333809428891
5092.1691.8909431100830.269056889916968
5193.6492.33497483689451.30502516310551
5293.7894.0770486426118-0.297048642611813
5393.9694.1573956409278-0.197395640927795
5493.8294.2977548521426-0.477754852142638
5593.8294.0618126160535-0.241812616053508
5693.8994.0132520561875-0.123252056187511
5794.0594.058500705531-0.00850070553104842
5894.4694.21679360262390.243206397376071
5994.6294.6756340602159-0.0556340602159082
6094.7294.8244616856898-0.104461685689756
6194.7294.9034837978342-0.183483797834157
6295.7694.8666367710140.893363228985962
6396.1496.08604105219370.053958947806322
6497.1196.47687703237540.633122967624573
6597.1997.5740201329863-0.384020132986279
6697.4397.5769016064099-0.146901606409898
6797.4397.7874009774977-0.357400977497733
6897.5697.7156280822403-0.15562808224027
6997.6697.8143750114626-0.154375011462591
7097.7597.8833735810646-0.133373581064575
7197.8297.9465896362335-0.126589636233547
7297.8297.9911680361985-0.171168036198523
7397.8297.9567942479131-0.13679424791313
7498.3597.92932336829760.420676631702349
7598.1998.5438032150956-0.353803215095567
7698.1998.3127528187687-0.122752818768745
7798.2198.2881017244552-0.0781017244552089
7898.2298.2924174167961-0.0724174167960712
7998.2698.2878746259316-0.0278746259315596
8098.2398.3222768723347-0.092276872334665
8198.2698.2737459261153-0.0137459261152912
8298.598.30098548342650.19901451657347
8398.5198.5809513731472-0.07095137314721
8498.5198.5767029916545-0.0667029916544664
8598.5198.5633077657463-0.0533077657463252
8698.8998.55260255527350.337397444726534
8799.5599.0003583617350.54964163826503
8899.999.77073682798460.129263172015357
89100.12100.146695324715-0.026695324714737
90100.09100.36133439717-0.271334397170477
91100.09100.276845303704-0.186845303704061
92100.09100.239323222746-0.149323222746162
93100.46100.2093362873420.250663712658152
94100.71100.6296743152890.080325684711454
95100.79100.89580523635-0.105805236349923
96100.79100.954557538038-0.164557538038082
97100.93100.9215112631670.00848873683258944
98101.15101.0632159625330.086784037467126
99101.53101.3006438433260.229356156674086
100101.91101.7267029098820.18329709011843
101102.18102.1435124422510.0364875577490125
102102.24102.420839835922-0.180839835921986
103102.2102.444523766803-0.24452376680297
104102.32102.355418756443-0.0354187564429651
105102.43102.468305998347-0.0383059983466154
106102.45102.570613427313-0.12061342731252
107102.84102.5663919633820.273608036617873
108102.96103.011337646775-0.0513376467746838
109102.96103.121028073559-0.161028073559393
110103.1103.0886905821010.0113094178990991
111103.4103.2309617277260.169038272274449
112103.74103.5649078190190.17509218098111
113103.97103.9400696500080.029930349992469
114104.29104.1760802319880.113919768012337
115104.33104.518957482169-0.188957482169101
116104.46104.521011235713-0.0610112357128401
117104.9104.6387590223560.261240977644292
118105.31105.1312211657350.178778834264889
119105.63105.5771233466290.0528766533705181
120105.68105.90774198156-0.227741981560172

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 85.74 & 86.17 & -0.429999999999993 \tabularnewline
4 & 85.88 & 86.2536478440076 & -0.373647844007635 \tabularnewline
5 & 85.88 & 86.3186122697938 & -0.438612269793779 \tabularnewline
6 & 85.96 & 86.2305306066743 & -0.270530606674257 \tabularnewline
7 & 85.96 & 86.2562029295853 & -0.296202929585306 \tabularnewline
8 & 85.99 & 86.196719763118 & -0.206719763117988 \tabularnewline
9 & 86.02 & 86.1852065137425 & -0.165206513742476 \tabularnewline
10 & 86.14 & 86.182029912241 & -0.0420299122409773 \tabularnewline
11 & 86.3 & 86.2935895086638 & 0.00641049133616889 \tabularnewline
12 & 86.32 & 86.4548768569146 & -0.134876856914644 \tabularnewline
13 & 86.32 & 86.4477910257796 & -0.127791025779629 \tabularnewline
14 & 86.77 & 86.4221281639365 & 0.347871836063504 \tabularnewline
15 & 87.47 & 86.9419874268507 & 0.528012573149311 \tabularnewline
16 & 87.39 & 87.7480223665775 & -0.358022366577487 \tabularnewline
17 & 87.3 & 87.5961246846067 & -0.296124684606667 \tabularnewline
18 & 87.31 & 87.4466572312152 & -0.136657231215153 \tabularnewline
19 & 87.31 & 87.4292138671515 & -0.119213867151501 \tabularnewline
20 & 87.38 & 87.4052734614475 & -0.0252734614475116 \tabularnewline
21 & 87.4 & 87.4701980710164 & -0.0701980710163639 \tabularnewline
22 & 87.32 & 87.4761009668797 & -0.15610096687972 \tabularnewline
23 & 87.37 & 87.364752931897 & 0.00524706810298881 \tabularnewline
24 & 87.4 & 87.4158066426955 & -0.0158066426954662 \tabularnewline
25 & 87.4 & 87.4426323690309 & -0.042632369030926 \tabularnewline
26 & 87.89 & 87.4340709807033 & 0.455929019296704 \tabularnewline
27 & 87.7 & 88.0156301755771 & -0.315630175577141 \tabularnewline
28 & 87.89 & 87.7622456496298 & 0.127754350370154 \tabularnewline
29 & 88.02 & 87.9779011463551 & 0.0420988536448732 \tabularnewline
30 & 88.08 & 88.1163553946738 & -0.0363553946738193 \tabularnewline
31 & 88.08 & 88.1690545418551 & -0.0890545418551341 \tabularnewline
32 & 88.15 & 88.1511707007154 & -0.00117070071534897 \tabularnewline
33 & 88.21 & 88.2209356018065 & -0.0109356018065512 \tabularnewline
34 & 88.41 & 88.2787395255436 & 0.131260474456425 \tabularnewline
35 & 88.39 & 88.5050991184875 & -0.115099118487521 \tabularnewline
36 & 88.41 & 88.4619850323614 & -0.0519850323614435 \tabularnewline
37 & 88.41 & 88.4715454518411 & -0.0615454518410985 \tabularnewline
38 & 89.1 & 88.4591859577527 & 0.640814042247271 \tabularnewline
39 & 90.35 & 89.2778735720277 & 1.07212642797232 \tabularnewline
40 & 90.61 & 90.7431768942412 & -0.133176894241174 \tabularnewline
41 & 91.18 & 90.9764324478549 & 0.203567552145088 \tabularnewline
42 & 91.22 & 91.5873126734778 & -0.367312673477755 \tabularnewline
43 & 91.22 & 91.5535493216682 & -0.333549321668158 \tabularnewline
44 & 91.4 & 91.4865662913057 & -0.0865662913057434 \tabularnewline
45 & 91.52 & 91.649182138072 & -0.129182138072011 \tabularnewline
46 & 91.68 & 91.7432399144947 & -0.06323991449473 \tabularnewline
47 & 91.71 & 91.8905401401659 & -0.180540140165931 \tabularnewline
48 & 91.77 & 91.8842842556392 & -0.114284255639191 \tabularnewline
49 & 91.77 & 91.9213338094289 & -0.151333809428891 \tabularnewline
50 & 92.16 & 91.890943110083 & 0.269056889916968 \tabularnewline
51 & 93.64 & 92.3349748368945 & 1.30502516310551 \tabularnewline
52 & 93.78 & 94.0770486426118 & -0.297048642611813 \tabularnewline
53 & 93.96 & 94.1573956409278 & -0.197395640927795 \tabularnewline
54 & 93.82 & 94.2977548521426 & -0.477754852142638 \tabularnewline
55 & 93.82 & 94.0618126160535 & -0.241812616053508 \tabularnewline
56 & 93.89 & 94.0132520561875 & -0.123252056187511 \tabularnewline
57 & 94.05 & 94.058500705531 & -0.00850070553104842 \tabularnewline
58 & 94.46 & 94.2167936026239 & 0.243206397376071 \tabularnewline
59 & 94.62 & 94.6756340602159 & -0.0556340602159082 \tabularnewline
60 & 94.72 & 94.8244616856898 & -0.104461685689756 \tabularnewline
61 & 94.72 & 94.9034837978342 & -0.183483797834157 \tabularnewline
62 & 95.76 & 94.866636771014 & 0.893363228985962 \tabularnewline
63 & 96.14 & 96.0860410521937 & 0.053958947806322 \tabularnewline
64 & 97.11 & 96.4768770323754 & 0.633122967624573 \tabularnewline
65 & 97.19 & 97.5740201329863 & -0.384020132986279 \tabularnewline
66 & 97.43 & 97.5769016064099 & -0.146901606409898 \tabularnewline
67 & 97.43 & 97.7874009774977 & -0.357400977497733 \tabularnewline
68 & 97.56 & 97.7156280822403 & -0.15562808224027 \tabularnewline
69 & 97.66 & 97.8143750114626 & -0.154375011462591 \tabularnewline
70 & 97.75 & 97.8833735810646 & -0.133373581064575 \tabularnewline
71 & 97.82 & 97.9465896362335 & -0.126589636233547 \tabularnewline
72 & 97.82 & 97.9911680361985 & -0.171168036198523 \tabularnewline
73 & 97.82 & 97.9567942479131 & -0.13679424791313 \tabularnewline
74 & 98.35 & 97.9293233682976 & 0.420676631702349 \tabularnewline
75 & 98.19 & 98.5438032150956 & -0.353803215095567 \tabularnewline
76 & 98.19 & 98.3127528187687 & -0.122752818768745 \tabularnewline
77 & 98.21 & 98.2881017244552 & -0.0781017244552089 \tabularnewline
78 & 98.22 & 98.2924174167961 & -0.0724174167960712 \tabularnewline
79 & 98.26 & 98.2878746259316 & -0.0278746259315596 \tabularnewline
80 & 98.23 & 98.3222768723347 & -0.092276872334665 \tabularnewline
81 & 98.26 & 98.2737459261153 & -0.0137459261152912 \tabularnewline
82 & 98.5 & 98.3009854834265 & 0.19901451657347 \tabularnewline
83 & 98.51 & 98.5809513731472 & -0.07095137314721 \tabularnewline
84 & 98.51 & 98.5767029916545 & -0.0667029916544664 \tabularnewline
85 & 98.51 & 98.5633077657463 & -0.0533077657463252 \tabularnewline
86 & 98.89 & 98.5526025552735 & 0.337397444726534 \tabularnewline
87 & 99.55 & 99.000358361735 & 0.54964163826503 \tabularnewline
88 & 99.9 & 99.7707368279846 & 0.129263172015357 \tabularnewline
89 & 100.12 & 100.146695324715 & -0.026695324714737 \tabularnewline
90 & 100.09 & 100.36133439717 & -0.271334397170477 \tabularnewline
91 & 100.09 & 100.276845303704 & -0.186845303704061 \tabularnewline
92 & 100.09 & 100.239323222746 & -0.149323222746162 \tabularnewline
93 & 100.46 & 100.209336287342 & 0.250663712658152 \tabularnewline
94 & 100.71 & 100.629674315289 & 0.080325684711454 \tabularnewline
95 & 100.79 & 100.89580523635 & -0.105805236349923 \tabularnewline
96 & 100.79 & 100.954557538038 & -0.164557538038082 \tabularnewline
97 & 100.93 & 100.921511263167 & 0.00848873683258944 \tabularnewline
98 & 101.15 & 101.063215962533 & 0.086784037467126 \tabularnewline
99 & 101.53 & 101.300643843326 & 0.229356156674086 \tabularnewline
100 & 101.91 & 101.726702909882 & 0.18329709011843 \tabularnewline
101 & 102.18 & 102.143512442251 & 0.0364875577490125 \tabularnewline
102 & 102.24 & 102.420839835922 & -0.180839835921986 \tabularnewline
103 & 102.2 & 102.444523766803 & -0.24452376680297 \tabularnewline
104 & 102.32 & 102.355418756443 & -0.0354187564429651 \tabularnewline
105 & 102.43 & 102.468305998347 & -0.0383059983466154 \tabularnewline
106 & 102.45 & 102.570613427313 & -0.12061342731252 \tabularnewline
107 & 102.84 & 102.566391963382 & 0.273608036617873 \tabularnewline
108 & 102.96 & 103.011337646775 & -0.0513376467746838 \tabularnewline
109 & 102.96 & 103.121028073559 & -0.161028073559393 \tabularnewline
110 & 103.1 & 103.088690582101 & 0.0113094178990991 \tabularnewline
111 & 103.4 & 103.230961727726 & 0.169038272274449 \tabularnewline
112 & 103.74 & 103.564907819019 & 0.17509218098111 \tabularnewline
113 & 103.97 & 103.940069650008 & 0.029930349992469 \tabularnewline
114 & 104.29 & 104.176080231988 & 0.113919768012337 \tabularnewline
115 & 104.33 & 104.518957482169 & -0.188957482169101 \tabularnewline
116 & 104.46 & 104.521011235713 & -0.0610112357128401 \tabularnewline
117 & 104.9 & 104.638759022356 & 0.261240977644292 \tabularnewline
118 & 105.31 & 105.131221165735 & 0.178778834264889 \tabularnewline
119 & 105.63 & 105.577123346629 & 0.0528766533705181 \tabularnewline
120 & 105.68 & 105.90774198156 & -0.227741981560172 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294670&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]3[/C][C]85.74[/C][C]86.17[/C][C]-0.429999999999993[/C][/ROW]
