The week's pick
Reseach Article
Time Series Forecasting using Evolutionary Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 75 - Number 10 |
| Year of Publication: 2013 |
| Authors: Sibarama Panigrahi, Yasobanta Karali, H. S. Behera |
10.5120/13146-0553
|
Sibarama Panigrahi, Yasobanta Karali, H. S. Behera . Time Series Forecasting using Evolutionary Neural Network. International Journal of Computer Applications. 75, 10 ( August 2013), 13-17. DOI=10.5120/13146-0553
Abstract
Efficient time series forecasting (TSF) is of utmost importance in order to make better decision under uncertainty. Over the past few years a large literature has evolved to forecast time series using different artificial neural network (ANN) models because of its several distinguishing characteristics. This paper evaluates the effectiveness of three methods to forecast time series, one carried out with ANN-GD using extended back propagation (EBP) algorithm, second one carried out with ANN-GA using genetic algorithm (GA) and the last one carried out with ANN-DE using differential evolution (DE). For comparative performance analysis between these methods two benchmark time series such as: wisconsin employment time series and monthly milk production time series are considered. Results show that both the ANN-GA and ANN-DE outperform ANN-GD considering forecast accuracy. It is also observed that the ANN-DE performs better than ANN-GA for both the time series considered.
References
- S. G. Makridakis, S. C. Wheelright, R. J. Hyndman, Forecasting: Methods and Applications.
- G. P. Zhang, B. E. Patuwo, M. Y. Hu, Forecasting with artificial neural networks: The state of art, International Journal of Forecasting 14 (1988) 35-62.
- J. Connors, D. Martin, and L. Atlas, "Recurrent neural networks and robust time series prediction," IEEE Trans. Neural Netw. , vol. 5, no. 2, pp. 240–254, Mar. 1994.
- C. M. Kuan and T. Liu, "Forecasting exchange rates using feed forward and recurrent neural networks," J. Appl. Econ. , vol. 10, no. 4, pp. 347–364, 1995.
- C. L. Giles, S. Lawrence, and A. C. Tsoi, "Noisy time series prediction using a recurrent neural network and grammatical inference," Mach. Learn. , vol. 44, nos. 1–2, pp. 161–183, 2001.
- X. B. Yan, Z. Wang, S. H. Yu, and Y. J. Li, "Time series forecasting with RBF neural network," in Proc. IEEE Int. Conf. Mach. Learn. Cybern. ,vol. 8. Guangzhou, China, Aug. 2005, pp. 4680–4683.
- F. Liang, "Bayesian neural networks for nonlinear time series forecasting," Stat. Comput. , vol. 15, no. 1, pp. 13–29, Jan. 2005.
- M. Firat, "Comparison of arti?cial intelligence techniques for river flow forecasting," Hydrol. Earth Syst. Sci. , vol. 12, no. 1, pp. 123–139, 2008.
- Y. Bodyanskiy, I. Pliss, and O. Vynokurova, "Adaptive wavelet-neuro-fuzzy network in the forecasting and emulation tasks," Int. J. Inf. Theories Appl. , vol. 15, no. 1, pp. 47–55, 2008.
- W. Yan, "Toward Automatic Time-Series Forecasting Using Neural Networks," IEEE Transaction on Neural Networks and Learning Systems, vol. 23,no. 7, July 2012.
- A. Sorjamaa, Y. Miche, R. Weiss, and A. Lendasse, "Long-term prediction of time-series using NNE-based projection and OP-ELM," in Proc. IEEE World Congr. Comput. Intell. , Jun. 2008, pp. 2674–2680.
- H. Dhari, A. M. Alimi and A. Abraham, "Hierarchical multi-dimensional differential evolution for the design of beta basis function neural network," Neurocomputing Journal, Elsevier Science, vol. 15. pp. 131-140, Nov. 2012.
- J. Peralta, G. Gutierrez, A. Sanchis, ADANN: Automatic Design of Artificial Neural Networks. ARC-FEC 2008 (GECCO 2008). ISBN 978-1-60558-131-6.
- J. Peralta, X. Li, G. Gutierrez, A. Sanchis, Time series forecasting by evolving artificial neural networks using genetic algorithms and differential evolution, In Proceedings of the 2010 WCCI conference, IJCNN-WCCI', (2010) 3999–4006.
- J. P. Donate, X. Li, G. G. Sanchez, A. S. D. Miguel, Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm, Neural Computing and Application (2011) DOI 10. 1007/s00521-011-0741-0.
- R. Storn, K. Price, Differential evolution- A simple and efficient heuristic for global optimization over continuous spaces, J. Glob. Optim. 11(4) (1997) 341-359.
- D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA:Addison-Wesley (1989).
- J. Kennedy, R. c. Eberhart, Y. Shi, Swarm intelligence, San Francisco, CA:Morgan Kaufmann (2001).
- K. Socha, M. Doringo, Ant colony optimization for continuous domains, Eur. J. Oper. Res. 185(3) (2008) 1155-1173.
- D. T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi, The bees algorithm- A novel tool for complex optimization problems, in IPROMS 2006. Oxford, U. K. : Elsevier (2006).
- H. G. Beyer, H. P. Schwefel, Evolutionary Strategies: A Compehensive introduction, Nat. Comput. 1(1) (2002) 3-52.
- A. Y. S. Lam and V. O. K. Li, "Chemical-Reaction-inspired metaheuristic for optimization", IEEE Transactionson on Evolutionary Computation, 14(3), (2010),381–399.
- A. Y. S. Lam, "Real-Coded Chemical Reaction Optimization", IEEE Transaction on Evolutionary Computation, 16(3) (2012), 339-353.
- K. K. Sahu, S. Panigrahi and H. S. Behera, "A Novel Chemical Reaction Optimization Algorithm for Higher Order Neural Network Training", Journal of Theoretical and Applied Information Technology, 53( 3) (2013), 402-409.
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