Classical Decomposition of Time Series by Moving Averages

*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: Fri, 10 Dec 2010 17:59:49 +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/10/t12920038838xocrl36ymwj91j.htm/, Retrieved Fri, 10 Dec 2010 18:58:07 +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/10/t12920038838xocrl36ymwj91j.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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Estimated Parameters of Exponential Smoothing Parameter Value alpha 0.665911538005217 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 9.96004530771969 0.0999546922803134 8 10.06 10.0266062905869 0.0333937094130921 9 10.06 10.0488435469819 0.0111564530181205 10 10.06 10.0562727577699 0.00372724223014131 11 10.06 10.0587547713759 0.00124522862414977 12 10.06 10.0595839834841 0.000416016515874418 13 10.06 10.0598610136820 0.000138986317953282 14 10.06 10.0599535662748 4.64337252044089e-05 15 10.06 10.0599844870282 1.55129718386604e-05 16 10.06 10.0599948172951 5.18270490168504e-06 17 10.06 10.0599982685181 1.73148190896200e-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 -0.119999978429320 22 9.94 9.98009060823286 -0.0400906082328589 23 9.94 9.95339380964495 -0.0133938096449508 24 9.94 9.94447471726453 -0.0044747172645323 25 9.94 9.94149495140877 -0.00149495140876965 26 9.94 9.9404994460169 -0.000499446016911875 27 10.06 9.94016685915164 0.119833140848362 28 10.06 10.0199651302780 0.0400348697220334 29 9.94 10.0466248119484 -0.106624811948405 30 10.06 9.97562211943433 0.0843778805656754 31 10.06 10.0318103236554 0.0281896763445655 32 10.06 10.0505821543859 0.00941784561408632 33 10.18 10.0568536064435 0.123146393556514 34 10.28 10.1388582107765 0.141141789223498 35 10.28 10.2328461567151 0.0471538432848693 36 10.18 10.2642464450198 -0.084246445019815 37 10.28 10.2081457652452 0.0718542347548006 38 10.28 10.2559943292230 0.0240056707770435 39 10.28 10.2719799823709 0.00802001762905569 40 10.18 10.2773206046451 -0.0973206046451374 41 10.28 10.2125136911263 0.0674863088737023 42 10.28 10.2574536028627 0.0225463971373205 43 10.18 10.2724675088569 -0.0924675088568687 44 10.18 10.2108923278185 -0.0308923278184814 45 10.18 10.1903207702883 -0.0103207702883150 46 10.28 10.1834480502722 0.0965519497277736 47 10.28 10.2477431076129 0.0322568923871493 48 10.18 10.2692233444336 -0.0892233444336465 49 10.18 10.2098084899159 -0.0298084899158688 50 10.18 10.1899586725504 -0.00995867255037908 51 10.18 10.1833270775959 -0.00332707759586626 52 10.18 10.1811115382369 -0.00111153823694110 53 10.18 10.1803713521000 -0.000371352100028588 54 10.28 10.1801240644520 0.0998759355480416 55 10.28 10.2466326023025 0.0333673976975355 56 10.28 10.2688523374225 0.0111476625775371 57 10.18 10.2762756945546 -0.0962756945546346 58 10.18 10.2121645987212 -0.0321645987212378 59 10.18 10.1907458213175 -0.0107458213174585 60 10.28 10.1835900549168 0.0964099450831792 61 10.18 10.2477905497262 -0.0677905497261584 62 10.18 10.2026480404958 -0.0226480404957936 63 10.28 10.1875664490164 0.0924335509835643 64 10.18 10.2491190171152 -0.0691190171151845 65 10.18 10.2030918661226 -0.0230918661226038 66 10.18 10.1877147260375 -0.00771472603749146 67 10.28 10.1825774009566 0.0974225990434228 68 10.28 10.2474522337220 0.0325477662779505 69 10.28 10.2691261668228 0.0108738331771665 70 10.28 10.2763671777979 0.00363282220214778 71 10.28 10.2787863160178 0.00121368398221655 72 10.28 10.2795945221850 0.000405477814966915 73 10.18 10.2798645345404 -0.0998645345404245 74 10.18 10.2133635887524 -0.0333635887524366 75 10.18 10.1911463900529 -0.0111463900529287 76 10.18 10.1837238803096 -0.00372388030957715 77 10.18 10.1812441054453 -0.00124410544528075 78 10.18 10.1804156412748 -0.000415641274774003 79 10.18 10.1801388609542 -0.000138860954230324 