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Time Series Forecasting Using a Hybrid Adaptive Particle Swarm Optimization and Neural Network Model

Author

Listed:
  • Xiao Yi

    (School of Information Management, Central China Normal University, Wuhan430079, China)

  • Liu John J.
  • Wang Yingfeng

    (Center for Transport Trade and Financial Studies, City University of Hong Kong, Hong Kong, China)

  • Hu Yi

    (School of Management, University of Chinese Academy of Sciences, Beijing100190, China)

Abstract

For time series forecasting, the problem that we often encounter is how to increase the prediction accuracy as much as possible with the irregular and noise data. This study proposes a novel multilayer feedforward neural network based on the improved particle swarm optimization with adaptive genetic operator (IPSO- MLFN). In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Further, a crossover rate which only depends on generation and does not associate with the individual fitness is designed. Finally, the parameters of MLFN are optimized by IPSO. The empirical results on the container throughput forecast of Shenzhen Port show that forecasts with IPSO-MLFN model are more conservative and credible.

Suggested Citation

  • Xiao Yi & Liu John J. & Wang Yingfeng & Hu Yi, 2014. "Time Series Forecasting Using a Hybrid Adaptive Particle Swarm Optimization and Neural Network Model," Journal of Systems Science and Information, De Gruyter, vol. 2(4), pages 335-344, August.
    • Handle: RePEc:bpj:jossai:v:2:y:2014:i:4:p:335-344:n:4
    DOI: 10.1515/JSSI-2014-0335
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    References listed on IDEAS

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