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

作者：

Highlights：

•

摘要

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.

论文关键词：Dynamic ensemble case-based reasoning,Artificial neural network,Knowledge discovery,Data mining,Stock price prediction,Learning system

论文评审过程：Available online 6 January 2005.

论文官网地址：https://doi.org/10.1016/j.eswa.2004.12.004