Multi-Granularity Spatio-Temporal Correlation Networks for Stock Trend Prediction

Abstract

In recent years, time series forecasting has been widely used in various fields, especially in financial markets. Stock trend forecasting has become one of the most common and complex challenges faced by investors and researchers. However, much of the current research relies primarily on single-granularity stock data for forecasting, with relatively few studies on multi-granularity data and fewer studies on spatial correlation of multi-granularity data. This inherent limitation restricts the comprehensive extraction of valuable information. To address this challenge, we propose the Multi-Granularity Deep Spatio-Temporal Correlation Framework (MDSTCF). Our approach combines the strengths of a multi-granularity residual learning, gated recurrent units, and graph attention networks to extract spatio-temporal information specific to each granularity. Subsequently, predictions at each granularity are generated through the prediction layer. Finally, a soft attention mechanism is employed to assign weights to the predictions at each granularity to obtain the final result. Comprehensive experiments conducted on tow stock datasets show that the proposed forecasting model improves the F1 score by about 7.88% and 11.2%, and the cumulative relative returns are close to 80% and 40%, respectively, compared to the previously studied time series forecasting models. The results clearly indicates that fusing multi-granularity information can significantly improve the performance of time series forecasting.

Keywords

• Time series forecasting
• multi-granularity learning
• graph neural network
• spatio-temporal correlation
• financial markets