80 10.18 10.1800463918426 -4.63918426305554e-05 81 10.18 10.1800154989794 -1.54989793532678e-05 82 10.28 10.1800051780302 0.0999948219698243 83 10.28 10.2465928837207 0.0334071162793403 84 10.28 10.2688390679026 0.0111609320974448 85 10.28 10.2762712613611 0.00372873863886269 86 10.28 10.2787542714430 0.00124572855703775 87 10.28 10.2795838164623 0.000416183537682357 88 10.28 10.2798609578820 0.00013904211801119 89 10.28 10.2799535476326 4.64523673588957e-05 90 10.18 10.2799844808000 -0.0999844808000336 91 10.28 10.2134036614138 0.0665963385861694 92 10.28 10.2577509316673 0.0222490683327372 93 10.28 10.2725668429799 0.00743315702010072 94 10.28 10.2775166680034 0.00248333199661133 95 10.18 10.2791703474326 -0.0991703474326293 96 10.28 10.2131316688493 0.0668683311507436 97 10.28 10.2576600620897 0.0223399379103100 98 10.28 10.2725364845025 0.00746351549751445 99 10.18 10.2775065255864 -0.0975065255863612 100 10.18 10.2125758051676 -0.0325758051676033 101 10.28 10.1908832006467 0.0891167993533131 102 10.28 10.2502271055662 0.0297728944338456 103 10.28 10.2700532194895 0.00994678051053555 104 10.28 10.2766768953974 0.00332310460256302 105 10.28 10.2788897890943 0.00111021090571661 106 10.28 10.2796290913460 0.000370908653980706 107 10.28 10.2798760836983 0.000123916301747684 108 10.18 10.2799586009933 -0.0999586009933324 109 10.28 10.2133950152690 0.0666049847309864 110 10.28 10.2577480430900 0.0222519569099600 111 10.28 10.2725658779396 0.00743412206042215 112 10.42 10.2775163455946 0.142483654405448 113 10.42 10.3723978550403 0.047602144959713 114 10.42 10.4040966726028 0.0159033273972433 115 10.42 10.4146868818093 0.00531311819074531 116 10.42 10.4182249485153 0.00177505148474211 117 10.42 10.4194069757795 0.000593024220499672 118 10.42 10.4198018774502 0.000198122549752711 119 10.42 10.4199338095421 6.61904579342121e-05 120 10.42 10.4199778865317 2.21134682902857e-05 121 10.42 10.4199926121454 7.38785461074087e-06 122 10.34 10.4199975318030 -0.0799975318030164 123 10.34 10.3667262523634 -0.0267262523634493 124 10.34 10.3489289325470 -0.0089289325469899 125 10.42 10.3429830533419 0.0770169466581212 126 10.42 10.3942695267435 0.0257304732565462 127 10.42 10.4114037457633 0.00859625423667687 128 10.42 10.4171280906432 0.00287190935684833 129 10.34 10.4190405282200 -0.0790405282199824 130 10.42 10.3664065285083 0.0535934714917303 131 10.42 10.4020950395364 0.0179049604636337 132 10.42 10.4140181592966 0.00598184070337204 133 10.42 10.4180015360395 0.00199846396048819 134 10.42 10.4193323362491 0.000667663750911984 135 10.42 10.4197769412443 0.000223058755672412 136 10.42 10.4199254786434 7.4521356618007e-05 137 10.42 10.4199751032746 2.48967254190546e-05 138 10.42 10.4199916822913 8.31770870313164e-06 139 10.42 10.4199972211495 2.77885050792293e-06 140 10.42 10.4199990716181 9.28381892251195e-07 141 10.42 10.4199996898383 3.10161679450971e-07 142 10.42 10.4199998963786 1.03621438540813e-07 143 10.42 10.4199999653813 3.46187274402610e-08 144 10.42 10.4199999884343 1.15657172727879e-08 145 10.42 10.4199999961360 3.86397225327073e-09 146 10.42 10.4199999987091 1.29090871325843e-09 147 10.34 10.4199999995687 -0.079999999568722 148 10.34 10.3667270768155 -0.0267270768154972 149 10.34 10.3489292079869 -0.00892920798690611 150 10.34 10.3429831453632 -0.00298314536317790 151 10.42 10.3409966344463 0.0790033655537083 152 10.42 10.3936058871098 0.0263941128902498 153 10.34 10.4111820314188 -0.071182031418779 154 10.34 10.3637810953984 -0.0237810953983644 155 10.34 10.3479449895862 -0.00794498958619094 156 10.42 10.3426543293514 0.0773456706485849 157 10.42 10.3941597038511 0.0258402961489406 158 10.42 10.4113670552021 0.00863294479789012 159 10.34 10.4171158327500 -0.0771158327499872 160 10.34 10.3657635099589 -0.0257635099588907 161 10.34 