Leveraging Machine Learning for Real-Time Financial Forecasting and Budget Optimization: Applying Regression Models, and Reinforcement Learning Techniques

The modern world, dynamic planning and accurate forecasting for dynamic datasets are essential for the practice of financial management. Often models used in traditional problems are not adaptable nor accurate when used in real time decision making. The objective of this study is to construct such a robust and adaptive framework by combining advanced machine learning techniques, to improve forecasting accuracy also to enable real time budget allocation. The structure of this research is different from other models as it presents a hybrid architecture that integrates Gradient Boosting Regression (GBR) to provide accurate financial forecasting with Deep Q Network (DQN) to dynamically allocate a budget. It presents the novelty of hyperparameter tuning, feature selection and stabilization of stand-alone model’s techniques using reinforcement learning. The framework is implemented in Python and uses Hyperparameter tuning and feature engineering to enhance GBR performance, outperforming LSTM, Random Forest, SVM as well as the Linear Regression. Experienced Replay, Target Networks and Bayesian Optimization for parameters fine tuning for real time adaptation have been integrated into the DQN component to make it stable. Even under dynamic financial scenarios, the hybrid model performs 10.0 percent better on average across dimensions of forecast accuracy and responsiveness in the reallocation of budget when compared to the axes in all horseshoe. The proposed system significantly improves the forecasting precision and the real time financial decision making. In other words, it is a new paradigm of integrated, intelligent financial platforms to form predictions and predations in financial planning.