[ROW][C]4[/C][C]85.88[/C][C]86.2536478440076[/C][C]-0.373647844007635[/C][/ROW]
[ROW][C]5[/C][C]85.88[/C][C]86.3186122697938[/C][C]-0.438612269793779[/C][/ROW]
[ROW][C]6[/C][C]85.96[/C][C]86.2305306066743[/C][C]-0.270530606674257[/C][/ROW]
[ROW][C]7[/C][C]85.96[/C][C]86.2562029295853[/C][C]-0.296202929585306[/C][/ROW]
[ROW][C]8[/C][C]85.99[/C][C]86.196719763118[/C][C]-0.206719763117988[/C][/ROW]
[ROW][C]9[/C][C]86.02[/C][C]86.1852065137425[/C][C]-0.165206513742476[/C][/ROW]
[ROW][C]10[/C][C]86.14[/C][C]86.182029912241[/C][C]-0.0420299122409773[/C][/ROW]
[ROW][C]11[/C][C]86.3[/C][C]86.2935895086638[/C][C]0.00641049133616889[/C][/ROW]
[ROW][C]12[/C][C]86.32[/C][C]86.4548768569146[/C][C]-0.134876856914644[/C][/ROW]
[ROW][C]13[/C][C]86.32[/C][C]86.4477910257796[/C][C]-0.127791025779629[/C][/ROW]
[ROW][C]14[/C][C]86.77[/C][C]86.4221281639365[/C][C]0.347871836063504[/C][/ROW]
[ROW][C]15[/C][C]87.47[/C][C]86.9419874268507[/C][C]0.528012573149311[/C][/ROW]
[ROW][C]16[/C][C]87.39[/C][C]87.7480223665775[/C][C]-0.358022366577487[/C][/ROW]
[ROW][C]17[/C][C]87.3[/C][C]87.5961246846067[/C][C]-0.296124684606667[/C][/ROW]
[ROW][C]18[/C][C]87.31[/C][C]87.4466572312152[/C][C]-0.136657231215153[/C][/ROW]
[ROW][C]19[/C][C]87.31[/C][C]87.4292138671515[/C][C]-0.119213867151501[/C][/ROW]
[ROW][C]20[/C][C]87.38[/C][C]87.4052734614475[/C][C]-0.0252734614475116[/C][/ROW]
[ROW][C]21[/C][C]87.4[/C][C]87.4701980710164[/C][C]-0.0701980710163639[/C][/ROW]
[ROW][C]22[/C][C]87.32[/C][C]87.4761009668797[/C][C]-0.15610096687972[/C][/ROW]
[ROW][C]23[/C][C]87.37[/C][C]87.364752931897[/C][C]0.00524706810298881[/C][/ROW]
[ROW][C]24[/C][C]87.4[/C][C]87.4158066426955[/C][C]-0.0158066426954662[/C][/ROW]
[ROW][C]25[/C][C]87.4[/C][C]87.4426323690309[/C][C]-0.042632369030926[/C][/ROW]
[ROW][C]26[/C][C]87.89[/C][C]87.4340709807033[/C][C]0.455929019296704[/C][/ROW]
[ROW][C]27[/C][C]87.7[/C][C]88.0156301755771[/C][C]-0.315630175577141[/C][/ROW]
[ROW][C]28[/C][C]87.89[/C][C]87.7622456496298[/C][C]0.127754350370154[/C][/ROW]
[ROW][C]29[/C][C]88.02[/C][C]87.9779011463551[/C][C]0.0420988536448732[/C][/ROW]
[ROW][C]30[/C][C]88.08[/C][C]88.1163553946738[/C][C]-0.0363553946738193[/C][/ROW]
[ROW][C]31[/C][C]88.08[/C][C]88.1690545418551[/C][C]-0.0890545418551341[/C][/ROW]
[ROW][C]32[/C][C]88.15[/C][C]88.1511707007154[/C][C]-0.00117070071534897[/C][/ROW]
[ROW][C]33[/C][C]88.21[/C][C]88.2209356018065[/C][C]-0.0109356018065512[/C][/ROW]
[ROW][C]34[/C][C]88.41[/C][C]88.2787395255436[/C][C]0.131260474456425[/C][/ROW]
[ROW][C]35[/C][C]88.39[/C][C]88.5050991184875[/C][C]-0.115099118487521[/C][/ROW]
[ROW][C]36[/C][C]88.41[/C][C]88.4619850323614[/C][C]-0.0519850323614435[/C][/ROW]
[ROW][C]37[/C][C]88.41[/C][C]88.4715454518411[/C][C]-0.0615454518410985[/C][/ROW]
[ROW][C]38[/C][C]89.1[/C][C]88.4591859577527[/C][C]0.640814042247271[/C][/ROW]
[ROW][C]39[/C][C]90.35[/C][C]89.2778735720277[/C][C]1.07212642797232[/C][/ROW]
[ROW][C]40[/C][C]90.61[/C][C]90.7431768942412[/C][C]-0.133176894241174[/C][/ROW]
[ROW][C]41[/C][C]91.18[/C][C]90.9764324478549[/C][C]0.203567552145088[/C][/ROW]
[ROW][C]42[/C][C]91.22[/C][C]91.5873126734778[/C][C]-0.367312673477755[/C][/ROW]
[ROW][C]43[/C][C]91.22[/C][C]91.5535493216682[/C][C]-0.333549321668158[/C][/ROW]
[ROW][C]44[/C][C]91.4[/C][C]91.4865662913057[/C][C]-0.0865662913057434[/C][/ROW]
[ROW][C]45[/C][C]91.52[/C][C]91.649182138072[/C][C]-0.129182138072011[/C][/ROW]
[ROW][C]46[/C][C]91.68[/C][C]91.7432399144947[/C][C]-0.06323991449473[/C][/ROW]
[ROW][C]47[/C][C]91.71[/C][C]91.8905401401659[/C][C]-0.180540140165931[/C][/ROW]
[ROW][C]48[/C][C]91.77[/C][C]91.8842842556392[/C][C]-0.114284255639191[/C][/ROW]
