Advanced approach to numerical forecasting using artificial neural networks

• Main Authors: Michael Štencl, Jiří Šťastný
• Format: Article
• Language: English
• Published: Mendel University Press 2009-01-01
• Series: Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
• Subjects: artificial neural networks; Radial basis function; Numerical Forecasting; Multi Layer Perceptron Network
• Online Access: https://acta.mendelu.cz/57/6/0297/
• ISSN: 1211-8516; 2464-8310

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.