Evaluating AI-Driven Models for Stock Price Prediction A Comprehensive Performance Analysis
DOI:
https://doi.org/10.32628/CSEIT25113392Keywords:
Machine Learning, ANN, LSTM, Stock Price Prediction, Stock Market, Predictive AnalyticsAbstract
Artificial Intelligence has revolutionized financial forecasting by enhancing accuracy and efficiency, particularly in stock price prediction. This paper evaluates AI-driven models, focusing on their performance, strengths, and limitations in prediction of stock price. Models of ANN are analyze through specify methodology integrates advanced techniques such as feature engineering, hyperparameter tuning, and ensemble learning. Results indicate significant improvements in prediction accuracy compared to traditional statistical methods. The study concludes with recommendations for optimizing AI models and exploring emerging techniques like Reinforcement Learning (RL).
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