Advanced approach to numerical forecasting using artificial neural networks

Pokročilé metody numerických predikcí s využitím neuronových sítí

Michael Štencl and Jiří Šťastný
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Michael Štencl: Ústav informatiky, Mendelova zemědělská a lesnická univerzita v Brně, Zemědělská 1, 613 00 Brno, Česká republika
Jiří Šťastný: Ústav informatiky, Mendelova zemědělská a lesnická univerzita v Brně, Zemědělská 1, 613 00 Brno, Česká republika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2009, vol. 57, issue 6, 297-304

Abstract: Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.

Keywords: artificial neural networks; Radial basis function; Numerical Forecasting; Multi Layer Perceptron Network (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:mup:actaun:actaun_2009057060297

DOI: 10.11118/actaun200957060297

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