[ROW][C]49[/C][C]91.77[/C][C]91.9213338094289[/C][C]-0.151333809428891[/C][/ROW]
[ROW][C]50[/C][C]92.16[/C][C]91.890943110083[/C][C]0.269056889916968[/C][/ROW]
[ROW][C]51[/C][C]93.64[/C][C]92.3349748368945[/C][C]1.30502516310551[/C][/ROW]
[ROW][C]52[/C][C]93.78[/C][C]94.0770486426118[/C][C]-0.297048642611813[/C][/ROW]
[ROW][C]53[/C][C]93.96[/C][C]94.1573956409278[/C][C]-0.197395640927795[/C][/ROW]
[ROW][C]54[/C][C]93.82[/C][C]94.2977548521426[/C][C]-0.477754852142638[/C][/ROW]
[ROW][C]55[/C][C]93.82[/C][C]94.0618126160535[/C][C]-0.241812616053508[/C][/ROW]
[ROW][C]56[/C][C]93.89[/C][C]94.0132520561875[/C][C]-0.123252056187511[/C][/ROW]
[ROW][C]57[/C][C]94.05[/C][C]94.058500705531[/C][C]-0.00850070553104842[/C][/ROW]
[ROW][C]58[/C][C]94.46[/C][C]94.2167936026239[/C][C]0.243206397376071[/C][/ROW]
[ROW][C]59[/C][C]94.62[/C][C]94.6756340602159[/C][C]-0.0556340602159082[/C][/ROW]
[ROW][C]60[/C][C]94.72[/C][C]94.8244616856898[/C][C]-0.104461685689756[/C][/ROW]
[ROW][C]61[/C][C]94.72[/C][C]94.9034837978342[/C][C]-0.183483797834157[/C][/ROW]
[ROW][C]62[/C][C]95.76[/C][C]94.866636771014[/C][C]0.893363228985962[/C][/ROW]
[ROW][C]63[/C][C]96.14[/C][C]96.0860410521937[/C][C]0.053958947806322[/C][/ROW]
[ROW][C]64[/C][C]97.11[/C][C]96.4768770323754[/C][C]0.633122967624573[/C][/ROW]
[ROW][C]65[/C][C]97.19[/C][C]97.5740201329863[/C][C]-0.384020132986279[/C][/ROW]
[ROW][C]66[/C][C]97.43[/C][C]97.5769016064099[/C][C]-0.146901606409898[/C][/ROW]
[ROW][C]67[/C][C]97.43[/C][C]97.7874009774977[/C][C]-0.357400977497733[/C][/ROW]
[ROW][C]68[/C][C]97.56[/C][C]97.7156280822403[/C][C]-0.15562808224027[/C][/ROW]
[ROW][C]69[/C][C]97.66[/C][C]97.8143750114626[/C][C]-0.154375011462591[/C][/ROW]
[ROW][C]70[/C][C]97.75[/C][C]97.8833735810646[/C][C]-0.133373581064575[/C][/ROW]
[ROW][C]71[/C][C]97.82[/C][C]97.9465896362335[/C][C]-0.126589636233547[/C][/ROW]
[ROW][C]72[/C][C]97.82[/C][C]97.9911680361985[/C][C]-0.171168036198523[/C][/ROW]
[ROW][C]73[/C][C]97.82[/C][C]97.9567942479131[/C][C]-0.13679424791313[/C][/ROW]
[ROW][C]74[/C][C]98.35[/C][C]97.9293233682976[/C][C]0.420676631702349[/C][/ROW]
[ROW][C]75[/C][C]98.19[/C][C]98.5438032150956[/C][C]-0.353803215095567[/C][/ROW]
[ROW][C]76[/C][C]98.19[/C][C]98.3127528187687[/C][C]-0.122752818768745[/C][/ROW]
[ROW][C]77[/C][C]98.21[/C][C]98.2881017244552[/C][C]-0.0781017244552089[/C][/ROW]
[ROW][C]78[/C][C]98.22[/C][C]98.2924174167961[/C][C]-0.0724174167960712[/C][/ROW]
[ROW][C]79[/C][C]98.26[/C][C]98.2878746259316[/C][C]-0.0278746259315596[/C][/ROW]
[ROW][C]80[/C][C]98.23[/C][C]98.3222768723347[/C][C]-0.092276872334665[/C][/ROW]
[ROW][C]81[/C][C]98.26[/C][C]98.2737459261153[/C][C]-0.0137459261152912[/C][/ROW]
[ROW][C]82[/C][C]98.5[/C][C]98.3009854834265[/C][C]0.19901451657347[/C][/ROW]
[ROW][C]83[/C][C]98.51[/C][C]98.5809513731472[/C][C]-0.07095137314721[/C][/ROW]
[ROW][C]84[/C][C]98.51[/C][C]98.5767029916545[/C][C]-0.0667029916544664[/C][/ROW]
[ROW][C]85[/C][C]98.51[/C][C]98.5633077657463[/C][C]-0.0533077657463252[/C][/ROW]
[ROW][C]86[/C][C]98.89[/C][C]98.5526025552735[/C][C]0.337397444726534[/C][/ROW]
[ROW][C]87[/C][C]99.55[/C][C]99.000358361735[/C][C]0.54964163826503[/C][/ROW]
[ROW][C]88[/C][C]99.9[/C][C]99.7707368279846[/C][C]0.129263172015357[/C][/ROW]
[ROW][C]89[/C][C]100.12[/C][C]100.146695324715[/C][C]-0.026695324714737[/C][/ROW]
[ROW][C]90[/C][C]100.09[/C][C]100.36133439717[/C][C]-0.271334397170477[/C][/ROW]
[ROW][C]91[/C][C]100.09[/C][C]100.276845303704[/C][C]-0.186845303704061[/C][/ROW]
[ROW][C]92[/C][C]100.09[/C][C]100.239323222746[/C][C]-0.149323222746162[/C][/ROW]
[ROW][C]93[/C][C]100.46[/C][C]100.209336287342[/C][C]0.250663712658152[/C][/ROW]
