3 accepted papers
Effectively modeling cross-variate dependencies is a central, yet challenging, task in multivariate time series forecasting. While attention-based methods have advanced the state-of-the-art by capturing global cross-variate dependencies, their quadratic complexity with respect to the number of varia…
Portfolio optimization encounters persistent challenges in adapting to dynamic markets due to static assumptions and high-dimensional decision spaces. Although reinforcement learning (RL) has emerged as a potential solution, conventional reward engineering often fails to capture complex market dynam
This paper proposes a novel approach to predicting stock returns based on Relational Temporal Graph Neural Networks (RTGNN). This method provides insights into the dynamic and asymmetric relationships that exist both among different stocks and within individual stocks over time, addressing the limit…