Abstract
Stock price prediction is one of the most important topics in financial engineering. In this paper, for stock closing price prediction, we propose a diagonal log-normal generalized RBF neural network in which the diagonal log-normal density functions serve as the RBFs. Specifically, it utilizes the dynamic split-and-merge EM algorithm to select the number of hidden units (or RBFs) as well as the initial values of the parameters, and implements a synchronous LMS learning algorithm for parameter learning. It is demonstrated by the experiments that the diagonal log-normal generalized RBF neural network has a competitive performance on stock closing price prediction.
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Zheng, W., Ma, J. (2014). Diagonal Log-Normal Generalized RBF Neural Network for Stock Price Prediction. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_64
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DOI: https://doi.org/10.1007/978-3-319-12436-0_64
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