Applying XGBoost for Time Series Prediction in Financial Market Data

Time series forecasting is a central theme in financial market, ability to estimate stock prices and trends accurately has a direct impact on investment strategies and risk management decisions. Statistical methods and neural network-based models tend to falter under the nonlinear and erratic nature of financial data. This work is aware of these shortcomings and proposes a new model, Weighted Chameleon Swarm-driven eXtreme Gradient Boosting (WCS-XGBoost), to improve prediction performance in challenging time series cases. Historical stock price data from credible public sources is collected, emphasizing daily closing prices and corresponding technical indicators. The data is normalized, then goes through feature extraction via Principal Component Analysis (PCA) to lower dimensionality while maintaining signal integrity. The predictive engine's central component, WCS-XGBoost, utilizes Chameleon Swarm Optimization to fine-tune XGBoost hyperparameters adaptively, maximizing accuracy and generalization. This framework guarantees that every phase, from raw data aggregation to model training, is optimized for application in financial time series. Metrics like accuracy (98.69%), precision recall, RMSE, and MAPE, outperform traditional models. The suggested system not only offers enhanced predictive power but also presents a scalable solution for market trend analysis and financial decision support. This framework highlights the potential of hybrid evolutionary learning in stock market forecasting methodologies advancement.