Integrating event information and multi dimensional relationships for improved financial time series forecasting.

ABSTRACT: Financial time series prediction is extremely challenging due to the intertwined effects of market narratives and complex inter-asset relationships. Traditional prediction models often fail to distinguish similar price patterns driven by different underlying causes, limiting their predictive accuracy in practical scenarios. To address these limitations, this study proposes the Dual-stream Alpha Factor Fusion Network (DAFF-Net), an innovative deep learning framework that integrates event-driven temporal pattern extraction with multi-dimensional relationship-aware channel soft clustering. The event-driven temporal pattern extractor employs an event-aware router to fuse time series data with contextual event information encoded from news, corporate announcements, and macroeconomic data, enabl

SUBMITTER: Du X 

PROVIDER: S-EPMC12578950 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature