Abstract
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.
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