Bayesian forecaster using class-based optimization

Authors:
Jae Joon Ahn;Hyun Woo Byun;Kyong Joo Oh;Tae Yoon Kim
Affiliations:
Department of Information & Industrial Engineering, Yonsei University, Seoul, South Korea 120-749;Department of Information & Industrial Engineering, Yonsei University, Seoul, South Korea 120-749;Department of Information & Industrial Engineering, Yonsei University, Seoul, South Korea 120-749;Department of Statistics, Keimyung University, Daegu, South Korea 704-701
Venue:
Applied Intelligence
Year:
2012

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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.