Computational intelligence hybrid learning approach to time series forecasting

Chunshien Li, Jhao Wun Hu, Tai Wei Chiang, Tsunghan Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybrid learning (HL) approach for the neuro-fuzzy system, the prediction performance is excellent and the speed of learning convergence is much faster than other compared approaches. In the experiments, we use the well-known Mackey-Glass chaos time series. According to the experimental results, the prediction performance and accuracy in time series forecasting by the proposed approach is much better than other compared approaches, as shown in Table IV. Excellent prediction performance by the proposed approach has been observed.

Original languageEnglish
Pages (from-to)772-779
Number of pages8
JournalWorld Academy of Science, Engineering and Technology
StatePublished - 2010


  • Forecasting
  • Hybrid learning (HL)
  • Neuro-fuzzy system (NFS)
  • Particle swarm optimization (PSO)
  • Recursive least-squares estimator (RLSE)
  • Time series


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