10.3486072914178 -0.00860729141775352 162 10.34 10.3428755967517 -0.00287559675169824 163 10.34 10.3409607036961 -0.00096070369609258 164 10.42 10.3403209600203 0.0796790399797391 165 10.42 10.3933801520799 0.0266198479200526 166 10.42 10.4111066159499 0.00889338405014506 167 10.34 10.4170288230008 -0.0770288230007576 168 10.42 10.3657344410056 0.054265558994409 169 10.42 10.4018705028563 0.0181294971437289 170 10.42 10.4139431441825 0.00605685581748716 171 10.42 10.4179764743554 0.00202352564458863 172 10.42 10.4193239634296 0.00067603657040749 173 10.42 10.4197741439819 0.000225856018060355 174 10.42 10.4199245441103 7.5455889705367e-05 175 10.42 10.4199747910579 2.52089421408641e-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 3.14051254335368e-07 180 10.42 10.4199998950791 1.04920900412253e-07 181 10.34 10.4199999649471 -0.0799999649471381 182 10.34 10.3667270652488 -0.0267270652488261 183 10.34 10.3489292041226 -0.00892920412261411 184 10.42 10.3429831440722 0.0770168559278392 185 10.42 10.3942695570554 0.0257304429446048 186 10.42 10.4114037558902 0.00859624410980864 187 10.42 10.4171280940264 0.00287190597357778 188 10.42 10.4190405293503 0.000959470649705807 189 10.42 10.4196794519263 0.000320548073689864 190 10.42 10.4198929085871 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7.1518786164404e-06 212 10.49 10.5499976106399 -0.0599976106398739 213 10.49 10.5100445094620 -0.0200445094620374 214 10.55 10.4966966393376 0.0533033606623885 215 10.55 10.5321919622171 0.0178080377828511 216 10.55 10.5440505400460 0.0059494599540173 217 10.55 10.5480123540743 0.00198764592573752 218 10.55 10.5493359504297 0.000664049570319847 219 10.55 10.5497781487004 0.000221851299636810 220 10.49 10.5499258820405 -0.0599258820405133 221 10.55 10.5100205457646 0.0399794542354037 222 10.55 10.5366433256231 0.0133566743768974 223 10.55 10.5455376892001 0.00446231079994241 224 10.55 10.5485091934479 0.00149080655209488 225 10.55 10.5495019387319 0.000498061268121575 226 10.55 10.5498336034770 0.00016639652304562 227 10.49 10.5499444088415 -0.0599444088415346 228 10.49 10.5100267353551 -0.0200267353550547 229 10.55 10.4966907012135 0.0533092987864539 230 10.55 10.5321899783584 0.0178100216415871 231 10.55 10.5440498772617 0.00595012273833184 232 10.49 10.5480121326457 -0.0580121326456702 233 10.55 10.5093811841726 0.0406188158273704 234 10.55 10.5364297222922 0.0135702777078155 235 10.55 10.5454663267918 0.00453367320824682 236 10.55 10.5484853520907 0.00151464790932998 237 10.55 10.5494939736095 0.000506026390491243 238 10.55 10.5498309424215 0.000169057578528253 239 10.55 10.5499435198136 5.64801863980335e-05 240 10.55 10.5499811306214 1.88693786071781e-05 241 10.55 10.5499936959583 6.30404167800691e-06 242 10.49 10.5499978938924 -0.0599978938924117 243 10.49 10.5100446040934 -0.0200446040934423 244 10.49 10.4966966709529 -0.00669667095287352 245 10.55 10.4922372804991 0.0577627195008699 246 10.55 10.5307021418813 0.0192978581186818 247 10.55 10.5435528082613 0.00644719173866370 248 10.49 10.5478460676278 -0.0578460676278443 249 10.55 10.5093257037662 0.0406742962337674 250 10.55 10.5364111869285 0.0135888130714594 251 10.55 10.5454601343406 0.00453986565937825 252 10.63 10.5484832832642 0.0815167167358037 253 10.63 10.6027662054789 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275 10.63 10.6253731586202 0.00462684137983338 276 10.63 10.6284542256795 0.00154577432048164 277 10.57 10.6294835746347 -0.0594835746346796 278 10.63 10.5898727759637 0.0401272240363486 279 10.6 10.6165939574376 -0.0165939574375766 280 10.7 10.6055438497187 0.0944561502812729 281 10.7 10.6684432900266 0.0315567099734189 282 10.7 10.6894572672994 0.0105427327006353 283 10.7 10.6964777946468 0.00352220535317826 284 10.7 10.6988232718307 0.00117672816927339 285 10.7 