Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble

Neurocomputing

Volumn 109, Issue , 2013, Pages 27-32

  Donate, Juan Peralta ^a   Cortez, Paulo ^b   Sánchez, Germán Gutiérrez ^a   De Miguel, Araceli Sanchis ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b UNIVERSITY OF MINHO   (Portugal)

Author keywords

Ensembles; Evolutionary computation; Genetic algorithms; Multilayer perceptron; Time Series Forecasting

Indexed keywords

COMBINATION METHOD; CROSS VALIDATION; ENSEMBLES; EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS; MULTI LAYER PERCEPTRON; ORGANIZATIONAL DECISION MAKING; REAL-WORLD TIME SERIES; TIME SERIES FORECASTING;

EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; INTELLIGENT AGENTS; NEURAL NETWORKS; TIME SERIES;

FORECASTING;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER MODEL; CONTROLLED STUDY; EVOLUTIONARY ALGORITHM; EVOLUTIONARY ARTIFICIAL NEURAL NETWORK; FORECASTING; INTERMETHOD COMPARISON; PRIORITY JOURNAL; STATISTICAL ANALYSIS; SYSTEM ANALYSIS; TIME SERIES ANALYSIS; TIME SERIES FORECASTING; VALIDATION STUDY;

EID: 84875936673     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.02.053     Document Type: Article

References (26)

1. Abraham A. Editorial-hybrid soft computing and applications. Int. J. Comput. Intell. Appl. 2009, 8.
2. Sedano J., Curiel L., Corchado E., de la Cal E., Villar J.R. A soft computing method for detecting lifetime building thermal insulation failures. Integrated Comput. Aided Eng. 2010, 17:103-115.
3. García S., Derrac J., Luengo J., Carmona C.J., Herrera F. Evolutionary selection of hyperrectangles in nested generalized exemplar learning. Appl. Soft Comput. 2011, 11:3032-3045.
4. Corchado E., Herrero A. Neural visualization of network traffic data for intrusion detection. Appl. Soft Comput. 2011, 11:2042-2056.
5. Wilk T., Wozniak M. Soft computing methods applied to combination of one-class classifiers. Neurocomputing 2012, 75:185-193.
6. Makridakis S., Wheelwright S., Hyndman R. Forecasting Methods and Applications 2008, John Wiley & Sons, USA. 3rd edition.
7. Winters P.R. Forecasting sales by exponentially weighted moving averages. Manage. Sci. 1960, 6:324-342.
8. Štěpnička M., Dvořák A., Pavliska V., Vavříčková L. A linguistic approach to time series modeling with the help of f-transform. Fuzzy Sets Syst. 2011, 180(1):164-184.
9. Zhang G., Eddy Patuwo B., Hu M.Y. Forecasting with artificial neural networks. the state of the art. Int. J. Forecast. 1998, 14(1):35-62.
10. Cortez P., Rocha M., Neves J. Time Series Forecasting by Evolutionary Neural Networks 2006, Idea Group Publishing, USA, Chapter III, pp. 47-70.
11. Peralta J., Li X., Sanchez G., Sanchis, A. Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm. Neural Comput. Appl. 2013, 22(1):11-20.
12. Yao X. A review of evolutionary artificial neural networks. Int. J. Intell. Syst. 1993, 8(4):539-567.
13. Abraham A. Meta learning evolutionary artificial neural networks. Neurocomputing 2004, 56:1-38.
14. Rocha M., Cortez P., Neves J. Evolution of neural networks for classification and regression. Neurocomputing 2007, 70:2809-2816.
15. Fogel D.B. Evolutionary Computation. Toward a New Philosophy of Machine Intelligence 1995, IEEE Press, Piscataway, NJ, USA.
16. J. Peralta, G. Gutierrez, A. Sanchis, Adann: automatic design of artificial neural networks, in: Proceedings of the 2008 GECCO Conference Companion on Genetic and Evolutionary Computation, GECCO '08, ACM, NY, USA, 2008, pp. 1863-1870.
17. L. Prechelt, Early stopping-but when?, in: G.B. Orr, K.-R. Müller (Eds.), Neural Networks: Tricks of the Trade, Outgrowth of a 1996 NIPS Workshop, Springer, 1998, pp. 55-69.
18. Mosteller F. A k-sample slippage test for an extreme population. Ann. Math. Stat. 1948, 19(1):58-65.
19. Sarkka S., Vehtari A., Lampinen J. Cats benchmark time series prediction by Kalman smoother with cross-validated noise density. Neurocomputing 2007, 70(13-15):2331-2341.
20. B. Wah, M. Qian, Time-series predictions using constrained formulations for neural-network training and cross validation, in: Proceedings of the International Conference on Intelligent Information Processing, 16th IFIP World Computer Congress, 2000, pp. 220-226.
21. Geman S., Bienenstock E., Doursat R. Neural networks and the bias/variance dilemma. Neural Comput. 1992, 4:1-58.
22. Krogh A., Sollich P. Statistical mechanics of ensemble learning. Phys. Rev. E 1997, 55(1):811-825.
23. Yao X., Islam M. Evolving artificial neural network ensembles. Comput. Intell. Mag. 2008, 3(1):31-42.
24. R. Hyndman, Time series data library, 〈〉 (accessed June, 2011). http://robjhyndman.com/tsdl/.
25. Hyndman R., Koehler A. Another look at measures of forecast accuracy. Int. J. Forecast. 2006, 22(4):679-688.
26. P. Cortez, Sensitivity analysis for time lag selection to forecast seasonal time series using neural networks and support vector machines, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2010), IEEE, Barcelona, Spain, 2010, pp. 3694-3701.