exponential smoothing

*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: Thu, 09 Dec 2010 19:08:42 +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/09/t12919215894fd709vdf0p7p78.htm/, Retrieved Thu, 09 Dec 2010 20:06:30 +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/09/t12919215894fd709vdf0p7p78.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 «

10 10 10 10 10 9.94 10.06 10.06 10.06 10.06 10.06 10.06 10.06 10.06 10.06 10.06 10.06 10.06 10.06 10.06 9.94 9.94 9.94 9.94 9.94 9.94 10.06 10.06 9.94 10.06 10.06 10.06 10.18 10.28 10.28 10.18 10.28 10.28 10.28 10.18 10.28 10.28 10.18 10.18 10.18 10.28 10.28 10.18 10.18 10.18 10.18 10.18 10.18 10.28 10.28 10.28 10.18 10.18 10.18 10.28 10.18 10.18 10.28 10.18 10.18 10.18 10.28 10.28 10.28 10.28 10.28 10.28 10.18 10.18 10.18 10.18 10.18 10.18 10.18 10.18 10.18 10.28 10.28 10.28 10.28 10.28 10.28 10.28 10.28 10.18 10.28 10.28 10.28 10.28 10.18 10.28 10.28 10.28 10.18 10.18 10.28 10.28 10.28 10.28 10.28 10.28 10.28 10.18 10.28 10.28 10.28 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.34 10.34 10.34 10.42 10.42 10.42 10.42 10.34 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.42 10.34 10.34 10.34 10.3 etc...

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Estimated Parameters of Exponential Smoothing Parameter Value alpha 0.665911537998786 beta FALSE gamma FALSE Interpolation Forecasts of Exponential Smoothing t Observed Fitted Residuals 2 10 10 0 3 10 10 0 4 10 10 0 5 10 10 0 6 9.94 10 -0.0600000000000005 7 10.06 9.96004530772007 0.099954692279928 8 10.06 10.0266062905864 0.0333937094136054 9 10.06 10.0488435469815 0.0111564530185078 10 10.06 10.0562727577697 0.00372724223034204 11 10.06 10.0587547713758 0.00124522862424037 12 10.06 10.0595839834841 0.000416016515911721 13 10.06 10.059861013682 0.000138986317967493 14 10.06 10.0599535662748 4.6433725209738e-05 15 10.06 10.0599844870282 1.55129718404368e-05 16 10.06 10.0599948172951 5.1827049034614e-06 17 10.06 10.0599982685181 1.73148191073835e-06 18 10.06 10.0599994215319 5.78468128509257e-07 19 10.06 10.0599998067405 1.93259527492273e-07 20 10.06 10.0599999354342 6.45657785014464e-08 21 9.94 10.0599999784293 -0.11999997842932 22 9.94 9.98009060823363 -0.0400906082336299 23 9.94 9.95339380964547 -0.013393809645466 24 9.94 9.94447471726479 -0.00447471726478987 25 9.94 9.94149495140888 -0.00149495140888334 26 9.94 9.94049944601696 -0.000499446016959837 27 10.06 9.94016685915166 0.119833140848343 28 10.06 10.0199651302772 0.0400348697227972 29 9.94 10.0466248119479 -0.106624811947892 30 10.06 9.97562211943484 0.0843778805651603 31 10.06 10.0318103236551 0.0281896763449367 32 10.06 10.0505821543856 0.00941784561439185 33 10.18 10.0568536064433 0.123146393556675 34 10.28 10.1388582107757 0.141141789224346 35 10.28 10.2328461567139 0.0471538432860612 36 10.18 10.2642464450191 -0.0842464450191134 37 10.28 10.2081457652455 0.071854234754495 38 10.28 10.2559943292226 0.0240056707774041 39 10.28 10.2719799823707 0.00802001762933102 40 10.18 10.277320604645 -0.0973206046449935 41 10.28 10.2125136911269 0.067486308873125 42 10.28 10.2574536028624 0.0225463971375603 43 10.18 10.2724675088566 -0.0924675088566431 44 10.18 10.210892327819 -0.0308923278190001 45 10.18 10.1903207702887 -0.0103207702886863 46 10.28 10.1834480502724 0.0965519497275853 47 10.28 10.2477431076123 0.0322568923877071 48 10.18 10.2692233444333 -0.0892233444332522 49 10.18 10.2098084899163 -0.0298084899163094 50 10.18 10.1899586725507 -0.00995867255071836 51 10.18 10.183327077596 -0.00332707759604389 52 10.18 10.181111538237 -0.00111153823702104 53 10.18 10.1803713521001 -0.000371352100062339 54 10.28 10.180124064452 0.0998759355480292 55 10.28 10.2466326023018 0.0333673976981732 56 10.28 10.268852337422 0.0111476625779652 57 10.18 10.2762756945544 -0.0962756945544196 58 10.18 10.2121645987218 -0.0321645987217849 59 10.18 10.1907458213178 -0.0107458213178475 60 10.28 10.183590054917 0.0964099450829803 61 10.18 10.2477905497256 -0.067790549725606 62 10.18 10.202648040496 -0.0226480404960441 63 10.28 10.1875664490167 0.0924335509833352 64 10.18 10.2491190171147 -0.0691190171146658 65 10.18 10.2030918661229 -0.0230918661228738 66 10.18 10.1877147260377 -0.00771472603772949 67 10.28 10.1825774009567 0.097422599043293 68 10.28 10.2474522337215 0.032547766278535 69 10.28 10.2691261668224 0.0108738331775715 70 10.28 10.2763671777976 0.00363282220235206 71 10.28 10.2787863160177 0.00121368398230715 72 10.28 10.279594522185 0.000405477815004218 73 10.18 10.2798645345404 -0.0998645345404086 74 10.18 10.2133635887531 -0.0333635887530725 75 10.18 10.1911463900534 -0.011146390053355 76 10.18 10.1837238803098 -0.00372388030979032 77 10.18 10.1812441054454 -0.0012441054453749 78 10.18 10.1804156412748 -0.000415641274813083 79 10.18 10.1801388609542 -0.000138860954246312 80 10.18 10.1800463918426 -4.63918426358845e-05 81 10.18 10.1800154989794 -1.54989793550442e-05 82 10.28 10.1800051780302 0.0999948219698243 83 10.28 10.24659288372 0.0334071162799834 84 10.28 10.2688390679021 0.0111609320978747 85 10.28 10.2762712613609 0.00372873863907941 86 10.28 10.2787542714429 0.00124572855713367 87 10.28 10.2795838164623 0.000416183537723214 88 10.28 10.279860957882 0.000139042118028954 89 10.28 10.2799535476326 4.64523673660011e-05 90 10.18 10.2799844808 -0.09998448080003 91 