Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach |
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| Affiliation: | 1. Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China;2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China;1. Department of Mathematics, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India;2. Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad 500 032, India;1. Centre of Image and Signal Processing, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. School of Computing, National University of Singapore, Singapore;1. Madurai Kamaraj University, Madurai 625 021, India;2. Sri Meenakshi Govt. Arts College for Women(A), Madurai 625 002, India;1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, PR China;2. Technology Center of Software Engineering, Institute of Software, Chinese Academy of Sciences, Beijing 100190, PR China;1. University of Belgrade, Technical Faculty in Bor, VJ 12, Bor, Serbia;2. University of Belgrade, Faculty of Mining and Geology, Djusina 7, Belgrade, Serbia;1. Department of Management Sciences, University of Waterloo, Waterloo, ON, Canada;2. HEC Paris, Strategy and Business Policy, I rue de la Liberation, Dept SPE, W1-214, 78351, Jouy-en-Josas, France |
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| 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. |
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| Keywords: | Artificial neural network (ANN) Multiobjective evolutionary algorithm (MOEA) Time series forecasting (TSF) Knee point |
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