| paper ES triple | | *The author of this computation has been verified* | | R Software Module: /rwasp_exponentialsmoothing.wasp (opens new window with default values) | | Title produced by software: Exponential Smoothing | | Date of computation: Sun, 26 Dec 2010 22:36:18 +0000 | | | | Cite this page as follows: | | Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t1293402867f7qw1qksqv23ape.htm/, Retrieved Sun, 26 Dec 2010 23:34:31 +0100 | | | | BibTeX entries for LaTeX users: | @Manual{KEY,
author = {{YOUR NAME}},
publisher = {Office for Research Development and Education},
title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t1293402867f7qw1qksqv23ape.htm/},
year = {2010},
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2010},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org},
}
| | | | Original text written by user: | | | | | IsPrivate? | | No (this computation is public) | | | | User-defined keywords: | | | | | Dataseries X: | | » Textbox « » Textfile « » CSV « | | 5.2
7.9
8.7
8.9
15.3
15.4
18.1
19.7
13
12.6
6.2
3.5
3.4
0
9.5
8.9
10.4
13.2
18.9
19
16.3
10.6
5.8
3.6
2.6
5
7.3
9.2
15.7
16.8
18.4
18.1
14.6
7.8
7.6
3.8
5.6
2.2
6.8
11.8
14.9
16.7
16.7
15.9
13.6
9.2
2.8
2.5
4.8
2.8
7.8
9
12.9
16.4
21.8
17.8
13.5
10
10.4
5.5
4
6.8
5.7
9.1
13.6
15
20.9
20.4
14
13.7
7.1
0.8
2.1
1.3
3.9
10.7
11.1
16.4
17.1
17.3
12.9
10.9
5.3
0.7
-0.2
6.5
8.6
8.5
13.3
16.2
17.5
21.2
14.8
10.3
7.3
5.1
4.4
6.2
7.7
9.3
15.6
16.3
16.6
17.4
15.3
9.7
3.7
4.6
5.4
3.1
7.9
10.1
15
15.6
19.7
18.1
17.7
10.7
6.2
4.2
4
5.9
7.1
10.5
15.1
16.8
15.3
18.4
16.1
11.3
7.9
5.6
3.4
4.8
6.5
8.5
15.1
15.7
18.7
19.2
12.9
14.4
6.2
3.3
4.6
7.2
7.8
9.9
13.6
17.1
17.8
18.6
14.7
10.5
8.6
4.4
2.3
2.8
8.8
10.7
13.9
19.3
19.5
20.4
15.3
7.9
8.3
4.5
3.2
5
6.6
11.1
12.8
16.3
17.4
18.9
15.8
11.7
6.4
2.9
4.7
2.4
7.2
10.7
13.4
18.5
18.3
16.8
16.6
14. etc... | | | | Output produced by software: |
| Estimated Parameters of Exponential Smoothing | | Parameter | Value | | alpha | 0.109108961234570 | | beta | 0.0227447508615387 | | gamma | 0.245515224436701 |
| Interpolation Forecasts of Exponential Smoothing | | t | Observed | Fitted | Residuals | | 13 | 3.4 | 4.58159722222222 | -1.18159722222222 | | 14 | 0 | 0.9563708465851 | -0.9563708465851 | | 15 | 9.5 | 10.1491786365861 | -0.649178636586125 | | 16 | 8.9 | 9.32805947803669 | -0.428059478036692 | | 17 | 10.4 | 10.7841707714203 | -0.384170771420342 | | 18 | 13.2 | 13.4566173362391 | -0.256617336239108 | | 19 | 18.9 | 17.0590109545510 | 1.84098904544905 | | 20 | 19 | 19.1698409280162 | -0.169840928016178 | | 21 | 16.3 | 12.6525165117421 | 3.64748348825789 | | 22 | 10.6 | 12.5315815303507 | -1.93158153035073 | | 23 | 5.8 | 6.03462703895405 | -0.234627038954051 | | 24 | 3.6 | 3.51390989241083 | 0.0860901075891731 | | 25 | 2.6 | 3.13245192076494 | -0.532451920764941 | | 26 | 5 | -0.372605145762412 | 5.37260514576241 | | 27 | 7.3 | 9.59372688888614 | -2.29372688888614 | | 28 | 9.2 | 8.6532412967104 | 0.546758703289598 | | 29 | 15.7 | 10.2394365300667 | 5.46056346993327 | | 30 | 16.8 | 13.6061228993839 | 3.19387710061615 | | 31 | 18.4 | 18.0809906329937 | 0.319009367006259 | | 32 | 18.1 | 19.6193489601023 | -1.51934896010233 | | 33 | 14.6 | 13.8197986361009 | 0.780201363899069 | | 34 | 7.8 | 12.1886723679915 | -4.38867236799146 | | 35 | 7.6 | 5.81164867968252 | 1.78835132031748 | | 36 | 3.8 | 3.6036776722818 | 0.196322327718201 | | 37 | 5.6 | 3.12110001805014 | 2.47889998194986 | | 38 | 2.2 | 1.26582386713320 | 0.934176132866802 | | 39 | 6.8 | 9.08965082938107 | -2.28965082938107 | | 40 | 11.8 | 8.78951538508724 | 3.01048461491276 | | 41 | 14.9 | 11.7440362599361 | 3.15596374006389 | | 42 | 16.7 | 14.3824924545376 | 2.31750754546238 | | 43 | 16.7 | 18.1497620004817 | -1.44976200048166 | | 44 | 15.9 | 19.1054765405788 | -3.20547654057882 | | 45 | 13.6 | 13.6331888605233 | -0.033188860523266 | | 46 | 9.2 | 10.7889818193017 | -1.58898181930174 | | 47 | 2.8 | 6.08170237718291 | -3.28170237718291 | | 48 | 2.5 | 2.97292984617687 | -0.472929846176875 | | 49 | 4.8 | 2.91553899948972 | 1.88446100051028 | | 50 | 2.8 | 0.655001877556304 | 2.14499812244370 | | 51 | 7.8 | 7.90627714916906 | -0.106277149169064 | | 52 | 9 | 9.00954589777025 | -0.0095458977702485 | | 53 | 12.9 | 11.6647741340474 | 1.23522586595262 | | 54 | 16.4 | 13.9039002280447 | 2.49609977195529 | | 55 | 21.8 | 16.8607271288579 | 4.93927287114213 | | 56 | 17.8 | 18.1394513793061 | -0.339451379306059 | | 57 | 13.5 | 13.6907826543287 | -0.190782654328711 | | 58 | 10 | 10.5057391758765 | -0.505739175876473 | | 59 | 10.4 | 5.56574810113977 | 4.83425189886023 | | 60 | 5.5 | 3.99633629548552 | 1.50366370451448 | | 61 | 4 | 4.71462262518618 | -0.714622625186177 | | 62 | 6.8 | 2.26542608247820 | 4.5345739175218 | | 63 | 5.7 | 9.3288775096819 | -3.6288775096819 | | 64 | 9.1 | 10.1040822905504 | -1.00408229055044 | | 65 | 13.6 | 12.9557207120591 | 0.64427928794092 | | 66 | 15 | 15.4373477088581 | -0.437347708858141 | | 67 | 20.9 | 18.6324089904038 | 2.26759100959620 | | 68 | 20.4 | 18.4823085395892 | 1.91769146041078 | | 69 | 14 | 14.3353145929054 | -0.335314592905434 | | 70 | 13.7 | 11.0881354693262 | 2.61186453067384 | | 71 | 7.1 | 7.68656529625276 | -0.586565296252761 | | 72 | 0.8 | 4.8140110442704 | -4.01401104427041 | | 73 | 2.1 | 4.44818472762206 | -2.34818472762206 | | 74 | 1.3 | 2.96795556133288 | -1.66795556133288 | | 75 | 3.9 | 7.55275408011954 | -3.65275408011954 | | 76 | 10.7 | 8.88307730430632 | 1.81692269569368 | | 77 | 11.1 | 12.3936642981000 | -1.29366429809998 | | 78 | 16.4 | 14.4130635557960 | 1.98693644420403 | | 79 | 17.1 | 18.4560968208236 | -1.35609682082355 | | 80 | 17.3 | 17.8169110987015 | -0.516911098701453 | | 81 | 12.9 | 12.8882685204609 | 0.0117314795390868 | | 82 | 10.9 | 10.3012273396169 | 0.598772660383075 | | 83 | 5.3 | 5.95307457364835 | -0.653074573648346 | | 84 | 0.7 | 2.29607024315198 | -1.59607024315198 | | 85 | -0.2 | 2.53690782769973 | -2.73690782769973 | | 86 | 6.5 | 1.14056868601959 | 5.35943131398041 | | 87 | 8.6 | 6.05294649750936 | 2.54705350249064 | | 88 | 8.5 | 9.26643584146882 | -0.766435841468818 | | 89 | 13.3 | 11.8187177737818 | 1.48128222621821 | | 90 | 16.2 | 14.8692648548251 | 1.33073514517488 | | 91 | 17.5 | 18.1186788314571 | -0.618678831457103 | | 92 | 21.2 | 17.7545257145555 | 3.44547428544447 | | 93 | 14.8 | 13.3946975944784 | 1.40530240552158 | | 94 | 10.3 | 11.1124219279581 | -0.812421927958102 | | 95 | 7.3 | 6.35729240232238 | 0.942707597677617 | | 96 | 5.1 | 2.69291387841357 | 2.40708612158643 | | 97 | 4.4 | 3.15570439412523 | 1.24429560587477 | | 98 | 6.2 | 4.00922574813211 | 2.19077425186789 | | 99 | 7.7 | 7.9974523353418 | -0.297452335341797 | | 100 | 9.3 | 10.2054923938311 | -0.905492393831103 | | 101 | 15.6 | 13.2635566673641 | 2.33644333263594 | | 102 | 16.3 | 16.4059202687909 | -0.105920268790889 | | 103 | 16.6 | 19.1000664380855 | -2.50006643808548 | | 104 | 17.4 | 19.4427836630769 | -2.04278366307689 | | 105 | 15.3 | 14.0474814332894 | 1.25251856671058 | | 106 | 9.7 | 11.2726649667697 | -1.57266496676967 | | 107 | 3.7 | 6.82580038788644 | -3.12580038788644 | | 108 | 4.6 | 3.03503363562198 | 1.56496636437802 | | 109 | 5.4 | 3.14674031374425 | 2.25325968625575 | | 110 | 3.1 | 4.31500634357628 | -1.21500634357628 | | 111 | 7.9 | 7.37657357684682 | 0.523426423153176 | | 112 | 10.1 | 9.53240597979732 | 0.567594020202675 | | 113 | 15 | 13.4551748513742 | 1.54482514862581 | | 114 | 15.6 | 15.9698674403621 | -0.369867440362063 | | 115 | 19.7 | 18.1038076765255 | 1.59619232347446 | | 116 | 18.1 | 18.9959074063675 | -0.895907406367478 | | 117 | 17.7 | 14.4517821430725 | 3.24821785692748 | | 118 | 10.7 | 11.2869920746194 | -0.586992074619451 | | 119 | 6.2 | 6.62062872992045 | -0.420628729920449 | | 120 | 4.2 | 4.17040118896349 | 0.0295988110365091 | | 121 | 4 | 4.28070573554931 | -0.280705735549313 | | 122 | 5.9 | 4.42317216717721 | 1.47682783282279 | | 123 | 7.1 | 8.17464881393191 | -1.07464881393191 | | 124 | 10.5 | 10.1777751286119 | 0.322224871388078 | | 125 | 15.1 | 14.2989078255951 | 0.801092174404939 | | 126 | 16.8 | 16.3231998861123 | 0.476800113887702 | | 127 | 15.3 | 18.9911942169848 | -3.69119421698484 | | 128 | 18.4 | 18.7598250473786 | -0.359825047378578 | | 129 | 16.1 | 15.1804775625236 | 0.919522437476431 | | 130 | 11.3 | 10.9168147743147 | 0.383185225685343 | | 131 | 7.9 | 6.38917630382262 | 1.51082369617738 | | 132 | 5.6 | 4.24943942127937 | 1.35056057872063 | | 133 | 3.4 | 4.44055365736714 | -1.04055365736714 | | 134 | 4.8 | 4.88720178877641 | -0.087201788776408 | | 135 | 6.5 | 7.9087370942207 | -1.40873709422071 | | 136 | 8.5 | 10.1789026577129 | -1.67890265771287 | | 137 | 15.1 | 14.1794265026914 | 0.920573497308599 | | 138 | 15.7 | 16.1391107620271 | -0.439110762027131 | | 139 | 18.7 | 17.7865310777461 | 0.913468922253859 | | 140 | 19.2 | 18.7886748099040 | 0.41132519009604 | | 141 | 12.9 | 15.5776501559411 | -2.67765015594108 | | 142 | 14.4 | 10.7996202512013 | 3.60037974879872 | | 143 | 6.2 | 6.8730640827102 | -0.673064082710194 | | 144 | 3.3 | 4.45798410997156 | -1.15798410997156 | | 145 | 4.6 | 3.84415886072835 | 0.75584113927165 | | 146 | 7.2 | 4.69155910200919 | 2.50844089799081 | | 147 | 7.8 | 7.70991315728871 | 0.090086842711286 | | 148 | 9.9 | 10.0909075607598 | -0.190907560759751 | | 149 | 13.6 | 14.8324404377178 | -1.23244043771784 | | 150 | 17.1 | 16.2645474549773 | 0.835452545022658 | | 151 | 17.8 | 18.3547791543433 | -0.554779154343333 | | 152 | 18.6 | 19.0911466125117 | -0.491146612511653 | | 