Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationWed, 02 Jun 2010 08:44:16 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Jun/02/t12754684248yswnn6xaoafvx4.htm/, Retrieved Thu, 28 Mar 2024 12:06:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=76900, Retrieved Thu, 28 Mar 2024 12:06:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W62
Estimated Impact172
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Exponential Smoothing] [nieuwe personenwagen] [2010-05-26 13:42:55] [f5ecd041e4b32af12787a4e421b18aaf]
- R PD    [Exponential Smoothing] [] [2010-06-02 08:44:16] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
40801
49081
52431
59650
75428
78705
68870
70641
80074
76464
69976
92917
92559
73981
71107
96942
86270
69610
57768
80077
71454
70382
69881
84530
79322
80181
82137
88439
91575
82909
73282
94089
108112
95653
85273
105093
102275
99308
79687
93263
114918
103374
65124
104045
101183
95492
85035
90692
107486
98179
82551
106804
110898
89950
65184
95357
98280
92146
77874
100039
104777
102341
71316
88838
85457
70784
70522
88629
88452
98886
79601
108135
113835
101617
68698
79182
86003
84165
68550
90385
100368
99081
81288
103491
111695
82504
62237
78249
92341
84412
75102
90461
106451
98379
72615
98367
116949
95832
68060
83923
87653
78054
57566
78784
88916
84662
63442
77773
88102
87972
61790
95276
104418
95420
82141
104064
96287
78426
59111
76837
76615
65860
57703
68656
77955
65856
60947
69885
80550
73694
67538
76326
84727




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server184.73.236.201 @ 184.73.236.201

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 184.73.236.201 @ 184.73.236.201 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76900&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]184.73.236.201 @ 184.73.236.201[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76900&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76900&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server184.73.236.201 @ 184.73.236.201







Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.548527818590737
beta0.000644959083758779
gamma0.777318754607708

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 0.548527818590737 \tabularnewline
beta & 0.000644959083758779 \tabularnewline
gamma & 0.777318754607708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76900&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]0.548527818590737[/C][/ROW]
[ROW][C]beta[/C][C]0.000644959083758779[/C][/ROW]
[ROW][C]gamma[/C][C]0.777318754607708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76900&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76900&T=1

As an alternative you can also use a QR Code:  

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

Estimated Parameters of Exponential Smoothing
ParameterValue
alpha0.548527818590737
beta0.000644959083758779
gamma0.777318754607708







Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
139255985982.68563034196576.3143696581
147398171586.96046683222394.03953316779
157110770498.5715023652608.428497634843
169694297786.5237936756-844.523793675617
178627087414.9842268457-1144.98422684574
186961070986.2703504739-1376.27035047389
195776877316.5777070669-19548.5777070669
208007765669.078093877314407.9219061227
217145481693.1770129404-10239.1770129404
227038270634.7007984483-252.700798448277
236988162502.41186398367378.58813601641
248453089920.4992505915-5390.4992505915
257932290255.6905370996-10933.6905370996
268018164778.698539693515402.3014603065
278213770194.843018547811942.1569814522
2888439103189.552725928-14750.5527259283
299157585079.59480093866495.40519906138
308290972758.251308114510150.7486918855
317328279035.7491092813-5753.74910928126
329408986878.2453869857210.754613015
3310811290308.882706354717803.1172936453
349565398150.921087073-2497.92108707299
358527391478.2838988088-6205.28389880882
36105093106972.388389777-1879.388389777
37102275107297.752646559-5022.75264655927
389930894317.00691440064990.99308559937
397968792815.9178297424-13128.9178297424
4093263102690.049489015-9427.04948901468
4111491894957.158704239319960.8412957607
4210337491310.511377073112063.4886229269
436512493062.119251958-27938.1192519581
4410404593284.165741870810760.8342581292
45101183102379.196874525-1196.19687452525
469549292668.27571899512823.72428100491
478503587608.6324510824-2573.63245108243
4890692106609.180934283-15917.1809342834
4910748698122.59580888489363.4041911152
509817996543.64430288181635.35569711824
518255186838.131213733-4287.13121373301
52106804102859.6803484683944.3196515321
53110898112777.735604522-1879.73560452218
548995094374.7745724244-4424.77457242443
556518473033.5320266145-7849.5320266145
569535797852.2563509315-2495.25635093151
579828095471.69271771992808.30728228015
589214689361.42628384432784.57371615572
597787482379.4880579576-4505.48805795761
6010003995630.23776504234408.7622349577
61104777107164.734780101-2387.73478010108
6210234196423.55916834525917.44083165479
637131686985.646123207-15669.646123207
648883899645.4512239981-10807.4512239981
658545799415.8046599267-13958.8046599267
667078473477.6505399322-2693.65053993219
677052251868.375963906318653.6240360937
688862993097.4877013623-4468.48770136235
698845291488.725954073-3036.72595407293
709888682154.8525934416731.14740656
717960180260.4592153847-659.459215384675
7210813598746.39010546859388.60989453146
73113835110626.2662273343208.73377266599
74101617105870.431017513-4253.43101751289
756869883275.1106313527-14577.1106313527
767918298238.2397860906-19056.2397860906
778600392372.765956804-6369.76595680404
788416574548.25560340559616.74439659451
796855067184.97228596131365.02771403872
809038590812.1005252403-427.100525240254
8110036891919.75654228568448.24345771439
829908195824.21055804843256.78944195157
838128880432.1772399301855.822760069917
84103491103272.507087103218.492912897287
85111695107947.3001022783747.69989772202
8682504100862.271080889-18358.2710808893
876223766896.0181375353-4659.01813753534
887824985720.0439735783-7471.04397357826
899234190658.07680010311682.92319989686
908441282860.35133391371551.64866608626
917510268173.8363440986928.16365590201
929046194222.0789336987-3761.07893369869
9310645196612.974253549838.02574646
949837999455.7023418756-1076.70234187559
957261580840.3224286113-8225.32242861128
969836798468.7998254973-101.799825497321
97116949104199.39360499212749.6063950084
989583294290.48283762761541.51716237245
996806076050.5858463905-7990.58584639052
1008392392062.3122483282-8139.31224832825
1018765399848.022238282-12195.022238282
1027805484388.6465854162-6334.6465854162
1035756667257.1697219225-9691.16972192255
1047878480426.1750096974-1642.1750096974
1058891688740.7260864393175.273913560726
1068466282438.29514175692223.7048582431
1076344263111.2467866248330.753213375188
1087777388273.5351006883-10500.5351006883
1098810292796.2129153977-4694.21291539774
1108797269365.404112028218606.5958879718
1116179057126.90380368894663.09619631115
1129527680017.711168161515258.2888318385
11310441899213.00994912085204.99005087925
1149542095359.463751015660.5362489844265
1158214180565.06559573161575.9344042684
116104064102750.1472747051313.85272529542
11796287113336.078986117-17049.078986117
1187842698310.4952321351-19884.4952321351
1195911166190.3665165909-7079.36651659092
1207683783482.4587396106-6645.45873961064
1217661592154.3440649274-15539.3440649274
1226586070944.9097820315-5084.90978203154
1235770340802.373213580116900.6267864199
1246865674113.1041187262-5457.10411872623
1257795578399.0665012763-444.066501276335
1266585669621.1892586357-3765.18925863574
1276094753238.4481545697708.55184543097
1286988578675.9997528165-8790.99975281647
1298055077251.85017859463298.14982140542
1307369472376.38928054721317.6107194528
1316753856371.666497091211166.3335029088
1327632683822.4196999445-7496.41969994445
1338472788904.1400319154-4177.14003191541

