Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

Xie, Hailun, Zhang, Li and Lim, Chee Peng (2020) Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer. IEEE Access, 8. pp. 161519-161541. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2020.3021527


In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks.

Item Type: Article
Uncontrolled Keywords: Evolutionary computation, Grey Wolf optimizer, time series prediction, and deep neural network
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 12 Oct 2020 10:15
Last Modified: 16 Dec 2022 16:00
URI: https://nrl.northumbria.ac.uk/id/eprint/44474

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