Hybrid radial basis function with particle swarm optimisation algorithm for time series prediction problems

Ali Hassan, Salwani Abdullah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Time Series Prediction (TSP) is to estimate some future value based on current and past data samples. Researches indicated that most of models applied on TSP suffer from a number of shortcomings such as easily trapped into a local optimum, premature convergence, and high computation complexity. In order to tackle these shortcomings, this research proposes a method which is Radial Base Function hybrid with Particle Swarm Optimization algorithm (RBF-PSO). The method is applied on two well-known benchmarks dataset Mackey-Glass Time Series (MGTS) and Competition on Artificial Time Series (CATS) and one real world dataset called the Rainfall dataset. The results revealed that the RBF-PSO yields competitive results in comparison with other methods tested on the same datasets, if not the best for MGTS case. The results also demonstrate that the proposed method is able to produce good prediction accuracy when tested on real world rainfall dataset as well.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages227-238
Number of pages12
Volume287
ISBN (Print)9783319076911
DOIs
Publication statusPublished - 2014
Event1st International Conference on Soft Computing and Data Mining, SCDM 2014 - Parit Raja, Batu Pahat
Duration: 16 Jun 201418 Jun 2014

Publication series

NameAdvances in Intelligent Systems and Computing
Volume287
ISSN (Print)21945357

Other

Other1st International Conference on Soft Computing and Data Mining, SCDM 2014
CityParit Raja, Batu Pahat
Period16/6/1418/6/14

Fingerprint

Particle swarm optimization (PSO)
Time series
Rain
Glass

Keywords

  • Competition on Artificial Time Series
  • Mackey-Glass Time Series
  • Particle Swarm Optimization
  • Radial Base Function
  • Time series prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Hassan, A., & Abdullah, S. (2014). Hybrid radial basis function with particle swarm optimisation algorithm for time series prediction problems. In Advances in Intelligent Systems and Computing (Vol. 287, pp. 227-238). (Advances in Intelligent Systems and Computing; Vol. 287). Springer Verlag. https://doi.org/10.1007/978-3-319-07692-8_22

Hybrid radial basis function with particle swarm optimisation algorithm for time series prediction problems. / Hassan, Ali; Abdullah, Salwani.

Advances in Intelligent Systems and Computing. Vol. 287 Springer Verlag, 2014. p. 227-238 (Advances in Intelligent Systems and Computing; Vol. 287).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hassan, A & Abdullah, S 2014, Hybrid radial basis function with particle swarm optimisation algorithm for time series prediction problems. in Advances in Intelligent Systems and Computing. vol. 287, Advances in Intelligent Systems and Computing, vol. 287, Springer Verlag, pp. 227-238, 1st International Conference on Soft Computing and Data Mining, SCDM 2014, Parit Raja, Batu Pahat, 16/6/14. https://doi.org/10.1007/978-3-319-07692-8_22
Hassan A, Abdullah S. Hybrid radial basis function with particle swarm optimisation algorithm for time series prediction problems. In Advances in Intelligent Systems and Computing. Vol. 287. Springer Verlag. 2014. p. 227-238. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-07692-8_22
Hassan, Ali ; Abdullah, Salwani. / Hybrid radial basis function with particle swarm optimisation algorithm for time series prediction problems. Advances in Intelligent Systems and Computing. Vol. 287 Springer Verlag, 2014. pp. 227-238 (Advances in Intelligent Systems and Computing).
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