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
13 & 92559 & 85982.6856303419 & 6576.3143696581 \tabularnewline
14 & 73981 & 71586.9604668322 & 2394.03953316779 \tabularnewline
15 & 71107 & 70498.5715023652 & 608.428497634843 \tabularnewline
16 & 96942 & 97786.5237936756 & -844.523793675617 \tabularnewline
17 & 86270 & 87414.9842268457 & -1144.98422684574 \tabularnewline
18 & 69610 & 70986.2703504739 & -1376.27035047389 \tabularnewline
19 & 57768 & 77316.5777070669 & -19548.5777070669 \tabularnewline
20 & 80077 & 65669.0780938773 & 14407.9219061227 \tabularnewline
21 & 71454 & 81693.1770129404 & -10239.1770129404 \tabularnewline
22 & 70382 & 70634.7007984483 & -252.700798448277 \tabularnewline
23 & 69881 & 62502.4118639836 & 7378.58813601641 \tabularnewline
24 & 84530 & 89920.4992505915 & -5390.4992505915 \tabularnewline
25 & 79322 & 90255.6905370996 & -10933.6905370996 \tabularnewline
26 & 80181 & 64778.6985396935 & 15402.3014603065 \tabularnewline
27 & 82137 & 70194.8430185478 & 11942.1569814522 \tabularnewline
28 & 88439 & 103189.552725928 & -14750.5527259283 \tabularnewline
29 & 91575 & 85079.5948009386 & 6495.40519906138 \tabularnewline
30 & 82909 & 72758.2513081145 & 10150.7486918855 \tabularnewline
31 & 73282 & 79035.7491092813 & -5753.74910928126 \tabularnewline
32 & 94089 & 86878.245386985 & 7210.754613015 \tabularnewline
33 & 108112 & 90308.8827063547 & 17803.1172936453 \tabularnewline
34 & 95653 & 98150.921087073 & -2497.92108707299 \tabularnewline
35 & 85273 & 91478.2838988088 & -6205.28389880882 \tabularnewline
36 & 105093 & 106972.388389777 & -1879.388389777 \tabularnewline
37 & 102275 & 107297.752646559 & -5022.75264655927 \tabularnewline
38 & 99308 & 94317.0069144006 & 4990.99308559937 \tabularnewline
39 & 79687 & 92815.9178297424 & -13128.9178297424 \tabularnewline
40 & 93263 & 102690.049489015 & -9427.04948901468 \tabularnewline
41 & 114918 & 94957.1587042393 & 19960.8412957607 \tabularnewline
42 & 103374 & 91310.5113770731 & 12063.4886229269 \tabularnewline
43 & 65124 & 93062.119251958 & -27938.1192519581 \tabularnewline
44 & 104045 & 93284.1657418708 & 10760.8342581292 \tabularnewline
45 & 101183 & 102379.196874525 & -1196.19687452525 \tabularnewline
46 & 95492 & 92668.2757189951 & 2823.72428100491 \tabularnewline
47 & 85035 & 87608.6324510824 & -2573.63245108243 \tabularnewline
48 & 90692 & 106609.180934283 & -15917.1809342834 \tabularnewline
49 & 107486 & 98122.5958088848 & 9363.4041911152 \tabularnewline
50 & 98179 & 96543.6443028818 & 1635.35569711824 \tabularnewline
51 & 82551 & 86838.131213733 & -4287.13121373301 \tabularnewline
52 & 106804 & 102859.680348468 & 3944.3196515321 \tabularnewline
53 & 110898 & 112777.735604522 & -1879.73560452218 \tabularnewline
54 & 89950 & 94374.7745724244 & -4424.77457242443 \tabularnewline
55 & 65184 & 73033.5320266145 & -7849.5320266145 \tabularnewline
56 & 95357 & 97852.2563509315 & -2495.25635093151 \tabularnewline
57 & 98280 & 95471.6927177199 & 2808.30728228015 \tabularnewline
58 & 92146 & 89361.4262838443 & 2784.57371615572 \tabularnewline
59 & 77874 & 82379.4880579576 & -4505.48805795761 \tabularnewline
60 & 100039 & 95630.2377650423 & 4408.7622349577 \tabularnewline
61 & 104777 & 107164.734780101 & -2387.73478010108 \tabularnewline
62 & 102341 & 96423.5591683452 & 5917.44083165479 \tabularnewline
63 & 71316 & 86985.646123207 & -15669.646123207 \tabularnewline
64 & 88838 & 99645.4512239981 & -10807.4512239981 \tabularnewline
65 & 85457 & 99415.8046599267 & -13958.8046599267 \tabularnewline
66 & 70784 & 73477.6505399322 & -2693.65053993219 \tabularnewline
