Enhancing Stock Market Predictions with Multi-Feature Time2Vec-Transformer Models

Accurate financial forecasting is inherently challenging due to the complex and dynamic nature of stock price movements, driven by diverse and interrelated factors. Traditional models often fail to capture both short-term volatility and long-term dependencies adequately. This paper introduces a novel financial prediction model that combines the Transformer architecture with Time2Vec, extending existing methodologies by integrating multiple correlated market features. The approach enhances prediction accuracy and reduces errors in extreme market conditions. Extensive experiments conducted on stock indices, including NASDAQ, S&P 500, and Exxon Mobil, demonstrate that the proposed model significantly outperforms traditional methods such as LSTM and RNN. By leveraging correlated market features, the model effectively captures intricate relationships, leading to improved forecasting performance. The study highlights the advantages of incorporating correlation aware features in financial models and discusses potential applications in broader financial markets. These findings pave the way for developing more robust prediction systems, offering valuable insights for investors, analysts, and policymakers in managing financial risks and opportunities.