Abstract
Continual Learning (CL) is crucial in artificial intelligence for systems to maintain relevance and effectiveness by adapting to new data while retaining previously acquired knowledge. This study explores the performance of multiple machine learning algorithms in CL tasks across various stock symbol datasets over different years. The algorithms assessed include decision trees, ridge regression, lasso regression, elastic net regression, random forests, support vector machines, gradient boosting, and Long Short-Term Memory (LSTM). These models are evaluated on their ability to incrementally gather and maintain knowledge over time, crucial for continual learning. Performance is measured using Mean Squared Error (MSE) and R-squared metrics to assess predictive precision and data conformity. Additionally, the evaluation extends to consider stability, flexibility, and scalability—important factors for models operating in dynamic environments. This comprehensive analysis aims to identify which algorithms best support the objectives of continual learning by effectively integrating new information without compromising the integrity of existing knowledge.