Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach

Wei Du, Sunney Yung Sun Leung, Chun Kit Kwong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)8049-8061
Number of pages13
JournalExpert Systems with Applications
Issue number18
Publication statusPublished - 15 Dec 2014


  • Artificial neural network (ANN)
  • Knee point
  • Multiobjective evolutionary algorithm (MOEA)
  • Time series forecasting (TSF)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

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