Improving Financial Time Series Prediction Accuracy Using Ensemble Empirical Mode Decomposition and Recurrent Neural Networks.
Henry ChaconEmre KesiciPeyman NajafiradPublished in: IEEE Access (2020)
Keyphrases
- prediction accuracy
- recurrent neural networks
- financial time series
- empirical mode decomposition
- non stationary
- ensemble methods
- neural network
- financial time series forecasting
- stock market
- stock market prediction
- turning points
- feed forward
- improve the prediction accuracy
- stock price
- echo state networks
- financial data
- artificial neural networks
- random fields
- hilbert huang transform
- autoregressive
- multivariate time series
- intrinsic mode functions
- exchange rate
- back propagation
- pattern recognition
- learning algorithm
- multiscale
- decision trees