67 & 70522 & 51868.3759639063 & 18653.6240360937 \tabularnewline
68 & 88629 & 93097.4877013623 & -4468.48770136235 \tabularnewline
69 & 88452 & 91488.725954073 & -3036.72595407293 \tabularnewline
70 & 98886 & 82154.85259344 & 16731.14740656 \tabularnewline
71 & 79601 & 80260.4592153847 & -659.459215384675 \tabularnewline
72 & 108135 & 98746.3901054685 & 9388.60989453146 \tabularnewline
73 & 113835 & 110626.266227334 & 3208.73377266599 \tabularnewline
74 & 101617 & 105870.431017513 & -4253.43101751289 \tabularnewline
75 & 68698 & 83275.1106313527 & -14577.1106313527 \tabularnewline
76 & 79182 & 98238.2397860906 & -19056.2397860906 \tabularnewline
77 & 86003 & 92372.765956804 & -6369.76595680404 \tabularnewline
78 & 84165 & 74548.2556034055 & 9616.74439659451 \tabularnewline
79 & 68550 & 67184.9722859613 & 1365.02771403872 \tabularnewline
80 & 90385 & 90812.1005252403 & -427.100525240254 \tabularnewline
81 & 100368 & 91919.7565422856 & 8448.24345771439 \tabularnewline
82 & 99081 & 95824.2105580484 & 3256.78944195157 \tabularnewline
83 & 81288 & 80432.1772399301 & 855.822760069917 \tabularnewline
84 & 103491 & 103272.507087103 & 218.492912897287 \tabularnewline
85 & 111695 & 107947.300102278 & 3747.69989772202 \tabularnewline
86 & 82504 & 100862.271080889 & -18358.2710808893 \tabularnewline
87 & 62237 & 66896.0181375353 & -4659.01813753534 \tabularnewline
88 & 78249 & 85720.0439735783 & -7471.04397357826 \tabularnewline
89 & 92341 & 90658.0768001031 & 1682.92319989686 \tabularnewline
90 & 84412 & 82860.3513339137 & 1551.64866608626 \tabularnewline
91 & 75102 & 68173.836344098 & 6928.16365590201 \tabularnewline
92 & 90461 & 94222.0789336987 & -3761.07893369869 \tabularnewline
93 & 106451 & 96612.97425354 & 9838.02574646 \tabularnewline
94 & 98379 & 99455.7023418756 & -1076.70234187559 \tabularnewline
95 & 72615 & 80840.3224286113 & -8225.32242861128 \tabularnewline
96 & 98367 & 98468.7998254973 & -101.799825497321 \tabularnewline
97 & 116949 & 104199.393604992 & 12749.6063950084 \tabularnewline
98 & 95832 & 94290.4828376276 & 1541.51716237245 \tabularnewline
99 & 68060 & 76050.5858463905 & -7990.58584639052 \tabularnewline
100 & 83923 & 92062.3122483282 & -8139.31224832825 \tabularnewline
101 & 87653 & 99848.022238282 & -12195.022238282 \tabularnewline
102 & 78054 & 84388.6465854162 & -6334.6465854162 \tabularnewline
103 & 57566 & 67257.1697219225 & -9691.16972192255 \tabularnewline
104 & 78784 & 80426.1750096974 & -1642.1750096974 \tabularnewline
105 & 88916 & 88740.7260864393 & 175.273913560726 \tabularnewline
106 & 84662 & 82438.2951417569 & 2223.7048582431 \tabularnewline
107 & 63442 & 63111.2467866248 & 330.753213375188 \tabularnewline
108 & 77773 & 88273.5351006883 & -10500.5351006883 \tabularnewline
109 & 88102 & 92796.2129153977 & -4694.21291539774 \tabularnewline
110 & 87972 & 69365.4041120282 & 18606.5958879718 \tabularnewline
111 & 61790 & 57126.9038036889 & 4663.09619631115 \tabularnewline
112 & 95276 & 80017.7111681615 & 15258.2888318385 \tabularnewline
113 & 104418 & 99213.0099491208 & 5204.99005087925 \tabularnewline
114 & 95420 & 95359.4637510156 & 60.5362489844265 \tabularnewline
115 & 82141 & 80565.0655957316 & 1575.9344042684 \tabularnewline
116 & 104064 & 102750.147274705 & 1313.85272529542 \tabularnewline
117 & 96287 & 113336.078986117 & -17049.078986117 \tabularnewline
118 & 78426 & 98310.4952321351 & -19884.4952321351 \tabularnewline
119 & 59111 & 66190.3665165909 & -7079.36651659092 \tabularnewline
120 & 76837 & 83482.4587396106 & -6645.45873961064 \tabularnewline
