A Novel Whale Optimization Algorithm for Time Series Prediction Using SVM

Abstract

Accurate time series forecasting is essential for decision-making in financial markets, power generation, and economic planning. However, traditional models often struggle to capture the complex nonlinear patterns in financial data, leading to suboptimal predictions. To address this, we propose a novel hybrid approach integrating the Whale Optimization Algorithm (WOA) with Support Vector Machines (SVM) for enhanced stock price forecasting. The WOA-SVM model optimizes key SVM hyperparameters—Regularization Parameter (????) and Kernel Coefficient (????)—while also performing feature selection to improve model generalization. By effectively balancing exploration and exploitation, WOA accelerates convergence, reduces computational complexity, and minimizes forecasting errors. Extensive experiments on S&P 500 and NIFTY 50 datasets confirm WOA-SVM’s superiority over SVM, LSTM, and Random Forest Regression, achieving the lowest MSE (2.45) and RMSE (1.56). These results highlight WOA-SVM as a robust and efficient tool for financial market forecasting, offering valuable insights to investors, analysts, and financial institutions.