Stock Price Prediction Using Transformers

Stock markets are known for their volatility, as prices can quickly change due to various factors like company performance and geopolitical events. Predictive models face challenges in capturing the complex patterns driving these fluctuations. This paper introduces a new transformer-based approach to stock price prediction, utilizing self-attention mechanisms to understand long-term dependencies and intricate patterns in financial data. The model is specifically designed to handle the rapid changes and volatility seen in stock markets, making it suitable for real-time prediction tasks. With a multi-head attention architecture, the transformer efficiently focuses on different parts of the input sequence, prioritizing the most relevant factors influencing stock prices.