Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks

This publication has 38 references indexed in Scilit:

• Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index – Case study of PETR4, Petrobras, Brazil
  Expert Systems with Applications, 2013
• Lattice-oriented percolation system applied to volatility behavior of stock market
  Journal of Applied Statistics, 2012
• Fluctuation prediction of stock market index by Legendre neural network with random time strength function
  Neurocomputing, 2012
• Statistical analysis and forecasting of return interval for SSE and model by lattice percolation system and neural network
  Computers & Industrial Engineering, 2012
• A prediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on Back Propagation Artificial Neural Network and Principal Components Analysis
  Computers and Electronics in Agriculture, 2009
• Design of experiments on neural network's training for nonlinear time series forecasting
  Neurocomputing, 2009
• Neural network model for standard PCA and its variants applied to remote sensing
  International Journal of Remote Sensing, 2005
• Financial time series forecasting using support vector machines
  Neurocomputing, 2003
• A multi-component nonlinear prediction system for the S&P 500 index
  Neurocomputing, 1996
• Stable adaptive neural control scheme for nonlinear systems
  IEEE Transactions on Automatic Control, 1996