Dynamic adaptive ensemble case-based reasoning: application to stock market prediction

Authors:
Se-Hak Chun;Yoon-Joo Park
Affiliations:
Hallyn University, Department of Business Administration, 1 Okchun-dong, Chunchun City, Kangwon-do, 200-702, Chunchun, Republic of Korea;KAIST, 207-43 Cheongryangri-dong, Dongdaemoon-gu, Seoul 130-012, Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2005

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Abstract

This paper proposes a new learning technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR). The main idea of DAE CBR originates from finding combinations of parameter and updating and applying an optimal CBR model to application or domain area. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index.