Before revision:  In this research, we create a novel framework  , Dynamic Advisor-Based Ensemble (dynABE) , for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .
After revision:  In this research, we create a novel framework , Dynamic Advisor-Based Ensemble (dynABE) . dynABE explores domain-specific areas based on the companies of interest, diversifies the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy .