Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
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.
dynamic ensemble case-based reasoning artificial neural network knowledge discovery data mining stock price prediction learning system