121 & 76615 & 92154.3440649274 & -15539.3440649274 \tabularnewline
122 & 65860 & 70944.9097820315 & -5084.90978203154 \tabularnewline
123 & 57703 & 40802.3732135801 & 16900.6267864199 \tabularnewline
124 & 68656 & 74113.1041187262 & -5457.10411872623 \tabularnewline
125 & 77955 & 78399.0665012763 & -444.066501276335 \tabularnewline
126 & 65856 & 69621.1892586357 & -3765.18925863574 \tabularnewline
127 & 60947 & 53238.448154569 & 7708.55184543097 \tabularnewline
128 & 69885 & 78675.9997528165 & -8790.99975281647 \tabularnewline
129 & 80550 & 77251.8501785946 & 3298.14982140542 \tabularnewline
130 & 73694 & 72376.3892805472 & 1317.6107194528 \tabularnewline
131 & 67538 & 56371.6664970912 & 11166.3335029088 \tabularnewline
132 & 76326 & 83822.4196999445 & -7496.41969994445 \tabularnewline
133 & 84727 & 88904.1400319154 & -4177.14003191541 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76900&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]92559[/C][C]85982.6856303419[/C][C]6576.3143696581[/C][/ROW]
[ROW][C]14[/C][C]73981[/C][C]71586.9604668322[/C][C]2394.03953316779[/C][/ROW]
[ROW][C]15[/C][C]71107[/C][C]70498.5715023652[/C][C]608.428497634843[/C][/ROW]
[ROW][C]16[/C][C]96942[/C][C]97786.5237936756[/C][C]-844.523793675617[/C][/ROW]
[ROW][C]17[/C][C]86270[/C][C]87414.9842268457[/C][C]-1144.98422684574[/C][/ROW]
[ROW][C]18[/C][C]69610[/C][C]70986.2703504739[/C][C]-1376.27035047389[/C][/ROW]
[ROW][C]19[/C][C]57768[/C][C]77316.5777070669[/C][C]-19548.5777070669[/C][/ROW]
[ROW][C]20[/C][C]80077[/C][C]65669.0780938773[/C][C]14407.9219061227[/C][/ROW]
[ROW][C]21[/C][C]71454[/C][C]81693.1770129404[/C][C]-10239.1770129404[/C][/ROW]
[ROW][C]22[/C][C]70382[/C][C]70634.7007984483[/C][C]-252.700798448277[/C][/ROW]
[ROW][C]23[/C][C]69881[/C][C]62502.4118639836[/C][C]7378.58813601641[/C][/ROW]
[ROW][C]24[/C][C]84530[/C][C]89920.4992505915[/C][C]-5390.4992505915[/C][/ROW]
[ROW][C]25[/C][C]79322[/C][C]90255.6905370996[/C][C]-10933.6905370996[/C][/ROW]
[ROW][C]26[/C][C]80181[/C][C]64778.6985396935[/C][C]15402.3014603065[/C][/ROW]
[ROW][C]27[/C][C]82137[/C][C]70194.8430185478[/C][C]11942.1569814522[/C][/ROW]
[ROW][C]28[/C][C]88439[/C][C]103189.552725928[/C][C]-14750.5527259283[/C][/ROW]
[ROW][C]29[/C][C]91575[/C][C]85079.5948009386[/C][C]6495.40519906138[/C][/ROW]
[ROW][C]30[/C][C]82909[/C][C]72758.2513081145[/C][C]10150.7486918855[/C][/ROW]
[ROW][C]31[/C][C]73282[/C][C]79035.7491092813[/C][C]-5753.74910928126[/C][/ROW]
[ROW][C]32[/C][C]94089[/C][C]86878.245386985[/C][C]7210.754613015[/C][/ROW]
[ROW][C]33[/C][C]108112[/C][C]90308.8827063547[/C][C]17803.1172936453[/C][/ROW]
[ROW][C]34[/C][C]95653[/C][C]98150.921087073[/C][C]-2497.92108707299[/C][/ROW]
[ROW][C]35[/C][C]85273[/C][C]91478.2838988088[/C][C]-6205.28389880882[/C][/ROW]
[ROW][C]36[/C][C]105093[/C][C]106972.388389777[/C][C]-1879.388389777[/C][/ROW]
[ROW][C]37[/C][C]102275[/C][C]107297.752646559[/C][C]-5022.75264655927[/C][/ROW]
[ROW][C]38[/C][C]99308[/C][C]94317.0069144006[/C][C]4990.99308559937[/C][/ROW]
[ROW][C]39[/C][C]79687[/C][C]92815.9178297424[/C][C]-13128.9178297424[/C][/ROW]
[ROW][C]40[/C][C]93263[/C][C]102690.049489015[/C][C]-9427.04948901468[/C][/ROW]
[ROW][C]41[/C][C]114918[/C][C]94957.1587042393[/C][C]19960.8412957607[/C][/ROW]
[ROW][C]42[/C][C]103374[/C][C]91310.5113770731[/C][C]12063.4886229269[/C][/ROW]
[ROW][C]43[/C][C]65124[/C][C]93062.119251958[/C][C]-27938.1192519581[/C][/ROW]
[ROW][C]44[/C][C]104045[/C][C]93284.1657418708[/C][C]10760.8342581292[/C][/ROW]
[ROW][C]45[/C][C]101183[/C][C]102379.196874525[/C][C]-1196.19687452525[/C][/ROW]
