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Multi-step-ahead chaotic time series predication using coevolutionary recurrent neural networks

Hussein, Shamina and Chandra, Rohitash and Sharma, Anuraganand (2016) Multi-step-ahead chaotic time series predication using coevolutionary recurrent neural networks. [Conference Proceedings]

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Abstract

Multi-step-ahead time series prediction has been one of the greatest challenges for machine learning. Recurrent neural networks (RNN) can efficiently model temporal sequences and have been promising for multi-step time series prediction. Cooperative neuro-evolution has been used for training RNNs with promising performance for single step ahead time series prediction. This paper employs cooperative neuro-evolution of RNNs for multi-step ahead prediction. The RNN recursively predicts the next values in the horizon where the output from the single-step ahead prediction are the input for predicting the next value in the horizon. The performance of cooperative neuro-evolution is compared with back-propagation through time (BPTT) learning algorithm. The results are promising which shows that cooperative neuro-evolution performs better compared to BPTT for most cases.

Item Type: Conference Proceedings
Uncontrolled Keywords: backpropagation;chaos;evolutionary computation;learning (artificial intelligence);recurrent neural nets;time series;multistep-ahead chaotic time series prediction;coevolutionary recurrent neural networks;RNN;cooperative neuro-evolution;single-step ahead prediction;back-propagation through time;BPTT learning algorithm;Time series analysis;Neurons;Recurrent neural networks;Training;Predictive models;Prediction algorithms;Biological neural networks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User: Anuraganand Sharma
Date Deposited: 03 Jul 2019 10:35
Last Modified: 03 Jul 2019 10:35
URI: http://repository.usp.ac.fj/id/eprint/11606
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