Improving Financial Time Series Prediction Accuracy Using Ensemble Empirical Mode Decomposition and Recurrent Neural Networks

Improving Financial Time Series Prediction Accuracy Using Ensemble Empirical Mode Decomposition and Recurrent Neural Networks

Recurrent neural networks have received vast amount of attention in time series prediction due to their flexibility in capturing dependencies on various scales. However, as in most of the classical forecasting methods, its accuracy is strongly tied to the degree of signal complexity. Specifically, s...

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Bibliographic Details
Main Authors: Henry Daniel Chacon, Emre Kesici, Peyman Najafirad
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Empirical mode decomposition
EMD
artificial intelligence
machine learning
long short term memory
LSTM
Online Access:https://ieeexplore.ieee.org/document/9099274/

Internet

https://ieeexplore.ieee.org/document/9099274/

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