时间序列预测从业者终于有了一个能处理异构多变量数据的专用基础模型——Falcon-X 通过潜在原型空间解决了语义对齐难题，做金融、能源或物联网预测的团队可以直接拿来用。
Falcon-X 是一种新型时间序列基础模型，解决了现有模型在跨变量建模中语义对齐和关系表达能力的不足。它通过将变量映射到统一的潜在原型空间，并采用统一原型差异注意力机制，显式评估正负语义亲和度，从而对齐异构物理量。该模型还通过潜在实体注意力在共享空间中高效进行跨变量交互，并通过变量重组路由器稳健重建特定变量轨迹。在 GIFT-Eval 和 fev-bench 基准测试中，Falcon-X 达到了最先进的预测性能，为复杂多变量环境提供了可扩展的范式。该模型已公开发布，以支持未来研究。
Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
Time series foundation models (TSFMs) are transforming the forecasting paradigm through large-scale cross-domain pretraining. However, most existing TSFMs remain univariate, and recent efforts to enable cross-variate modeling still operate directly within the raw variate space. This design introduces fundamental limitations in semantic alignment and relational expressivity. Specifically, raw-space group mixing lacks a dedicated mechanism to align heterogeneous physical quantities, while standard non-negative attention fails to capture the complex synergistic and antagonistic interactions ubiquitous in real-world systems. To address these challenges, we propose Falcon-X, decouples variates from the raw space and maps them into a unified latent prototype space. Falcon-X employs a Unified Prototype Diff-Attention mechanism that explicitly evaluates both positive and negative semantic affinities to explicitly align heterogeneous variates. Cross-variate interactions are then efficiently performed within this shared space via Latent Entity Attention, naturally facilitating zero-shot structural transfer. Finally, a Variate Reassembly Router robustly reconstructs variate-specific trajectories via a request-and-dispatch mechanism. Extensive evaluations on the GIFT-Eval and fev-bench benchmarks demonstrate that Falcon-X achieves state-of-the-art forecasting performance, offering a principled and scalable paradigm for complex multivariate environments. Falcon-X is publicly released to support future research.