Artificial Intelligence-Based Forecasting Market Trends and Guiding Investment Decisions

This research seeks to aid investors in forecasting market trends and making educated judgements via the use of an AI-based approach. To begin, financial information will be normalized using a two-stage technique including an Improved Hierarchical Clustering algorithm. This method takes contextual and temporal connections into account. The next step is to use Bi-LSTM, a model that may be utilized for both predication and classification. For precise financial data sequential presentation pattern identification and forecasting, the Bi-LSTM model is trained utilizing both historical and prospective data. The methodology's ability to provide valuable insights for educated investment choices in ever-changing financial markets is shown by validation using historical data, which also shows that it is more accurate than other known approaches. By outlining a comprehensive plan for combining clustering and deep learning methods, this study helps move AI-based financial forecasting forward.