A dual-stage advanced deep learning algorithm for long-term and long-sequence prediction for multivariate financial time series

This publication has 24 references indexed in Scilit:

• SVM hyperparameters tuning for recursive multi-step-ahead prediction
  Neural Computing and Applications, 2016
• Recurrent neural network and a hybrid model for prediction of stock returns
  Expert Systems with Applications, 2015
• Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
  Geoscientific Model Development, 2014
• COPAR—multivariate time series modeling using the copula autoregressive model
  Applied Stochastic Models in Business and Industry, 2014
• Forecasting stock indices with back propagation neural network
  Expert Systems with Applications, 2011
• Using artificial neural network models in stock market index prediction
  Expert Systems with Applications, 2011
• Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
  Climate Research, 2005
• Time series forecasting using a hybrid ARIMA and neural network model
  Neurocomputing, 2003
• Financial time series prediction using least squares support vector machines within the evidence framework
  IEEE Transactions on Neural Networks, 2001
• Long Short-Term Memory
  Neural Computation, 1997