[ROW][C]94[/C][C]100.71[/C][C]100.629674315289[/C][C]0.080325684711454[/C][/ROW]
[ROW][C]95[/C][C]100.79[/C][C]100.89580523635[/C][C]-0.105805236349923[/C][/ROW]
[ROW][C]96[/C][C]100.79[/C][C]100.954557538038[/C][C]-0.164557538038082[/C][/ROW]
[ROW][C]97[/C][C]100.93[/C][C]100.921511263167[/C][C]0.00848873683258944[/C][/ROW]
[ROW][C]98[/C][C]101.15[/C][C]101.063215962533[/C][C]0.086784037467126[/C][/ROW]
[ROW][C]99[/C][C]101.53[/C][C]101.300643843326[/C][C]0.229356156674086[/C][/ROW]
[ROW][C]100[/C][C]101.91[/C][C]101.726702909882[/C][C]0.18329709011843[/C][/ROW]
[ROW][C]101[/C][C]102.18[/C][C]102.143512442251[/C][C]0.0364875577490125[/C][/ROW]
[ROW][C]102[/C][C]102.24[/C][C]102.420839835922[/C][C]-0.180839835921986[/C][/ROW]
[ROW][C]103[/C][C]102.2[/C][C]102.444523766803[/C][C]-0.24452376680297[/C][/ROW]
[ROW][C]104[/C][C]102.32[/C][C]102.355418756443[/C][C]-0.0354187564429651[/C][/ROW]
[ROW][C]105[/C][C]102.43[/C][C]102.468305998347[/C][C]-0.0383059983466154[/C][/ROW]
[ROW][C]106[/C][C]102.45[/C][C]102.570613427313[/C][C]-0.12061342731252[/C][/ROW]
[ROW][C]107[/C][C]102.84[/C][C]102.566391963382[/C][C]0.273608036617873[/C][/ROW]
[ROW][C]108[/C][C]102.96[/C][C]103.011337646775[/C][C]-0.0513376467746838[/C][/ROW]
[ROW][C]109[/C][C]102.96[/C][C]103.121028073559[/C][C]-0.161028073559393[/C][/ROW]
[ROW][C]110[/C][C]103.1[/C][C]103.088690582101[/C][C]0.0113094178990991[/C][/ROW]
[ROW][C]111[/C][C]103.4[/C][C]103.230961727726[/C][C]0.169038272274449[/C][/ROW]
[ROW][C]112[/C][C]103.74[/C][C]103.564907819019[/C][C]0.17509218098111[/C][/ROW]
[ROW][C]113[/C][C]103.97[/C][C]103.940069650008[/C][C]0.029930349992469[/C][/ROW]
[ROW][C]114[/C][C]104.29[/C][C]104.176080231988[/C][C]0.113919768012337[/C][/ROW]
[ROW][C]115[/C][C]104.33[/C][C]104.518957482169[/C][C]-0.188957482169101[/C][/ROW]
[ROW][C]116[/C][C]104.46[/C][C]104.521011235713[/C][C]-0.0610112357128401[/C][/ROW]
[ROW][C]117[/C][C]104.9[/C][C]104.638759022356[/C][C]0.261240977644292[/C][/ROW]
[ROW][C]118[/C][C]105.31[/C][C]105.131221165735[/C][C]0.178778834264889[/C][/ROW]
[ROW][C]119[/C][C]105.63[/C][C]105.577123346629[/C][C]0.0528766533705181[/C][/ROW]
[ROW][C]120[/C][C]105.68[/C][C]105.90774198156[/C][C]-0.227741981560172[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294670&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294670&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
385.7486.17-0.429999999999993
485.8886.2536478440076-0.373647844007635
585.8886.3186122697938-0.438612269793779
685.9686.2305306066743-0.270530606674257
785.9686.2562029295853-0.296202929585306
885.9986.196719763118-0.206719763117988
986.0286.1852065137425-0.165206513742476
1086.1486.182029912241-0.0420299122409773
1186.386.29358950866380.00641049133616889
1286.3286.4548768569146-0.134876856914644
1386.3286.4477910257796-0.127791025779629
1486.7786.42212816393650.347871836063504
1587.4786.94198742685070.528012573149311
1687.3987.7480223665775-0.358022366577487
1787.387.5961246846067-0.296124684606667
1887.3187.4466572312152-0.136657231215153
1987.3187.4292138671515-0.119213867151501
2087.3887.4052734614475-0.0252734614475116
2187.487.4701980710164-0.0701980710163639
2287.3287.4761009668797-0.15610096687972
2387.3787.3647529318970.00524706810298881
2487.487.4158066426955-0.0158066426954662
2587.487.4426323690309-0.042632369030926
2687.8987.43407098070330.455929019296704
2787.788.0156301755771-0.315630175577141
2887.8987.76224564962980.127754350370154
2988.0287.97790114635510.0420988536448732
3088.0888.1163553946738-0.0363553946738193
3188.0888.1690545418551-0.0890545418551341
3288.1588.1511707007154-0.00117070071534897
3388.2188.2209356018065-0.0109356018065512
3488.4188.27873952554360.131260474456425