[ROW][C]46[/C][C]95492[/C][C]92668.2757189951[/C][C]2823.72428100491[/C][/ROW]
[ROW][C]47[/C][C]85035[/C][C]87608.6324510824[/C][C]-2573.63245108243[/C][/ROW]
[ROW][C]48[/C][C]90692[/C][C]106609.180934283[/C][C]-15917.1809342834[/C][/ROW]
[ROW][C]49[/C][C]107486[/C][C]98122.5958088848[/C][C]9363.4041911152[/C][/ROW]
[ROW][C]50[/C][C]98179[/C][C]96543.6443028818[/C][C]1635.35569711824[/C][/ROW]
[ROW][C]51[/C][C]82551[/C][C]86838.131213733[/C][C]-4287.13121373301[/C][/ROW]
[ROW][C]52[/C][C]106804[/C][C]102859.680348468[/C][C]3944.3196515321[/C][/ROW]
[ROW][C]53[/C][C]110898[/C][C]112777.735604522[/C][C]-1879.73560452218[/C][/ROW]
[ROW][C]54[/C][C]89950[/C][C]94374.7745724244[/C][C]-4424.77457242443[/C][/ROW]
[ROW][C]55[/C][C]65184[/C][C]73033.5320266145[/C][C]-7849.5320266145[/C][/ROW]
[ROW][C]56[/C][C]95357[/C][C]97852.2563509315[/C][C]-2495.25635093151[/C][/ROW]
[ROW][C]57[/C][C]98280[/C][C]95471.6927177199[/C][C]2808.30728228015[/C][/ROW]
[ROW][C]58[/C][C]92146[/C][C]89361.4262838443[/C][C]2784.57371615572[/C][/ROW]
[ROW][C]59[/C][C]77874[/C][C]82379.4880579576[/C][C]-4505.48805795761[/C][/ROW]
[ROW][C]60[/C][C]100039[/C][C]95630.2377650423[/C][C]4408.7622349577[/C][/ROW]
[ROW][C]61[/C][C]104777[/C][C]107164.734780101[/C][C]-2387.73478010108[/C][/ROW]
[ROW][C]62[/C][C]102341[/C][C]96423.5591683452[/C][C]5917.44083165479[/C][/ROW]
[ROW][C]63[/C][C]71316[/C][C]86985.646123207[/C][C]-15669.646123207[/C][/ROW]
[ROW][C]64[/C][C]88838[/C][C]99645.4512239981[/C][C]-10807.4512239981[/C][/ROW]
[ROW][C]65[/C][C]85457[/C][C]99415.8046599267[/C][C]-13958.8046599267[/C][/ROW]
[ROW][C]66[/C][C]70784[/C][C]73477.6505399322[/C][C]-2693.65053993219[/C][/ROW]
[ROW][C]67[/C][C]70522[/C][C]51868.3759639063[/C][C]18653.6240360937[/C][/ROW]
[ROW][C]68[/C][C]88629[/C][C]93097.4877013623[/C][C]-4468.48770136235[/C][/ROW]
[ROW][C]69[/C][C]88452[/C][C]91488.725954073[/C][C]-3036.72595407293[/C][/ROW]
[ROW][C]70[/C][C]98886[/C][C]82154.85259344[/C][C]16731.14740656[/C][/ROW]
[ROW][C]71[/C][C]79601[/C][C]80260.4592153847[/C][C]-659.459215384675[/C][/ROW]
[ROW][C]72[/C][C]108135[/C][C]98746.3901054685[/C][C]9388.60989453146[/C][/ROW]
[ROW][C]73[/C][C]113835[/C][C]110626.266227334[/C][C]3208.73377266599[/C][/ROW]
[ROW][C]74[/C][C]101617[/C][C]105870.431017513[/C][C]-4253.43101751289[/C][/ROW]
[ROW][C]75[/C][C]68698[/C][C]83275.1106313527[/C][C]-14577.1106313527[/C][/ROW]
[ROW][C]76[/C][C]79182[/C][C]98238.2397860906[/C][C]-19056.2397860906[/C][/ROW]
[ROW][C]77[/C][C]86003[/C][C]92372.765956804[/C][C]-6369.76595680404[/C][/ROW]
[ROW][C]78[/C][C]84165[/C][C]74548.2556034055[/C][C]9616.74439659451[/C][/ROW]
[ROW][C]79[/C][C]68550[/C][C]67184.9722859613[/C][C]1365.02771403872[/C][/ROW]
[ROW][C]80[/C][C]90385[/C][C]90812.1005252403[/C][C]-427.100525240254[/C][/ROW]
[ROW][C]81[/C][C]100368[/C][C]91919.7565422856[/C][C]8448.24345771439[/C][/ROW]
[ROW][C]82[/C][C]99081[/C][C]95824.2105580484[/C][C]3256.78944195157[/C][/ROW]
[ROW][C]83[/C][C]81288[/C][C]80432.1772399301[/C][C]855.822760069917[/C][/ROW]
[ROW][C]84[/C][C]103491[/C][C]103272.507087103[/C][C]218.492912897287[/C][/ROW]
[ROW][C]85[/C][C]111695[/C][C]107947.300102278[/C][C]3747.69989772202[/C][/ROW]
[ROW][C]86[/C][C]82504[/C][C]100862.271080889[/C][C]-18358.2710808893[/C][/ROW]
[ROW][C]87[/C][C]62237[/C][C]66896.0181375353[/C][C]-4659.01813753534[/C][/ROW]
[ROW][C]88[/C][C]78249[/C][C]85720.0439735783[/C][C]-7471.04397357826[/C][/ROW]
[ROW][C]89[/C][C]92341[/C][C]90658.0768001031[/C][C]1682.92319989686[/C][/ROW]
[ROW][C]90[/C][C]84412[/C][C]82860.3513339137[/C][C]1551.64866608626[/C][/ROW]
