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Hybrid Radial Basis Function with Particle Swarm Optimisation Algorithm for Time Series Prediction Problems

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 287)

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.

Keywords

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

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia (UKM)BangiMalaysia

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