Advanced approach to numerical forecasting using artificial neural networks

Advanced approach to numerical forecasting using artificial neural networks

Peer-reviewed article not indexed in WoS or Scopus

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.

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.

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

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

ŠTENCL, M.; ŠŤASTNÝ, J.

2010

21.12.2009

1211-8516

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

2009

6

Czech Republic

297

305

8