3588.3988.5050991184875-0.115099118487521
3688.4188.4619850323614-0.0519850323614435
3788.4188.4715454518411-0.0615454518410985
3889.188.45918595775270.640814042247271
3990.3589.27787357202771.07212642797232
4090.6190.7431768942412-0.133176894241174
4191.1890.97643244785490.203567552145088
4291.2291.5873126734778-0.367312673477755
4391.2291.5535493216682-0.333549321668158
4491.491.4865662913057-0.0865662913057434
4591.5291.649182138072-0.129182138072011
4691.6891.7432399144947-0.06323991449473
4791.7191.8905401401659-0.180540140165931
4891.7791.8842842556392-0.114284255639191
4991.7791.9213338094289-0.151333809428891
5092.1691.8909431100830.269056889916968
5193.6492.33497483689451.30502516310551
5293.7894.0770486426118-0.297048642611813
5393.9694.1573956409278-0.197395640927795
5493.8294.2977548521426-0.477754852142638
5593.8294.0618126160535-0.241812616053508
5693.8994.0132520561875-0.123252056187511
5794.0594.058500705531-0.00850070553104842
5894.4694.21679360262390.243206397376071
5994.6294.6756340602159-0.0556340602159082
6094.7294.8244616856898-0.104461685689756
6194.7294.9034837978342-0.183483797834157
6295.7694.8666367710140.893363228985962
6396.1496.08604105219370.053958947806322
6497.1196.47687703237540.633122967624573
6597.1997.5740201329863-0.384020132986279
6697.4397.5769016064099-0.146901606409898
6797.4397.7874009774977-0.357400977497733
6897.5697.7156280822403-0.15562808224027
6997.6697.8143750114626-0.154375011462591
7097.7597.8833735810646-0.133373581064575
7197.8297.9465896362335-0.126589636233547
7297.8297.9911680361985-0.171168036198523
7397.8297.9567942479131-0.13679424791313
7498.3597.92932336829760.420676631702349
7598.1998.5438032150956-0.353803215095567
7698.1998.3127528187687-0.122752818768745
7798.2198.2881017244552-0.0781017244552089
7898.2298.2924174167961-0.0724174167960712
7998.2698.2878746259316-0.0278746259315596
8098.2398.3222768723347-0.092276872334665
8198.2698.2737459261153-0.0137459261152912
8298.598.30098548342650.19901451657347
8398.5198.5809513731472-0.07095137314721
8498.5198.5767029916545-0.0667029916544664
8598.5198.5633077657463-0.0533077657463252
8698.8998.55260255527350.337397444726534
8799.5599.0003583617350.54964163826503
8899.999.77073682798460.129263172015357
89100.12100.146695324715-0.026695324714737
90100.09100.36133439717-0.271334397170477
91100.09100.276845303704-0.186845303704061
92100.09100.239323222746-0.149323222746162
93100.46100.2093362873420.250663712658152
94100.71100.6296743152890.080325684711454
95100.79100.89580523635-0.105805236349923
96100.79100.954557538038-0.164557538038082
97100.93100.9215112631670.00848873683258944
98101.15101.0632159625330.086784037467126
99101.53101.3006438433260.229356156674086
100101.91101.7267029098820.18329709011843
101102.18102.1435124422510.0364875577490125
102102.24102.420839835922-0.180839835921986
103102.2102.444523766803-0.24452376680297
104102.32102.355418756443-0.0354187564429651
105102.43102.468305998347-0.0383059983466154
106102.45102.570613427313-0.12061342731252
107102.84102.5663919633820.273608036617873
108102.96103.011337646775-0.0513376467746838
109102.96103.121028073559-0.161028073559393
110103.1103.0886905821010.0113094178990991
111103.4103.2309617277260.169038272274449
112103.74103.5649078190190.17509218098111
113103.97103.9400696500080.029930349992469
114104.29104.1760802319880.113919768012337
115104.33104.518957482169-0.188957482169101
116104.46104.521011235713-0.0610112357128401
117104.9104.6387590223560.261240977644292
118105.31105.1312211657350.178778834264889
119105.63105.5771233466290.0528766533705181
120105.68105.90774198156-0.227741981560172






Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
