Commenced in January 2007
Paper Count: 30184
Computational Intelligence Hybrid Learning Approach to Time Series Forecasting
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060910Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1172
 K. E. Parsopoulos and M. N. Vrahatis, "Particle swarm optimization method for constrained optimization problems," Intelligent Technologies-Theory and Application: New Trends in Intelligent Technologies, pp. 214-220, 2002.
 K. E. Parsopoulos and M. N. Vrahatis, "Recent approaches to global optimization problems through particle swarm optimization," Natural Computing, vol. 1, pp. 235-306, 2002.
 Y. Shi, R. C. Eberhart, E. Center, and I. N. Carmel, "Empirical study of particle swarm optimization," Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, vol. 3 pp.1945-1950,1999.
 J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE International Conference on Neuro Network, 1995, vol. 4, pp. 1942-1948, 1995.
 J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE transactions on systems, man, and cybernetics, vol. 23, pp.665-685, 1993.
 J. S. R. Jang, C. T. Sun, E. Mizutani, "Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence," Prentice Hall, 1997.
 M. Sugeno and G. T. Kang, "Structure identification of fuzzy model," Fuzzy sets and systems, vol. 28, pp. 15-33, 1988.
 I. Sugiarto and S. Natarajan, "Parameter estimation using least square method for MIMO Takagi-Sugeno neuro-fuzzy in time series forecasting," Jurnal Teknik Elektro, pp. 82-87, vol. 7, 2008.
 T. A. Jilani, S. M. A. Burney, and C. Ardil, "Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning," International Journal of Computational Intelligence, vol.4, pp. 112-117, 2007.
 S. Chen, C. F. N. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Transactions on neural networks, vol. 2, pp. 302-309, 1991.
 K. B. Cho and B. H. Wang, "Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction," Fuzzy Sets and Systems, vol. 83, pp. 325-339, 1996.
 D. Nauck and R. Kruse, "Neuro-fuzzy systems for function approximation," Fuzzy Sets and Systems, vol. 101, pp. 261-272, 1999.
 S. Paul and S. Kumar, "Subsethood-product fuzzy neural inference system (SuPFuNIS)," IEEE Transactions on Neural Networks, vol. 13, pp. 578-599, 2002.
 Y. Chen, B. Yang, and J. Dong, "Time-series prediction using a local linear wavelet neural network," Neurocomputing, vol. 69, pp. 449-465, 2006.
 J. Kim and N. Kasabov, "HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems," Neural Networks, vol. 12, pp. 1301-1319, 1999.
 Y. Chen, B. Yang, J. Dong, and A. Abraham, "Time-series forecasting using flexible neural tree model," Information sciences, vol. 174, pp. 219-235, 2005.