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2007 | 54 | 1 | 20-33
Tytuł artykułu

MULTILAYER PERCEPTRONS AS APPROXIMATIONS TO PROBABILITY DENSITY FUNCTIONS IN TIME SERIES FORECASTING

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the method of utilisation of multilayer perceptron neural networks to probability densiity function approximation in the problem of time series forecasting. The theoretical background has been given and the specification of neural prediction model, which generates the probability distribution of the forecasted variable in the issue of financial time series predicition, has been described. Next, the research concerning the performance of such model designed for the forecasting of the Polish stock index WIG has been discussed. Two versions of the model have been applied: first - comprised of 12 perceptron networks with single output each, second - based on one network with 12 outputs. Three test cases (for subsequent stock exchange sessions ) have been analysed. Obtained probability distributions are somewhat similar to empirical distribution (achieved for model development data), but they clearly indicate predicted tendency of index change and show specific uncertainty of the forecast.
Rocznik
Tom
54
Numer
1
Strony
20-33
Opis fizyczny
Rodzaj publikacji
ARTICLE
Twórcy
autor
  • J. Morajda, Akademia Ekonomiczna w Krakowie, Katedra Informatyki, ul. Rakowicka 27, 31-510 Kraków, Poland
Bibliografia
Typ dokumentu
Bibliografia
Identyfikatory
CEJSH db identifier
07PLAAAA02525245
Identyfikator YADDA
bwmeta1.element.34c254ea-2f32-3512-984a-3f970454ca27
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