Enhanced Stock Price Forecasting Using Vector Regression and Interactive Visualization

This research explores an innovative approach to stock price forecasting by integrating Support Vector Regression (SVR) with advanced data preprocessing and visualization techniques. Leveraging historical stock data, the proposed methodology preprocesses the dataset to extract relevant features and optimizes SVR parameters through rigorous hyperparameter tuning. The trained SVR model accurately predicts future stock prices, facilitating informed investment decisions. Additionally, interactive data visualization tools are employed to present forecasted results alongside actual stock prices, enhancing interpretability and facilitating actionable insights for stakeholders. Experimental evaluations demonstrate the effectiveness of the proposed approach in achieving high prediction accuracy and providing valuable forecasts for financial markets. The findings underscore the significance of SVR-based forecasting models augmented by interactive visualization for improved decision-making in stock trading and investment scenarios.