Integrating BERT, GPT, Prophet Algorithm, and Finance Investment Strategies for Enhanced Predictive Modeling and Trend Analysis in Blockchain Technology - Igba Emmanuel,; Moral Kuve Ihimoyan,; Babatunde Awotiwon,; Akinkunmi Rasheed Apampa, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
This paper explores the integration of advanced machine learning models, including BERT, GPT, and the Prophet algorithm, with finance investment strategies to enhance predictive modeling and trend analysis in blockchain technology. The rapid evolution of blockchain has transformed financial ecosystems, offering decentralized platforms for secure and transparent transactions. However, predicting market trends and investment opportunities within this domain remains a complex challenge due to high volatility and the multifaceted nature of financial data. By leveraging the natural language processing capabilities of BERT and GPT for sentiment analysis and market behavior prediction, combined with the time-series forecasting strength of the Prophet algorithm, this study aims to provide a robust framework for analyzing blockchain-driven financial markets. Furthermore, the integration of finance investment strategies ensures practical applicability by aligning machine learning insights with real-world investment decision-making processes. The proposed approach demonstrates potential for optimizing portfolio management, enhancing risk mitigation, and improving strategic investment in blockchain ecosystems. This work bridges the gap between cutting-edge machine learning technologies and financial innovation, offering valuable insights for researchers and practitioners in both domains.
Keywords: Predictive Modeling in Blockchain; BERT and GPT for Sentiment Analysis; Prophet Algorithm for Time-Series Forecasting; Finance Investment Strategies Blockchain Trend Analysis; AI-Driven Financial Insights