Table of Contents
Advances in Artificial Neural Systems
Volume 2011, Article ID 814769, 11 pages
Research Article

Stock Price Prediction Based on Procedural Neural Networks

Department of Electrical Engineering, Jiangnan University, Wuxi 214122, China

Received 11 January 2011; Revised 28 March 2011; Accepted 6 April 2011

Academic Editor: Songcan Chen

Copyright © 2011 Jiuzhen Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present a spatiotemporal model, namely, procedural neural networks for stock price prediction. Compared with some successful traditional models on simulating stock market, such as BNN (backpropagation neural networks, HMM (hidden Markov model) and SVM (support vector machine)), the procedural neural network model processes both spacial and temporal information synchronously without slide time window, which is typically used in the well-known recurrent neural networks. Two different structures of procedural neural networks are constructed for modeling multidimensional time series problems. Learning algorithms for training the models and sustained improvement of learning are presented and discussed. Experiments on Yahoo stock market of the past decade years are implemented, and simulation results are compared by PNN, BNN, HMM, and SVM.