A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Stock market prediction with multiple classifiers
Applied Intelligence
Analysis of the predictive ability of time delay neural networksapplied to the S&P 500 time series
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Applied Stochastic Models in Business and Industry
Predicting time series of railway speed restrictions with time-dependent machine learning techniques
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Neural network has been popular in time series prediction in financial areas because of their advantages in handling nonlinear systems. This paper presents a study of using a novel recurrent neural network-echo state network (ESN) to predict the next closing price in stock markets. The Hurst exponent is applied to adaptively determine initial transient and choose sub-series with greatest predictability during training. The experiment results on nearly all stocks of S&P 500 demonstrate that ESN outperforms other conventional neural networks in most cases. Experiments also indicate that if we include principle component analysis (PCA) to filter noise in data pretreatment and choose appropriate parameters, we can effectively prevent coarse prediction performance. But in most cases PCA improves the prediction accuracy only a little.