[ROW][C]91[/C][C]75102[/C][C]68173.836344098[/C][C]6928.16365590201[/C][/ROW]
[ROW][C]92[/C][C]90461[/C][C]94222.0789336987[/C][C]-3761.07893369869[/C][/ROW]
[ROW][C]93[/C][C]106451[/C][C]96612.97425354[/C][C]9838.02574646[/C][/ROW]
[ROW][C]94[/C][C]98379[/C][C]99455.7023418756[/C][C]-1076.70234187559[/C][/ROW]
[ROW][C]95[/C][C]72615[/C][C]80840.3224286113[/C][C]-8225.32242861128[/C][/ROW]
[ROW][C]96[/C][C]98367[/C][C]98468.7998254973[/C][C]-101.799825497321[/C][/ROW]
[ROW][C]97[/C][C]116949[/C][C]104199.393604992[/C][C]12749.6063950084[/C][/ROW]
[ROW][C]98[/C][C]95832[/C][C]94290.4828376276[/C][C]1541.51716237245[/C][/ROW]
[ROW][C]99[/C][C]68060[/C][C]76050.5858463905[/C][C]-7990.58584639052[/C][/ROW]
[ROW][C]100[/C][C]83923[/C][C]92062.3122483282[/C][C]-8139.31224832825[/C][/ROW]
[ROW][C]101[/C][C]87653[/C][C]99848.022238282[/C][C]-12195.022238282[/C][/ROW]
[ROW][C]102[/C][C]78054[/C][C]84388.6465854162[/C][C]-6334.6465854162[/C][/ROW]
[ROW][C]103[/C][C]57566[/C][C]67257.1697219225[/C][C]-9691.16972192255[/C][/ROW]
[ROW][C]104[/C][C]78784[/C][C]80426.1750096974[/C][C]-1642.1750096974[/C][/ROW]
[ROW][C]105[/C][C]88916[/C][C]88740.7260864393[/C][C]175.273913560726[/C][/ROW]
[ROW][C]106[/C][C]84662[/C][C]82438.2951417569[/C][C]2223.7048582431[/C][/ROW]
[ROW][C]107[/C][C]63442[/C][C]63111.2467866248[/C][C]330.753213375188[/C][/ROW]
[ROW][C]108[/C][C]77773[/C][C]88273.5351006883[/C][C]-10500.5351006883[/C][/ROW]
[ROW][C]109[/C][C]88102[/C][C]92796.2129153977[/C][C]-4694.21291539774[/C][/ROW]
[ROW][C]110[/C][C]87972[/C][C]69365.4041120282[/C][C]18606.5958879718[/C][/ROW]
[ROW][C]111[/C][C]61790[/C][C]57126.9038036889[/C][C]4663.09619631115[/C][/ROW]
[ROW][C]112[/C][C]95276[/C][C]80017.7111681615[/C][C]15258.2888318385[/C][/ROW]
[ROW][C]113[/C][C]104418[/C][C]99213.0099491208[/C][C]5204.99005087925[/C][/ROW]
[ROW][C]114[/C][C]95420[/C][C]95359.4637510156[/C][C]60.5362489844265[/C][/ROW]
[ROW][C]115[/C][C]82141[/C][C]80565.0655957316[/C][C]1575.9344042684[/C][/ROW]
[ROW][C]116[/C][C]104064[/C][C]102750.147274705[/C][C]1313.85272529542[/C][/ROW]
[ROW][C]117[/C][C]96287[/C][C]113336.078986117[/C][C]-17049.078986117[/C][/ROW]
[ROW][C]118[/C][C]78426[/C][C]98310.4952321351[/C][C]-19884.4952321351[/C][/ROW]
[ROW][C]119[/C][C]59111[/C][C]66190.3665165909[/C][C]-7079.36651659092[/C][/ROW]
[ROW][C]120[/C][C]76837[/C][C]83482.4587396106[/C][C]-6645.45873961064[/C][/ROW]
[ROW][C]121[/C][C]76615[/C][C]92154.3440649274[/C][C]-15539.3440649274[/C][/ROW]
[ROW][C]122[/C][C]65860[/C][C]70944.9097820315[/C][C]-5084.90978203154[/C][/ROW]
[ROW][C]123[/C][C]57703[/C][C]40802.3732135801[/C][C]16900.6267864199[/C][/ROW]
[ROW][C]124[/C][C]68656[/C][C]74113.1041187262[/C][C]-5457.10411872623[/C][/ROW]
[ROW][C]125[/C][C]77955[/C][C]78399.0665012763[/C][C]-444.066501276335[/C][/ROW]
[ROW][C]126[/C][C]65856[/C][C]69621.1892586357[/C][C]-3765.18925863574[/C][/ROW]
[ROW][C]127[/C][C]60947[/C][C]53238.448154569[/C][C]7708.55184543097[/C][/ROW]
[ROW][C]128[/C][C]69885[/C][C]78675.9997528165[/C][C]-8790.99975281647[/C][/ROW]
[ROW][C]129[/C][C]80550[/C][C]77251.8501785946[/C][C]3298.14982140542[/C][/ROW]
[ROW][C]130[/C][C]73694[/C][C]72376.3892805472[/C][C]1317.6107194528[/C][/ROW]
[ROW][C]131[/C][C]67538[/C][C]56371.6664970912[/C][C]11166.3335029088[/C][/ROW]
[ROW][C]132[/C][C]76326[/C][C]83822.4196999445[/C][C]-7496.41969994445[/C][/ROW]
[ROW][C]133[/C][C]84727[/C][C]88904.1400319154[/C][C]-4177.14003191541[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76900&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76900&T=2

