Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35863
Title: Time series forecasting by neural networks : a knee point-based multiobjective evolutionary algorithm approach
Authors: Du, W
Leung, SYS 
Kwong, CK 
Keywords: Artificial neural network (ANN)
Multiobjective evolutionary algorithm (MOEA)
Time series forecasting (TSF)
Knee point
Issue Date: 2014
Publisher: Pergamon Press
Source: Expert systems with applications, 2014, v. 41, no. 18, p. 8049-8061 How to cite?
Journal: Expert systems with applications 
Abstract: In this paper, we investigate the problem of time series forecasting using single hidden layer feedforward neural networks (SLFNs), which is optimized via multiobjective evolutionary algorithms. By utilizing the adaptive differential evolution (JADE) and the knee point strategy, a nondominated sorting adaptive differential evolution (NSJADE) and its improved version knee point-based NSJADE (KP-NSJADE) are developed for optimizing SLFNs. JADE aiming at refining the search area is introduced in nondominated sorting genetic algorithm II (NSGA-II). The presented NSJADE shows superiority on multimodal problems when compared with NSGA-II. Then NSJADE is applied to train SLFNs for time series forecasting. It is revealed that individuals with better forecasting performance in the whole population gather around the knee point. Therefore, KP-NSJADE is proposed to explore the neighborhood of the knee point in the objective space. And the simulation results of eight popular time series databases illustrate the effectiveness of our proposed algorithm in comparison with several popular algorithms.
URI: http://hdl.handle.net/10397/35863
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2014.06.041
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