10.28 10.2134036614145 0.0665963385855282 92 10.28 10.257750931667 0.0222490683329504 93 10.28 10.2725668429797 0.00743315702031566 94 10.28 10.2775166680033 0.00248333199673034 95 10.18 10.2791703474326 -0.0991703474325742 96 10.28 10.2131316688499 0.0668683311501255 97 10.28 10.2576600620895 0.0223399379105338 98 10.28 10.2725364845023 0.00746351549773294 99 10.18 10.2775065255862 -0.0975065255862404 100 10.18 10.2125758051682 -0.0325758051681895 101 10.28 10.1908832006471 0.089116799352908 102 10.28 10.2502271055657 0.0297728944342843 103 10.28 10.2700532194891 0.00994678051087483 104 10.28 10.2766768953973 0.00332310460274243 105 10.28 10.2788897890942 0.0011102109058001 106 10.28 10.279629091346 0.000370908654016233 107 10.28 10.2798760836982 0.000123916301763671 108 10.18 10.2799586009933 -0.099958600993327 109 10.28 10.2133950152697 0.0666049847303452 110 10.28 10.2577480430898 0.0222519569101749 111 10.28 10.2725658779394 0.00743412206063709 112 10.42 10.2775163455944 0.142483654405568 113 10.42 10.3723978550393 0.0476021449606687 114 10.42 10.4040966726021 0.0159033273978686 115 10.42 10.4146868818089 0.00531311819105618 116 10.42 10.4182249485151 0.00177505148488066 117 10.42 10.4194069757794 0.000593024220556515 118 10.42 10.4198018774502 0.000198122549775803 119 10.42 10.4199338095421 6.61904579430939e-05 120 10.42 10.4199778865317 2.21134682938384e-05 121 10.42 10.4199926121454 7.38785461251723e-06 122 10.34 10.419997531803 -0.0799975318030146 123 10.34 10.366726252364 -0.0267262523639626 124 10.34 10.3489289325473 -0.00892893254733274 125 10.42 10.3429830533421 0.0770169466579489 126 10.42 10.394269526743 0.0257304732569832 127 10.42 10.411403745763 0.00859625423698951 128 10.42 10.417128090643 0.00287190935700643 129 10.34 10.4190405282199 -0.0790405282199114 130 10.42 10.3664065285088 0.0535934714912472 131 10.42 10.4020950395362 0.0179049604638166 132 10.42 10.4140181592965 0.00598184070354968 133 10.42 10.4180015360394 0.00199846396058589 134 10.42 10.419332336249 0.000667663750956393 135 10.42 10.4197769412443 0.000223058755690175 136 10.42 10.4199254786434 7.45213566251124e-05 137 10.42 10.4199751032746 2.4896725420831e-05 138 10.42 10.4199916822913 8.317708704908e-06 139 10.42 10.4199972211495 2.77885050792293e-06 140 10.42 10.4199990716181 9.28381892251195e-07 141 10.42 10.4199996898383 3.10161677674614e-07 142 10.42 10.4199998963786 1.03621438540813e-07 143 10.42 10.4199999653813 3.4618727440261e-08 144 10.42 10.4199999884343 1.15657172727879e-08 145 10.42 10.419999996136 3.86397225327073e-09 146 10.42 10.4199999987091 1.29090871325843e-09 147 10.34 10.4199999995687 -0.079999999568722 148 10.34 10.366727076816 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170 10.42 10.4139431441823 0.0060568558176719 171 10.42 10.4179764743553 0.00202352564468988 172 10.42 10.4193239634295 0.000676036570453675 173 10.42 10.4197741439819 0.000225856018079895 174 10.42 10.4199245441103 7.54558897142488e-05 175 10.42 10.4199747910579 2.52089421444168e-05 176 10.42 10.4199915779833 8.42201670891995e-06 177 10.42 10.4199971863014 2.81369860921643e-06 178 10.42 10.4199990599758 9.40024241558035e-07 179 10.42 10.4199996859487 3.14051252559011e-07 180 10.42 10.4199998950791 1.04920900412253e-07 181 10.34 10.4199999649471 -0.0799999649471381 182 10.34 10.3667270652493 -0.0267270652493412 183 10.34 10.348929204123 -0.00892920412295872 184 10.42 10.3429831440723 0.0770168559276652 185 10.42 10.394269557055 0.0257304429450436 186 10.42 10.4114037558899 0.0085962441101195 187 10.42 10.4171280940263 0.00287190597373765 188 10.42 10.4190405293502 0.000959470649778638 189 10.42 10.4196794519263 0.000320548073720062 190 10.42 10.4198929085871 0.000107091412946403 191 10.42 10.4199642219946 3.57780054454082e-05 192 10.42 10.4199880469812 1.19530188129602e-05 193 10.42 10.4199960066343 3.9933656719171e-06 194 10.42 10.4199986658626 1.33413739611399e-06 195 10.42 10.4199995542801 4.45719910757703e-07 196 10.42 10.4199998510901 1.48909879982284e-07 197 10.42 10.4199999502509 4.97490724171712e-08 198 10.34 10.4199999833794 -0.079999983379409 199 10.34 10.3667270714073 -0.0267270714073486 200 10.42 10.3489292061803 0.0710707938197217 201 10.42 10.3962560677996 0.0237439322004356 202 10.55 10.4120674262093 0.137932573790705 203 10.55 10.5039183185624 0.0460816814376059 204 10.55 10.5346046419221 0.0153953580779191 205 10.55 10.5448565884978 0.00514341150221043 206 10.55 10.5482816455618 0.00171835443821244 207 10.55 10.5494259176086 0.000574082391436193 208 10.55 10.5498082056968 0.000191794303216142 209 10.55 10.5499359237362 6.40762637829084e-05 210 10.55 10.5499785928596 2.14071404176508e-05 211 10.55 10.5499928481214 7.15187861821676e-06 212 10.49 10.5499976106399 -0.0599976106398721 213 10.49 10.5100445094624 -0.0200445094624229 214 10.55 10.4966966393379 0.0533033606621309 215 10.55 10.5321919622169 0.0178080377831069 216 10.55 10.5440505400458 0.00594945995421803 217 10.55 10.5480123540742 0.00198764592584233 218 10.55 10.5493359504296 0.000664049570367808 219 10.55 10.5497781487003 0.00022185129965635 220 10.49 10.5499258820405 -0.0599258820405062 221 10.55 10.510020545765 0.0399794542350218 222 10.55 10.536643325623 0.013356674377027 223 10.55 10.5455376891999 0.00446231080007209 224 10.55 10.5485091934478 0.00149080655216771 225 10.55 10.5495019387318 0.000498061268155325 226 10.55 10.5498336034769 0.000166396523059831 227 10.49 10.5499444088415 -0.0599444088415293 228 10.49 10.5100267353554 -0.0200267353554384 229 10.55 10.4966907012138 0.0533092987861963 230 10.55 10.5321899783582 0.0178100216418429 231 10.55 10.5440498772615 0.00595012273853079 232 10.49 10.5480121326456 -0.0580121326455671 233 10.55 10.509381184173 0.0406188158270329 234 10.55 10.536429722292 0.0135702777079647 235 10.55 10.5454663267916 0.0045336732083836 236 10.55 10.5484853520906 0.00151464790940459 237 10.55 10.5494939736095 0.00050602639052677 238 10.55 10.5498309424215 0.000169057578542464 239 10.55 10.5499435198136 5.6480186405139e-05 240 10.55 10.5499811306214 1.88693786089544e-05 241 10.55 10.5499936959583 6.30404167800691e-06 242 10.49 10.5499978938924 -0.0599978938924117 243 10.49 10.5100446040938 -0.0200446040938278 244 10.49 10.4966966709531 -0.00669667095312931 245 10.55 10.4922372804993 0.057762719500742 246 10.55 10.530702141881 0.0192978581190104 247 10.55 10.5435528082611 0.00644719173889818 248 10.49 10.5478460676277 -0.0578460676277253 249 10.55 10.5093257037666 0.0406742962334352 250 10.55 10.5364111869284 0.0135888130716104 251 10.55 10.5454601343405 0.00453986565951681 252 10.63 10.5484832832641 0.0815167167358801 253 10.63 10.6027662054783 0.0272337945216794 254 10.63 10.6209015034738 0.00909849652620487 255 10.63 10.626960297289 0.00303970271096254 256 10.63 10.6289844703964 0.00101552960364693 257 10.57 10.6296607232766 -0.0596607232766022 258 10.63 10.5899319592814 0.0400680407186407 259 10.63 10.6166137299009 0.0133862700990921 260 10.63 10.6255278016107 0.00447219838933854 261 10.63 10.6285058901183 0.00149410988165855 262 10.57 10.6295008351276 -0.0595008351275759 263 10.57 10.5898785424956 -0.0198785424955599 264 10.57 10.5766411916892 -0.00664119168916777 265 10.63 10.5722187455173 0.0577812544827108 266 10.63 10.6106959495574 0.0193040504426296 267 10.63 10.6235507394772 0.00644926052277128 268 10.63 10.6278453764709 0.00215462352909768 269 10.57 10.629280165139 -0.059280165138972 270 10.63 10.5898048191985 0.0401951808015433 271 10.63 10.6165712538662 0.0134287461338491 272 10.57 10.6255136108575 -0.0555136108575383 273 10.63 10.5885464568715 0.0414535431284708 274 10.63 10.6161508495317 0.0138491504682925 275 10.63 10.62537315862 0.00462684137997549 276 10.63 10.6284542256794 0.0015457743205598 277 10.57 10.6294835746346 -0.059483574634644 278 10.63 10.589872775964 0.0401272240359773 279 10.6 10.6165939574374 -0.0165939574374434 280 10.7 10.6055438497188 0.0944561502812107 281 10.7 10.668443290026 0.0315567099740051 282 10.7 10.689457267299 0.0105427327010332 283 10.7 10.6964777946466 0.00352220535337722 284 10.7 10.6988232718306 0.00117672816936221 285 10.7 10.6996068686957 0.00039313130429619 286 10.7 10.6998686593672 0.000131340632817611 287 10.7 10.69995612061 4.3879390016599e-05 288 10.7 10.6999853404021 