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

• Authors: Morajda J.
• Languages of publication: EN

Abstracts

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.

Keywords

EN

MULTILAYER PERCEPTRON; NEURAL NETWORKS; PROBABILITY DENSITY FUNCTION; TIME SERIES

Discipline

• ECONOMICS: ECONOMICS

• Publisher: Główny Urząd Statystyczny
• Journal: Przegląd Statystyczny
• Year: 2007
• Volume: 54
• Issue: 1
• Pages: 20-33
• Document type: ARTICLE

Contributors

author

Morajda J.

• J. Morajda, Akademia Ekonomiczna w Krakowie, Katedra Informatyki, ul. Rakowicka 27, 31-510 Kraków, Poland

Identifiers

CEJSH db identifier

07PLAAAA02525245