Application Research of Deep Learning in Financial Time Series Prediction

作者：Lin Zou · 发表于：Procedia Computer Science · 年份：2025 · DOI：10.1016/j.procs.2025.04.172 · 被引用次数：2 · 研究领域：Stock Market Forecasting Methods、Time Series Analysis and Forecasting、Energy Load and Power Forecasting

The market’s every twitch has the power to ripple through the entire economic landscape. Think of stock indices as the market’s temperature gauge, signaling whether it’s heating up or cooling down. For the current methods of predicting stock indices using deep learning models, the computational complexity of deep learning models is relatively high. Therefore, how to reduce computational costs without compromising prediction performance has become a key and difficult point for future work. This study only conducted predictive research on market data and did not apply the two deep learning models proposed by our institute to time series data in other fields. Therefore, applying the deep learning model proposed in this article to time series in other non-financial fields is a key direction for future research. This study not only provides a new perspective for predicting markets, but also lays the foundation for further application of deep learning technology in the financial field.