1.46595979249042e-05 289 10.7 10.6999951023975 4.89760252442295e-06 290 10.6 10.6999983637675 -0.0999983637675044 291 10.6 10.6334082995537 -0.0334082995537237 292 10.6 10.611161327416 -0.0111613274159801 293 10.6 10.6037288707103 -0.00372887071029737 294 10.7 10.6012457726806 0.0987542273193949 295 10.7 10.6670073520787 0.0329926479212546 296 10.6 10.6889775369986 -0.088977536998641 297 10.7 10.6297263684885 0.0702736315114674 298 10.7 10.6765223905291 0.0234776094709055 299 10.7 10.6921564015604 0.00784359843959948 300 10.7 10.6973795442608 0.00262045573924041 301 10.7 10.6991245359723 0.000875464027664918 302 10.7 10.6997075175695 0.00029248243053992 303 10.7 10.6999022849946 9.77150053813602e-05 304 10.7 10.6999673545441 3.26454558621236e-05 305 10.7 10.6999890935299 1.09064701394601e-05 306 10.7 10.6999963562742 3.6437258348343e-06 307 10.7 10.6999987826732 1.2173267602833e-06 308 10.7 10.6999995933052 4.06694825372256e-07 309 10.7 10.699999864128 1.35872047835051e-07 310 10.7 10.6999999546067 4.53932837984894e-08 311 10.8 10.6999999848346 0.100000015165374 312 10.8 10.7665911487333 0.0334088512666977 313 10.8 10.7888384882631 0.011161511736919 314 10.8 10.7962710677102 0.00372893228979621 315 10.7 10.7987542067464 -0.0987542067463973 316 10.7 10.7329926410481 -0.0329926410480539 317 10.7 10.7110224607051 -0.011022460705103 318 10.85 10.7036824769444 0.146317523055563 319 10.75 10.8011170037585 -0.0511170037585398 320 10.75 10.7670776011678 -0.0170776011678004 321 10.75 10.7557054295088 -0.00570542950882036 322 10.75 10.7519061181697 -0.00190611816965891 323 10.75 10.7506368120877 -0.000636812087693173 324 10.75 10.750212751571 -0.000212751570961345 325 10.75 10.7500710778451 -7.10778451313843e-05 326 10.75 10.750023746288 -2.37462879617567e-05 327 10.75 10.7500079333608 -7.9333608233867e-06 328 10.75 10.7500026504443 -2.65044431557726e-06 329 10.75 10.7500008854829 -8.85482865697895e-07 330 10.85 10.7500002958296 0.0999997041703917 331 10.85 10.8165912526331 0.0334087473668632 332 10.85 10.8388385229748 0.0111614770251816 333 10.85 10.846271079307 0.00372892069300512 334 10.85 10.8487542106207 0.00124578937925079 335 10.85 10.8495837961423 0.000416203857691144 336 10.85 10.8498609510933 0.000139048906694583 337 10.85 10.8499535453646 4.64546353811102e-05 338 10.85 10.8499844800423 1.55199576870046e-05 339 10.85 10.8499948149612 5.18503879476384e-06 340 10.75 10.8499982677384 -0.0999982677383624 341 10.75 10.7834082674715 -0.0334082674714953 342 10.85 10.7611613166977 0.0888386833023223 343 10.85 10.8203200209293 0.0296799790706856 344 10.85 10.84008426144 0.00991573855995398 345 10.85 10.8466872661549 0.00331273384510133 346 10.75 10.8488932538447 -0.09889325384467 347 10.75 10.7830390950793 -0.0330390950792623 348 10.75 10.7610379804609 -0.0110379804609426 349 10.75 10.7536876619158 -0.00368766191579617 350 10.75 10.7512320052978 -0.00123200529782963 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 351 10.7504115987551 10.6652816507396 10.8355415467706 352 10.7504115987551 10.6481337789363 10.852689418574 353 10.7504115987551 10.6334740222253 10.8673491752849 354 10.7504115987551 10.6204576023471 10.8803655951632 355 10.7504115987551 10.6086311855797 10.8921920119306

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/09/t12919215894fd709vdf0p7p78/1hoys1291921716.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919215894fd709vdf0p7p78/1hoys1291921716.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919215894fd709vdf0p7p78/2hoys1291921716.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919215894fd709vdf0p7p78/2hoys1291921716.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919215894fd709vdf0p7p78/3hoys1291921716.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919215894fd709vdf0p7p78/3hoys1291921716.ps (open in new window)

Parameters (Session):

par1 = 12 ; par2 = Single ; par3 = additive ;

Parameters (R input):

par1 = 12 ; par2 = Single ; par3 = additive ;

R code (references can be found in the software module):

par1 <- 5 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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