Journal of Dalian Maritime University >
2017 , Vol. 43 >Issue 4: 97 - 103
DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2017.04.014
Intelligent prediction model for nonlinear time series based on reinforcement learning
Received date: 2017-06-16
Revised date: 2017-08-16
Online published: 2017-08-21
Aiming at the nonlinear and high noise issues in time series prediction, an hybrid intelligent model for time series forward multistep prediction was proposed. Firstly, for training the model, a method of combining reinforcement learning and hidden Markov model was proposed, TD(λ) approach was used to reinforce learning, and historical observed data was adopted as reward returns and emphasized the different degree of influence in far more recent to iteratively improve the roles of historical observed data played in model training processes. Further, in the process of forward multi-step prediction, a method of combining reinforcement learning with neural network and hidden Markov model was proposed, which took full advantages of data fitting by neural network and reducing systematic random error in predicting processes by hidden Markov model. Experimental results of rare earth futures trade data predictions show that the intelligent hybrid model obviously reduces mean absolute error, mean absolute percentage error and root mean square error, which improves the prediction precision and effect.
SUN Ruo-ying , FAN Hou-ming , ZHAO Gang . Intelligent prediction model for nonlinear time series based on reinforcement learning[J]. Journal of Dalian Maritime University, 2017 , 43(4) : 97 -103 . DOI: 10.16411/j.cnki.issn1006-7736.2017.04.014
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