As an alternative you can also use a QR Code:  

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

Interpolation Forecasts of Exponential Smoothing
tObservedFittedResiduals
139255985982.68563034196576.3143696581
147398171586.96046683222394.03953316779
157110770498.5715023652608.428497634843
169694297786.5237936756-844.523793675617
178627087414.9842268457-1144.98422684574
186961070986.2703504739-1376.27035047389
195776877316.5777070669-19548.5777070669
208007765669.078093877314407.9219061227
217145481693.1770129404-10239.1770129404
227038270634.7007984483-252.700798448277
236988162502.41186398367378.58813601641
248453089920.4992505915-5390.4992505915
257932290255.6905370996-10933.6905370996
268018164778.698539693515402.3014603065
278213770194.843018547811942.1569814522
2888439103189.552725928-14750.5527259283
299157585079.59480093866495.40519906138
308290972758.251308114510150.7486918855
317328279035.7491092813-5753.74910928126
329408986878.2453869857210.754613015
3310811290308.882706354717803.1172936453
349565398150.921087073-2497.92108707299
358527391478.2838988088-6205.28389880882
36105093106972.388389777-1879.388389777
37102275107297.752646559-5022.75264655927
389930894317.00691440064990.99308559937
397968792815.9178297424-13128.9178297424
4093263102690.049489015-9427.04948901468
4111491894957.158704239319960.8412957607
4210337491310.511377073112063.4886229269
436512493062.119251958-27938.1192519581
4410404593284.165741870810760.8342581292
45101183102379.196874525-1196.19687452525
469549292668.27571899512823.72428100491
478503587608.6324510824-2573.63245108243
4890692106609.180934283-15917.1809342834
4910748698122.59580888489363.4041911152
509817996543.64430288181635.35569711824
518255186838.131213733-4287.13121373301
52106804102859.6803484683944.3196515321
53110898112777.735604522-1879.73560452218
548995094374.7745724244-4424.77457242443
556518473033.5320266145-7849.5320266145
569535797852.2563509315-2495.25635093151
579828095471.69271771992808.30728228015
589214689361.42628384432784.57371615572
597787482379.4880579576-4505.48805795761
6010003995630.23776504234408.7622349577
61104777107164.734780101-2387.73478010108
6210234196423.55916834525917.44083165479
637131686985.646123207-15669.646123207
648883899645.4512239981-10807.4512239981
658545799415.8046599267-13958.8046599267
667078473477.6505399322-2693.65053993219
677052251868.375963906318653.6240360937
688862993097.4877013623-4468.48770136235
698845291488.725954073-3036.72595407293
709888682154.8525934416731.14740656
717960180260.4592153847-659.459215384675
7210813598746.39010546859388.60989453146
73113835110626.2662273343208.73377266599
74101617105870.431017513-4253.43101751289
756869883275.1106313527-14577.1106313527
767918298238.2397860906-19056.2397860906
778600392372.765956804-6369.76595680404
788416574548.25560340559616.74439659451
796855067184.97228596131365.02771403872
809038590812.1005252403-427.100525240254
8110036891919.75654228568448.24345771439
829908195824.21055804843256.78944195157
838128880432.1772399301855.822760069917
84103491103272.507087103218.492912897287
85111695107947.3001022783747.69989772202
8682504100862.271080889-18358.2710808893
876223766896.0181375353-4659.01813753534
887824985720.0439735783-7471.04397357826
899234190658.07680010311682.92319989686
908441282860.35133391371551.64866608626
917510268173.8363440986928.16365590201
929046194222.0789336987-3761.07893369869
9310645196612.974253549838.02574646
949837999455.7023418756-1076.70234187559
957261580840.3224286113-8225.32242861128
969836798468.7998254973-101.799825497321
97116949104199.39360499212749.6063950084
989583294290.48283762761541.51716237245
996806076050.5858463905-7990.58584639052
1008392392062.3122483282-8139.31224832825
1018765399848.022238282-12195.022238282
1027805484388.6465854162-6334.6465854162
1035756667257.1697219225-9691.16972192255
1047878480426.1750096974-1642.1750096974
1058891688740.7260864393175.273913560726
1068466282438.29514175692223.7048582431
1076344263111.2467866248330.753213375188
1087777388273.5351006883-10500.5351006883
1098810292796.2129153977-4694.21291539774
1108797269365.404112028218606.5958879718
1116179057126.90380368894663.09619631115
1129527680017.711168161515258.2888318385
11310441899213.00994912085204.99005087925
1149542095359.463751015660.5362489844265
1158214180565.06559573161575.9344042684
116104064102750.1472747051313.85272529542
11796287113336.078986117-17049.078986117
1187842698310.4952321351-19884.4952321351
1195911166190.3665165909-7079.36651659092
1207683783482.4587396106-6645.45873961064
1217661592154.3440649274-15539.3440649274
1226586070944.9097820315-5084.90978203154
1235770340802.373213580116900.6267864199
1246865674113.1041187262-5457.10411872623
1257795578399.0665012763-444.066501276335
1266585669621.1892586357-3765.18925863574
1276094753238.4481545697708.55184543097
1286988578675.9997528165-8790.99975281647
1298055077251.85017859463298.14982140542
1307369472376.38928054721317.6107194528
1316753856371.666497091211166.3335029088
1327632683822.4196999445-7496.41969994445
1338472788904.1400319154-4177.14003191541







Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
13477597.877802282259479.76865782895715.9869467364
13557963.741290255437295.822408176678631.6601723341
13674155.486310694851216.711479630797094.2611417589
13783193.667893694558186.8784027714108200.457384618
13873493.604508595646574.8500283783100412.358988813
13963203.814407557634497.944985265991909.6838298492
14078621.038087817848230.8687499474109011.207425688
14186262.989420700854275.0888296753118250.890011726
14278883.64226897645372.1935242394112395.091013713
14365612.28295165230641.7709050069100582.794998297
14480384.396312777544011.5436078764116757.249017679
14590741.494293436653016.6963164359128466.292270437

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
134 & 77597.8778022822 & 59479.768657828 & 95715.9869467364 \tabularnewline
135 & 57963.7412902554 & 37295.8224081766 & 78631.6601723341 \tabularnewline
136 & 74155.4863106948 & 51216.7114796307 & 97094.2611417589 \tabularnewline
137 & 83193.6678936945 & 58186.8784027714 & 108200.457384618 \tabularnewline
138 & 73493.6045085956 & 46574.8500283783 & 100412.358988813 \tabularnewline
139 & 63203.8144075576 & 34497.9449852659 & 91909.6838298492 \tabularnewline
140 & 78621.0380878178 & 48230.8687499474 & 109011.207425688 \tabularnewline
141 & 86262.9894207008 & 54275.0888296753 & 118250.890011726 \tabularnewline
142 & 78883.642268976 & 45372.1935242394 & 112395.091013713 \tabularnewline
143 & 65612.282951652 & 30641.7709050069 & 100582.794998297 \tabularnewline
144 & 80384.3963127775 & 44011.5436078764 & 116757.249017679 \tabularnewline
145 & 90741.4942934366 & 53016.6963164359 & 128466.292270437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76900&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]134[/C][C]77597.8778022822[/C][C]59479.768657828[/C][C]95715.9869467364[/C][/ROW]
[ROW][C]135[/C][C]57963.7412902554[/C][C]37295.8224081766[/C][C]78631.6601723341[/C][/ROW]
[ROW][C]136[/C][C]74155.4863106948[/C][C]51216.7114796307[/C][C]97094.2611417589[/C][/ROW]
[ROW][C]137[/C][C]83193.6678936945[/C][C]58186.8784027714[/C][C]108200.457384618[/C][/ROW]
[ROW][C]138[/C][C]73493.6045085956[/C][C]46574.8500283783[/C][C]100412.358988813[/C][/ROW]
[ROW][C]139[/C][C]63203.8144075576[/C][C]34497.9449852659[/C][C]91909.6838298492[/C][/ROW]
[ROW][C]140[/C][C]78621.0380878178[/C][C]48230.8687499474[/C][C]109011.207425688[/C][/ROW]
[ROW][C]141[/C][C]86262.9894207008[/C][C]54275.0888296753[/C][C]118250.890011726[/C][/ROW]
[ROW][C]142[/C][C]78883.642268976[/C][C]45372.1935242394[/C][C]112395.091013713[/C][/ROW]
[ROW][C]143[/C][C]65612.282951652[/C][C]30641.7709050069[/C][C]100582.794998297[/C][/ROW]
[ROW][C]144[/C][C]80384.3963127775[/C][C]44011.5436078764[/C][C]116757.249017679[/C][/ROW]
[ROW][C]145[/C][C]90741.4942934366[/C][C]53016.6963164359[/C][C]128466.292270437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76900&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76900&T=3

As an alternative you can also use a QR Code:  

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

Extrapolation Forecasts of Exponential Smoothing
tForecast95% Lower Bound95% Upper Bound
13477597.877802282259479.76865782895715.9869467364
13557963.741290255437295.822408176678631.6601723341
13674155.486310694851216.711479630797094.2611417589
13783193.667893694558186.8784027714108200.457384618
13873493.604508595646574.8500283783100412.358988813
13963203.814407557634497.944985265991909.6838298492
14078621.038087817848230.8687499474109011.207425688
14186262.989420700854275.0888296753118250.890011726
14278883.64226897645372.1935242394112395.091013713
14365612.28295165230641.7709050069100582.794998297
14480384.396312777544011.5436078764116757.249017679
14590741.494293436653016.6963164359128466.292270437



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