HATR-I: Hierarchical Adaptive Temporal Relational Interaction for Stock Trend Prediction

Heyuan Wang, Tengjiao Wang, Shun Li, Shijie Guan

Abstract: Stock trend prediction is a hot issue in the Fintech field. Effective stock profiling is challenging due to highly non-stationary dynamics and complex interplays. Existing methods usually regard each stock independently or detect simplistic homogeneous structures. Practically, stock correlation originates from diverse aspects and underlying relationship signals are implicit in comprehensive graphs. Besides, RNNs are extensively used to simulate stock volatility while inadequate in capturing fine-granular patterns across local time snippets. To this end, in this paper we propose HATR-I, a Hierarchical Adaptive Temporal-Relational Interaction model to characterize and predict stock evolutions. Specifically, we grasp short- and long-term transition regularities of stock dynamics based on cascaded dilated convolutions and gating paths. By formulating different views of domain adjacency graphs into a unified multiplex network with edge attributes, we inject node- and semantic-level dual attention to refine the propagation of inter-stock collaborative information. Particularly, the stock pair matching is proceeding along each time-stage rather than until final compressed representations, meanwhile significant feature points and scales are identified considering the effect of time attenuation. Finally, we deduce latent shared clusters as global regularization to optimize the stock representations. Experiments on three real-world stock market datasets demonstrate the effectiveness of our proposed model.