153 | 14.7 | 15.1080265924714 | -0.408026592471405 | | 154 | 10.5 | 11.9584576696969 | -1.45845766969692 | | 155 | 8.6 | 6.54031192102597 | 2.05968807897403 | | 156 | 4.4 | 4.3192099979424 | 0.0807900020575989 | | 157 | 2.3 | 4.26410172632599 | -1.96410172632599 | | 158 | 2.8 | 5.19627306668732 | -2.39627306668732 | | 159 | 8.8 | 7.13654678060156 | 1.66345321939844 | | 160 | 10.7 | 9.61768094813946 | 1.08231905186054 | | 161 | 13.9 | 14.2634148249320 | -0.363414824931963 | | 162 | 19.3 | 16.2378938778460 | 3.06210612215396 | | 163 | 19.5 | 18.2677664574395 | 1.23223354256048 | | 164 | 20.4 | 19.2182406065344 | 1.18175939346555 | | 165 | 15.3 | 15.4451912361175 | -0.14519123611748 | | 166 | 7.9 | 12.1045553000659 | -4.20455530006594 | | 167 | 8.3 | 7.15949865034186 | 1.14050134965814 | | 168 | 4.5 | 4.40618344426226 | 0.0938165557377433 | | 169 | 3.2 | 3.90617242595118 | -0.706172425951179 | | 170 | 5 | 4.88513881074623 | 0.114861189253766 | | 171 | 6.6 | 7.99767593134607 | -1.39767593134607 | | 172 | 11.1 | 10.0204100250225 | 1.07958997497754 | | 173 | 12.8 | 14.3523243389097 | -1.55232433890966 | | 174 | 16.3 | 16.9460869291732 | -0.646086929173233 | | 175 | 17.4 | 18.1616660696579 | -0.76166606965792 | | 176 | 18.9 | 18.8691464208698 | 0.0308535791302482 | | 177 | 15.8 | 14.6630250065956 | 1.1369749934044 | | 178 | 11.7 | 10.5603154362884 | 1.13968456371161 | | 179 | 6.4 | 7.36666002880971 | -0.966660028809713 | | 180 | 2.9 | 4.14845373224355 | -1.24845373224355 | | 181 | 4.7 | 3.31763615470415 | 1.38236384529585 | | 182 | 2.4 | 4.69987266647393 | -2.29987266647393 | | 183 | 7.2 | 7.20792459172523 | -0.00792459172523063 | | 184 | 10.7 | 9.91740469949437 | 0.782595300505626 | | 185 | 13.4 | 13.6337716204613 | -0.233771620461345 | | 186 | 18.5 | 16.5654201307873 | 1.93457986921272 | | 187 | 18.3 | 18.0394982864143 | 0.260501713585700 | | 188 | 16.8 | 19.0365941215135 | -2.23659412151354 | | 189 | 16.6 | 14.8241279089308 | 1.77587209106919 | | 190 | 14.1 | 10.7924210170246 | 3.30757898297541 | | 191 | 6.1 | 7.3806678152282 | -1.28066781522820 | | 192 | 3.5 | 4.07186580745141 | -0.571865807451407 | | 193 | 1.7 | 3.89728208659807 | -2.19728208659807 | | 194 | 2.3 | 4.08163839000467 | -1.78163839000467 | | 195 | 4.5 | 7.14692898291867 | -2.64692898291867 | | 196 | 9.3 | 9.73421227297943 | -0.434212272979424 | | 197 | 14.2 | 13.0853215975591 | 1.11467840244092 | | 198 | 17.3 | 16.6315363940932 | 0.668463605906798 | | 199 | 23 | 17.5913218461818 | 5.40867815381816 | | 200 | 16.3 | 18.6067413494982 | -2.30674134949819 | | 201 | 18.4 | 15.2668778666604 | 3.13312213333958 | | 202 | 14.2 | 11.7242739445940 | 2.47572605540603 | | 203 | 9.1 | 7.22210756028859 | 1.87789243971141 | | 204 | 5.9 | 4.42472992160946 | 1.47527007839054 | | 205 | 7.2 | 4.13482720416418 | 3.06517279583582 | | 206 | 6.8 | 5.01417979504679 | 1.78582020495321 | | 207 | 8 | 8.31820528104268 | -0.318205281042676 | | 208 | 14.3 | 11.6880897459961 | 2.61191025400392 | | 209 | 14.6 | 15.7624374296896 | -1.16243742968956 | | 210 | 17.5 | 19.0090424993913 | -1.50904249939135 | | 211 | 17.2 | 20.8090964733673 | -3.60909647336733 | | 212 | 17.2 | 19.1716835179484 | -1.97168351794840 | | 213 | 14.1 | 17.0777166125462 | -2.97771661254622 | | 214 | 10.5 | 12.7289019852212 | -2.22890198522123 | | 215 | 6.8 | 7.57530775326229 | -0.775307753262285 | | 216 | 4.1 | 4.38644513108627 | -0.286445131086266 | | 217 | 6.5 | 4.23377411650648 | 2.26622588349352 | | 218 | 6.1 | 4.72583784270226 | 1.37416215729774 | | 219 | 6.3 | 7.50343106928831 | -1.20343106928831 | | 220 | 9.3 | 11.3941201050319 | -2.09412010503187 | | 221 | 16.4 | 14.0942603654181 | 2.30573963458189 | | 222 | 16.1 | 17.6168850483410 | -1.51688504834097 | | 223 | 18 | 18.9301471159034 | -0.93014711590341 | | 224 | 17.6 | 17.9232296592855 | -0.323229659285516 | | 225 | 14 | 15.7732190024372 | -1.77321900243723 | | 226 | 10.5 | 11.7067432949641 | -1.20674329496408 | | 227 | 6.9 | 6.97228436852123 | -0.0722843685212275 | | 228 | 2.8 | 3.95846803938906 | -1.15846803938906 | | 229 | 0.7 | 4.25823885681884 | -3.55823885681884 | | 230 | 3.6 | 3.89447673748547 | -0.294476737485466 | | 231 | 6.7 | 5.89686963250033 | 0.803130367499673 | | 232 | 12.5 | 9.78730724419937 | 2.71269275580063 | | 233 | 14.4 | 13.9618449809456 | 0.438155019054411 | | 234 | 16.5 | 16.4275139352394 | 0.072486064760593 | | 235 | 18.7 | 18.0293974336996 | 0.670602566300399 | | 236 | 19.4 | 17.3207292352203 | 2.07927076477968 | | 237 | 15.8 | 15.1125065089669 | 0.687493491033143 | | 238 | 11.3 | 11.4413328157303 | -0.141332815730257 | | 239 | 9.7 | 7.07681337610198 | 2.62318662389802 | | 240 | 2.9 | 4.13176478608773 | -1.23176478608773 |
| Extrapolation Forecasts of Exponential Smoothing | | t | Forecast | 95% Lower Bound | 95% Upper Bound | | 241 | 3.91070668600518 | 0.158000081670111 | 7.66341329034024 | | 242 | 4.66994839507301 | 0.893948854527247 | 8.44594793561877 | | 243 | 6.96617294738838 | 3.16598547526647 | 10.7663604195103 | | 244 | 11.2062871495763 | 7.38101105514013 | 15.0315632440124 | | 245 | 14.6002421585177 | 10.7489718333377 | 18.4515124836976 | | 246 | 16.9499352249460 | 13.0717609068749 | 20.8281095430172 | | 247 | 18.6863669131940 | 14.7803754345676 | 22.5923583918204 | | 248 | 18.2226126243335 | 14.2878881424782 | 22.1573371061888 | | 249 | 15.4879115345081 | 11.5235362427872 | 19.4522868262291 | | 250 | 11.5635414804101 | 7.568596298205 | 15.5584866626152 | | 251 | 7.8225716577233 | 3.79613689791498 | 11.8490064175316 | | 252 | 3.74506991847836 | -0.313774068834694 | 7.80391390579142 |
| | | | Charts produced by software: |  | | http://www.freestatistics.org/blog/date/2010/Dec/26/t1293402867f7qw1qksqv23ape/159171293402974.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t1293402867f7qw1qksqv23ape/159171293402974.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t1293402867f7qw1qksqv23ape/2g1ia1293402974.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t1293402867f7qw1qksqv23ape/2g1ia1293402974.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t1293402867f7qw1qksqv23ape/3g1ia1293402974.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t1293402867f7qw1qksqv23ape/3g1ia1293402974.ps (open in new window) |
| | | | 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')
| | |
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