FORECASTING FUTURE TRENDS: A GENERATIVE AI APPROACH TO DYNAMIC TREND PREDICTION

In the rapidly evolving digital landscape, trend forecasting has become a critical task for decision-makers across industries. Traditional methods struggle with adaptability, scalability, and real-time trend identification. This paper presents a novel framework that integrates Generative AI with the Proposed Guided Remora Optimization Algorithm (PGROA) to enhance trend prediction accuracy while maintaining robustness across dynamic and multimodal datasets. The framework leverages transformer-based architectures for feature extraction, adaptive learning mechanisms for real-time updates, and cross-domain generalization techniques to ensure scalability. Additionally, interpretability methods such as SHAP values and attention mechanisms provide transparency in model predictions. The proposed system is evaluated on diverse datasets, demonstrating superior performance with an accuracy of 94.8%, an F1-score of 93.8%, and a significantly reduced RMSE of 0.072, outperforming existing deep learning and hybrid models. This research establishes a scalable and interpretable AI-driven approach to trend prediction, equipping decision-makers with actionable insights for dynamic environments.