121105.912007071984105.358000313438106.466013830531
122106.144014143968105.278279321608107.009748966328
123106.376021215952105.21306151697107.538980914935
124106.608028287937105.144944454381108.071112121492
125106.840035359921105.068406366769108.611664353072
126107.072042431905104.981254160317109.162830703493
127107.304049503889104.882572969875109.725526037903
128107.536056575873104.772022622942110.300090528804
129107.768063647857104.649545371465110.886581924249
130108.000070719841104.515229026801111.484912412882
131108.232077791826104.369237486026112.094918097626
132108.46408486381104.211773442132112.716396285487

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
121 & 105.912007071984 & 105.358000313438 & 106.466013830531 \tabularnewline
122 & 106.144014143968 & 105.278279321608 & 107.009748966328 \tabularnewline
123 & 106.376021215952 & 105.21306151697 & 107.538980914935 \tabularnewline
124 & 106.608028287937 & 105.144944454381 & 108.071112121492 \tabularnewline
125 & 106.840035359921 & 105.068406366769 & 108.611664353072 \tabularnewline
126 & 107.072042431905 & 104.981254160317 & 109.162830703493 \tabularnewline
127 & 107.304049503889 & 104.882572969875 & 109.725526037903 \tabularnewline
128 & 107.536056575873 & 104.772022622942 & 110.300090528804 \tabularnewline
129 & 107.768063647857 & 104.649545371465 & 110.886581924249 \tabularnewline
130 & 108.000070719841 & 104.515229026801 & 111.484912412882 \tabularnewline
131 & 108.232077791826 & 104.369237486026 & 112.094918097626 \tabularnewline
132 & 108.46408486381 & 104.211773442132 & 112.716396285487 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294670&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]121[/C][C]105.912007071984[/C][C]105.358000313438[/C][C]106.466013830531[/C][/ROW]
[ROW][C]122[/C][C]106.144014143968[/C][C]105.278279321608[/C][C]107.009748966328[/C][/ROW]
[ROW][C]123[/C][C]106.376021215952[/C][C]105.21306151697[/C][C]107.538980914935[/C][/ROW]
[ROW][C]124[/C][C]106.608028287937[/C][C]105.144944454381[/C][C]108.071112121492[/C][/ROW]
[ROW][C]125[/C][C]106.840035359921[/C][C]105.068406366769[/C][C]108.611664353072[/C][/ROW]
[ROW][C]126[/C][C]107.072042431905[/C][C]104.981254160317[/C][C]109.162830703493[/C][/ROW]
[ROW][C]127[/C][C]107.304049503889[/C][C]104.882572969875[/C][C]109.725526037903[/C][/ROW]
[ROW][C]128[/C][C]107.536056575873[/C][C]104.772022622942[/C][C]110.300090528804[/C][/ROW]
[ROW][C]129[/C][C]107.768063647857[/C][C]104.649545371465[/C][C]110.886581924249[/C][/ROW]
[ROW][C]130[/C][C]108.000070719841[/C][C]104.515229026801[/C][C]111.484912412882[/C][/ROW]
[ROW][C]131[/C][C]108.232077791826[/C][C]104.369237486026[/C][C]112.094918097626[/C][/ROW]
[ROW][C]132[/C][C]108.46408486381[/C][C]104.211773442132[/C][C]112.716396285487[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294670&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294670&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
121105.912007071984105.358000313438106.466013830531
122106.144014143968105.278279321608107.009748966328
123106.376021215952105.21306151697107.538980914935
124106.608028287937105.144944454381108.071112121492
125106.840035359921105.068406366769108.611664353072
126107.072042431905104.981254160317109.162830703493
127107.304049503889104.882572969875109.725526037903
128107.536056575873104.772022622942110.300090528804
129107.768063647857104.649545371465110.886581924249
130108.000070719841104.515229026801111.484912412882
131108.232077791826104.369237486026112.094918097626
132108.46408486381104.211773442132112.716396285487


Figures (Output of Computation)

Input Parameters & R Code

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