Turning point identification and Bayesian forecasting of a volatile time series
Computers and Industrial Engineering
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Hybrid Intelligent Systems for Stock Market Analysis
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Stock valuation along a Markov chain
Applied Mathematics and Computation
Chaos and Fractals
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
StockMarket Forecasting Using Hidden Markov Model: A New Approach
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Prediction of multivariate chaotic time series with local polynomial fitting
Computers & Mathematics with Applications
Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait
Expert Systems with Applications: An International Journal
A new chaos-based fast image encryption algorithm
Applied Soft Computing
Complex number procedure neural networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
IEEE Transactions on Neural Networks
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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.