Aleksejs Zorins


The paper focuses on the retail turnover prediction with artificial neural networks. The artificial neural networks have the potential to learn complex, non-linear relationships within data. The main disadvantage is that neural networks are “black boxes”, so the user cannot explain the obtained results and relationships between data. The modular neural networks allow obtaining more appropriate results by splitting the task into subtasks, thus giving the user more information in the output. In many cases an additional advantage of modular neural network is more precise prediction results, which will be shown in the experimental part of this paper.


modular neural networks; artificial neural networks; time series prediction

Full Text:



Baestaens D.E., Van den Bergh W.M. Tracking the Amsterdam Stock Index Using Neural Networks. Neural Networks in Capital Markets, Vol. 5, 1995. P. 149-161.

Poh H.L., Yao J., Jasics T. Neural Networks for the Analysis and Forecasting of Advertising and Promotion Impact, International Journal of Intelligent Systems in Accounting, Finance & Management, Vol.7, 1998. P. 253-268.

Refenes A.N., Zapranis A., Francis G. Stock Performance Modelling Using Neural Networks, Neural Networks, Vol. 7. No 2. 1994. P. 357-388.

Rojas R. Neural networks. A systematic approach, Springer, Berlin, 1996.

Konar A. Computational intelligence: principles, techniques and applications. Springer-Verlag, London, 2005.

Palit A.K. Computational intelligence in time series forecasting: theory and engineering applications. – London, Springer-Verlag, 2005.

Fausett L. Fundamentals of Neural Networks. Architectures, algorithms and applications, Prentice Hall, 1994.

Zorin A. Data preprocessing methods for interval based neural network prediction, Proceedings of the International Conference “Environment. Technology. Resources”, Rezekne, Latvia, June 20-22, 2007. P. 211-219.

Zorin A. Forecasting with neural networks: exact and interval value prediction, Proceedings of the International Conference “Simulation and Optimisation in Business and Industry”, Tallinn, Estonia, May 17-20, 2006. P. 249-254.



  • There are currently no refbacks.

SCImago Journal & Country Rank