A Neural Network Model for Prediction: Architecture and Training Analysis
نویسندگان
چکیده
The main purpose of the present paper is to establish an optimum feedforward neural architecture and a well suited training algorithm for financial forecasting. The artificial neural networks (ANNs) ability to extract significant information provides valuable framework for the representation of relationships present in financial data. The evaluation of the computing effort involved in the ANN training shows us that a good choice for the network architecture and training algorithms can substantially improve the achieved results.
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