SCAlign: Transaction Event Prediction via Multi-Scale Market Dynamics Alignment

Event prediction plays a pivotal role in analyzing consumer behavior for inventory and pricing optimization. In dynamic financial markets, customer behavior is often influenced by price commitment policies, where the historical and pre-announced future transaction price dynamics can lead to complex behavior patterns, such as advance consumption or delayed purchasing. Therefore, these phenomena pose significant challenges to traditional event modeling approaches that rely solely on consumer behaviors. To address this problem, we propose SCAlign, a cross-domain and multi-scale framework for market dynamics alignment, designed for event prediction. Our model integrates both heterogeneous historical and limited observable future commitment prices at different scales, aligning customer behavior with market fluctuations across multiple time scales. Finally, through a Mixture-of-Experts (MoE) framework, the model dynamically fuses these aligned features, enabling adaptive selection of the relevant and appropriate representations for prediction tasks. Empirical evaluations across diverse transaction environments demonstrate that our model outperforms state-of-the-art prediction baselines. Furthermore, it achieves optimal performance across varying data scales, showcasing its robustness and generalizability.