Abstract
Suppose that several forecasters exist for the problem in which class-wise accuracies of forecasting classifiers are important. For such a case, we propose to use a new Bayesian approach for deriving one unique forecaster out of the existing forecasters. Our Bayesian approach links the existing forecasting classifiers via class-based optimization by the aid of an evolutionary algorithm (EA). To show the usefulness of our Bayesian approach in practical situations, we have considered the case of the Korean stock market, where numerous lag-l forecasting classifiers exist for monitoring its status.
Similar content being viewed by others
References
Aci M, Inan C, Avci M (2010) A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm. Expert Syst Appl 37(7):5061–5067
Ahn JJ, Lee SJ, Oh KJ, Kim TY (2009) Intelligent forecasting for financial time series subject to structural changes. Intell Data Anal 13:151–163
Ashlock D (2006) Evolutionary computation for modeling and optimization. Springer, Berlin
Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford Univ. Press, Oxford
Bao D (2008) A generalized model for financial time series representation and prediction. Appl Intell 29:1–11
Beenstock M, Szpiro G (2002) Specification search in nonlinear time-series models using the genetic algorithm. J Econ Dyn Control 26(5):811–835
Berry M, Linoff GF (1997) Data mining techniques. Wiley, New York
Choe H, Kho B, Stulz RM (1999) Do foreign investors destabilize stock markets? The Korean experience in 1997. J Financ Econ 54:227–264
Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9:159–195
Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. University of Michigan Press, Ann Arbor
Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236
Kim TY, Oh KJ, Sohn I, Hwang C (2004) Usefulness of artificial neural networks for early warning system of economic crisis. Expert Syst Appl 26:585–590
Kim W, Wei S (1999) Foreign portfolio investors before and during a crisis. NBER Working Paper No. 6968, Cambridge, MA
McKinnon KIM (1999) Convergence of the Nelder–Mead simplex method to a non-stationary point. SIAM J Optim 9:148–158
Mitchell TM (1997) Machine learning. McGraw Hill, New York
Polikar R (2006) Ensemble based systems in decision making. IEEE Circ Syst Mag 6(3):21–45
Shmueli G, Patel NR, Bruce PC (2006) Data ming for business intelligence. Wiley, New York
Son IS, Oh KJ, Kim TY, Kim DH (2009) An early warning system for global institutional investors at emerging stock markets based on machine learning forecasting. Expert Syst Appl 36:4951–4957
Qian B, Rasheed K (2007) Stock market prediction with multiple classifiers. Appl Intell 26:25–33
Ross B, Zuviria E (2007) Evolving dynamic Bayesian networks with multi-objective genetic algorithms. Appl Intell 26:13–23
Vanderplaats GN (2007) Multidiscipline design optimization. Vanderplaatz R&D
White H (1989) Learning in artificial neural networks: a statistical perspective. Neural Comput 1:425–464
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ahn, J.J., Byun, H.W., Oh, K.J. et al. Bayesian forecaster using class-based optimization. Appl Intell 36, 553–563 (2012). https://doi.org/10.1007/s10489-011-0275-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-011-0275-2