Stock Prediction Based on Phase Space Reconstruction and Echo State Networks

Abstract

In this paper a synthetic model for stock prediction is proposed based on phase space reconstruction, Echo State Networks (ESN) and Moving Average Convergence/Divergence (MACD). In this model, time series data is reconstructed in phase space before feeding to the ESN. Guided by the MACD strategy, stock prices are predicted and valuable trading advices are provided. The proposed model is tested by Microsoft Company stock price. The experiments are compared with those provided by other models such as BackPropagation Neural Networks (BNN), Support Vector Machine (SVM), Hidden Markov Model (HMM) and Procedural Neural Networks (PNN). The test results show that the proposed model is effective and efficient in stock price prediction.