Computational Intelligence Hybrid Learning Approach to Time Series Forecasting
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.
[1] 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.
[2] 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.
[3] 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.
[4] J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE
International Conference on Neuro Network, 1995, vol. 4, pp.
1942-1948, 1995.
[5] J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference
system," IEEE transactions on systems, man, and cybernetics, vol.
23, pp.665-685, 1993.
[6] 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.
[7] M. Sugeno and G. T. Kang, "Structure identification of fuzzy
model," Fuzzy sets and systems, vol. 28, pp. 15-33, 1988.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] D. Nauck and R. Kruse, "Neuro-fuzzy systems for function
approximation," Fuzzy Sets and Systems, vol. 101, pp. 261-272,
1999.
[13] S. Paul and S. Kumar, "Subsethood-product fuzzy neural inference
system (SuPFuNIS)," IEEE Transactions on Neural Networks, vol.
13, pp. 578-599, 2002.
[14] Y. Chen, B. Yang, and J. Dong, "Time-series prediction using a
local linear wavelet neural network," Neurocomputing, vol. 69, pp.
449-465, 2006.
[15] 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.
[16] 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.
[1] 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.
[2] 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.
[3] 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.
[4] J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE
International Conference on Neuro Network, 1995, vol. 4, pp.
1942-1948, 1995.
[5] J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference
system," IEEE transactions on systems, man, and cybernetics, vol.
23, pp.665-685, 1993.
[6] 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.
[7] M. Sugeno and G. T. Kang, "Structure identification of fuzzy
model," Fuzzy sets and systems, vol. 28, pp. 15-33, 1988.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] D. Nauck and R. Kruse, "Neuro-fuzzy systems for function
approximation," Fuzzy Sets and Systems, vol. 101, pp. 261-272,
1999.
[13] S. Paul and S. Kumar, "Subsethood-product fuzzy neural inference
system (SuPFuNIS)," IEEE Transactions on Neural Networks, vol.
13, pp. 578-599, 2002.
[14] Y. Chen, B. Yang, and J. Dong, "Time-series prediction using a
local linear wavelet neural network," Neurocomputing, vol. 69, pp.
449-465, 2006.
[15] 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.
[16] 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.
@article{"International Journal of Information, Control and Computer Sciences:54455", author = "Chunshien Li and Jhao-Wun Hu and Tai-Wei Chiang and Tsunghan Wu", title = "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.", keywords = "forecasting, hybrid learning (HL), Neuro-FuzzySystem (NFS), particle swarm optimization (PSO), recursiveleast-squares estimator (RLSE), time series", volume = "4", number = "7", pages = "1149-8", }