Detail publikace

Advanced approach to numerical forecasting using artificial neural networks

ŠTENCL, M. ŠŤASTNÝ, J.

Originální název

Advanced approach to numerical forecasting using artificial neural networks

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Artificial Neural Networks, Multi Layer Perceptron Network, Numerical Forecasting, Radial basis function

Autoři

ŠTENCL, M.; ŠŤASTNÝ, J.

Rok RIV

2009

Vydáno

21. 12. 2009

ISSN

1211-8516

Periodikum

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

Ročník

2009

Číslo

6

Stát

Česká republika

Strany od

297

Strany do

305

Strany počet

8

BibTex

@article{BUT48389,
  author="Michael {Štencl} and Jiří {Šťastný}",
  title="Advanced approach to numerical forecasting using artificial neural networks",
  journal="Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis",
  year="2009",
  volume="2009",
  number="6",
  pages="297--305",
  issn="1211-8516"
}