摘要：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.
