OPELM and OPKNN in long-term prediction of time series using projected input data

Neurocomputing

Volumn 73, Issue 10-12, 2010, Pages 1976-1986

  Sovilj, Dušan ^a   Sorjamaa, Antti ^a   Yu, Qi ^a   Miche, Yoan ^a   Séverin, Eric ^b  

^a AALTO UNIVERSITY   (Finland)

^b UNIV LILLE   (France)

Author keywords

Delta test; Genetic algorithm; Hannan Quinn information criterion; Long term time series prediction; OPELM; OPKNN; Projection

Indexed keywords

ACCURATE PREDICTION; CURSE OF DIMENSIONALITY; DATA SETS; EXTREME LEARNING MACHINE; FAST MODEL; FINANCIAL DATA; HANNAN-QUINN INFORMATION CRITERION; INFORMATION CRITERION; INPUT DATAS; INPUT VARIABLES; K-NEAREST NEIGHBORS; LONG-TERM PREDICTION; TIME SERIES PREDICTION;

FUZZY CONTROL; GENETIC ALGORITHMS; TIME SERIES; UNCERTAINTY ANALYSIS;

FORECASTING;

ARTICLE; DATA ANALYSIS; GENETIC ALGORITHM; MACHINE LEARNING; MATHEMATICAL COMPUTING; METHODOLOGY; OPTIMALLY PRUNED EXTREME LEARNING MACHINE; OPTIMALLY PRUNED KAPPA NEAREST NEIGHBORS; PREDICTION; PRIORITY JOURNAL; STATISTICAL MODEL; TIME SERIES ANALYSIS;

EID: 77952552963     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.11.033     Document Type: Article

References (40)

