Abstract
This article presents an intelligent system using artificial neural techniques for time series prediction in stock exchange markets. For this purpose, is developed a hybrid neural network with supervised learning algorithm able to learn to predict the evolution of stock exchange for a given period of time. The learning model proposed for the intelligent system considers a First Input First Output (FIFO) queue with input values taken from the values obtained by prediction by the neural network at previous time. Analysis of the performance parameters of the neural network uses the method of the coefficient of certainty of neural prediction. Experimental study highlights the effectiveness of the proposed learning model for hybrid neural predictive system properties and its usefulness.
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Tudor, N.L. (2014). Intelligent System for Time Series Prediction in Stock Exchange Markets. In: Abramowicz, W., Kokkinaki, A. (eds) Business Information Systems. BIS 2014. Lecture Notes in Business Information Processing, vol 176. Springer, Cham. https://doi.org/10.1007/978-3-319-06695-0_11
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DOI: https://doi.org/10.1007/978-3-319-06695-0_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06694-3
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