10.6996068686957 0.000393131304258887 286 10.7 10.6998686593672 0.000131340632801624 287 10.7 10.69995612061 4.38793900094936e-05 288 10.7 10.6999853404021 1.46595979213515e-05 289 10.7 10.6999951023975 4.89760252264659e-06 290 10.6 10.6999983637675 -0.0999983637675061 291 10.6 10.6334082995531 -0.0334082995530807 292 10.6 10.6111613274155 -0.0111613274155484 293 10.6 10.6037288707101 -0.00372887071008066 294 10.7 10.6012457726805 0.0987542273194926 295 10.7 10.6670073520793 0.0329926479206524 296 10.6 10.6889775369991 -0.0889775369990531 297 10.7 10.6297263684881 0.0702736315119026 298 10.7 10.6765223905294 0.0234776094706000 299 10.7 10.6921564015607 0.00784359843934723 300 10.7 10.6973795442609 0.00262045573910541 301 10.7 10.6991245359724 0.000875464027602746 302 10.7 10.6997075175695 0.000292482430513274 303 10.7 10.6999022849946 9.77150053707021e-05 304 10.7 10.6999673545441 3.26454558585709e-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 10.6999998641279 1.35872049611407e-07 310 10.7 10.6999999546067 4.53932837984894e-08 311 10.8 10.6999999848346 0.100000015165374 312 10.8 10.7665911487339 0.0334088512660564 313 10.8 10.7888384882635 0.0111615117364892 314 10.8 10.7962710677104 0.00372893228958127 315 10.7 10.7987542067465 -0.0987542067464915 316 10.7 10.7329926410474 -0.0329926410474499 317 10.7 10.7110224607047 -0.0110224607046874 318 10.85 10.7036824769442 0.146317523055773 319 10.75 10.8011170037594 -0.0511170037594102 320 10.75 10.7670776011678 -0.0170776011677631 321 10.75 10.7557054295087 -0.00570542950869779 322 10.75 10.7519061181696 -0.00190611816958075 323 10.75 10.7506368120877 -0.000636812087655869 324 10.75 10.7502127515709 -0.000212751570945358 325 10.75 10.7500710778451 -7.10778451242788e-05 326 10.75 10.7500237462880 -2.37462879599804e-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 10.8165912526338 0.0334087473662183 332 10.85 10.8388385229752 0.0111614770247517 333 10.85 10.8462710793072 0.00372892069278841 334 10.85 10.8487542106208 0.00124578937915309 335 10.85 10.8495837961423 0.000416203857650288 336 10.85 10.8498609510933 0.000139048906678596 337 10.85 10.8499535453646 4.64546353740047e-05 338 10.85 10.8499844800423 1.55199576834519e-05 339 10.85 10.8499948149612 5.18503879298748e-06 340 10.75 10.8499982677384 -0.0999982677383642 341 10.75 10.7834082674709 -0.0334082674708522 342 10.85 10.7611613166972 0.0888386833027521 343 10.85 10.8203200209297 0.0296799790702575 344 10.85 10.8400842614404 0.00991573855962002 345 10.85 10.8466872661551 0.00331273384492548 346 10.75 10.8488932538448 -0.09889325384475 347 10.75 10.7830390950787 -0.033039095078653 348 10.75 10.7610379804605 -0.0110379804605270 349 10.75 10.7536876619156 -0.00368766191558656 350 10.75 10.7512320052977 -0.00123200529773548 Extrapolation Forecasts of Exponential Smoothing t Forecast 95% Lower Bound 95% Upper Bound 351 10.7504115987551 10.6652816507396 10.8355415467706 352 10.7504115987551 10.6481337789360 10.8526894185742 353 10.7504115987551 10.6334740222248 10.8673491752854 354 10.7504115987551 10.6204576023463 10.8803655951639 355 10.7504115987551 10.6086311855788 10.8921920119314

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t12920038838xocrl36ymwj91j/17kp71292003981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920038838xocrl36ymwj91j/17kp71292003981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920038838xocrl36ymwj91j/27kp71292003981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920038838xocrl36ymwj91j/27kp71292003981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920038838xocrl36ymwj91j/37kp71292003981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t12920038838xocrl36ymwj91j/37kp71292003981.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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