1. Ljung L. System Identification: Theory for the User 1998, Prentice Hall PTR, Upper Saddle River, New Jersey. second ed.
2. Box G., Jenkins G., Reinsel G. Time Series Analysis: Forecasting and Control 1994, Prentice-Hall, Englewood Cliffs, NJ. third ed.
3. Hansen J., Nelson R. Forecasting and recombining time series components by using neural network. Computational Operational Research 2003, 50:307-317.
4. Zhang G., Patuwo E., Hu M. A simulation study of artificial neural network for nonlinear time-series forecasting. Computational Operational Research 2001, 28:381-396.
5. Weigend A., Gershendfeld N. Times Series Prediction: Forecasting the Future and Understanding the Past 1994, Addison-Wesley Publishing Company, Reading, MA.
6. Clements M., Franses P., Swanson N. Forecasting economic and financial time-series with non-linear models. International Journal of Forecasting 2004, 20(2):169-183.
7. M. Verleysen, D. François, The curse of dimensionality in data mining and time series prediction, in: Lecture Notes in Computer Science, vol. 3512, Springer, Heidelberg, 2005, pp. 758-770.
8. Brockwell P., Davis R. Introduction to Time Series and Forecasting 2002, Springer, Berlin. second ed.
9. McNelis P.D. Neural Networks in Finance: Gaining Predictive Edge in the Market 2005, Elsevier Academic Press, Burlington, MA.
10. Kodogiannis V., Lolis A. Forecasting financial time series using neural network and fuzzy system-based techniques. Neural Computing and Applications 2002, 11(2):90-102. URL: 〈http://dx.doi.org/10.1007/s005210200021〉. 10.1007/s005210200021.
11. Alpaydin E. Introduction to Machine Learning 2004, The MIT Press, Cambridge, MA.
12. Guillén A., Sovilj D., Mateo F., Rojas I., Lendasse A. Minimizing the delta test for variable selection in regression problems. International Journal on High Performance Systems Architecture 2008, 1(4):269-281.
13. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning 1989, Addison Wesley, Reading, MA.
14. Guyon I., Gunn S., Nikravesh M., Zadeh L. Feature Extraction: Foundations and Applications 2006, Springer Verlag, New York, USA.
15. D. Sovilj, A. Sorjamaa, Y. Miche, Tabu search with delta test for time series prediction using OP-KNN, in: A. Lendasse (Ed.), ESTSP, European Symposium on Time Series Prediction, 2008, pp. 187-196.
16. Sorjamaa A., Hao J., Reyhani N., Ji Y., Lendasse A. Methodology for long-term prediction of time series. Neurocomputing 2007, 70(16-18):2861-2869.
17. A. Sorjamaa, A. Lendasse, Time series prediction using dirrec strategy, in: M. Verleysen (Ed.), ESANN, European Symposium on Artificial Neural Networks, European Symposium on Artificial Neural Networks, 2006, pp. 143-148.
18. Huang G.-B., Zhu Q.-Y., Siew C.-K. Extreme learning machine: theory and applications. Neurocomputing 2006, 70(1-3):489-501.
19. I. Oh, J. Lee, B. Moon, Local search-embedded genetic algorithm for feature selection, in: ICPR, 2002, pp. II:148-151. URL. http://dx.doi.org/10.1109/ICPR.2002.1048259.
20. Oh I., Lee J., Moon B. Hybrid genetic algorithms for feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(11):1424-1437.
21. Punch W.F., Goodman E.D., Pei M., Chia-Shun L., Hovland P., Enbody R. Further research on feature selection and classification using genetic algorithms. Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA'93) 1993, 557-564. Morgan Kaufmann Publishers, San Mateo, CA. S. Forrest (Ed.).
22. M. Raymer, W. Punch, E. Goodman, L. Kuhn, A. Jain, Dimensionality reduction using genetic algorithms, IEEETEC: IEEE Transactions on Evolutionary Computation, A Publication of the IEEE Neural Networks Council 4, 2000.
23. Y. Saeys, I. Inza, P. Larrañaga, A review of feature selection techniques in bioinformatics, Bioinformatics 23 (19) (2007) 2507-2517, URL. http://dx.doi.org/10.1093/bioinformatics/btm344.
24. F. Mateo, D. Sovilj, R.G. Gironés, A. Lendasse, RCGA-S/RCGA-SP methods to minimize the delta test for regression tasks, in: J. Cabestany, et al. (Eds.), Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain, June 10-12, 2009. Proceedings, Part I, Lecture Notes in Computer Science, vol. 5517, Springer, Berlin-Heidelberg, 2009, pp. 359-366. http://dx.doi.org/10.1007/978-3-642-02478-8.
25. E. Liitiäinen, F. Corona, A. Lendasse, Nearest neighbor distributions and noise variance estimation, in: ESANN 2007, European Symposium on Artificial Neural Networks, Bruges, Belgium, 2007, pp. 67-72.
26. Rao C.R., Mitra S.K. Generalized Inverse of Matrices and Its Applications 1972, John Wiley & Sons Inc, New York, USA.
27. Y. Miche, P. Bas, C. Jutten, O. Simula, A. Lendasse, A methodology for building regression models using extreme learning machine: OP-ELM, in: ESANN 2008, European Symposium on Artificial Neural Networks, Bruges, Belgium, 2008.
28. T. Poggio, F. Girosi, A theory of networks for approximation and learning, Laboratory, Massachusetts Institute of Technology 1140, 1989.
29. B. Caputo, K. Sim, F. Furesjo, A. Smola, Appearance-based object recognition using svms: Which kernel should i use?, in: Proceedings of NIPS Workshop on Statistical Methods for Computational Experiments in Visual Processing and Computer Vision, 2002.
30. T. Similä, J. Tikka, Multiresponse sparse regression with application to multidimensional scaling, in: Artificial Neural Networks: Formal Models and Their Applications-ICANN 2005, vol. 3697/2005, 2005, pp. 97-102.
31. B. Efron, T. Hastie, I. Johnstone, R. Tibshirani, Least angle regression, in: Annals of Statistics, vol. 32, 2004, pp. 407-499.
32. R. Myers, Classical and Modern Regression with Applications, second ed., Duxbury, Pacific Grove, CA, USA, 1990.
33. G. Bontempi, M. Birattari, H. Bersini, Recursive lazy learning for modeling and control, in: European Conference on Machine Learning, 1998, pp. 292-303.
34. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974, 19(6):716-723.
35. Schwarz G. Estimating the dimension of a model. Annals of Statistics 1978, 6:461-464.
36. Bhansali R.J., Downham D.Y. Some properties of the order of an autoregressive model selected by a generalization of Akaike's epf criterion. Biometrika 1977, 64(3):547-551.
37. Hannan E.J., Quinn B.G. The determination of the order of an autoregression. Journal of the Royal Statistical Society 1979, B 41:190-195.
38. Y. Miche, A. Lendasse, A faster model selection criterion for op-elm and op-knn: Hannan-Quinn criterion, in: ESANN'09: European Symposium on Artificial Neural Networks, Bruges, Belgium, 2009.
39. Q. Yu, E. Séverin, A. Lendasse, Variable selection for financial modeling, in: CEF 2007, 13th International Conference on Computing in Economics and Finance, Montréal, Quebec, Canada, 2007, pp. 237-241.
40. Kapler J.K. Measuring the economic rate of return on assets. Review of Industrial Organization 2000, 17(4):457-463.