UFO模型加速不规则时间序列概率预测，兼具全局感知与局部敏感性。
U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Series
- 融合U-Net并行特征提取、Transformer全局建模与Neural CDE连续动态建模
- 在5个基准上优于10个先进模型，长序列预测准确率提升显著
- 推理速度比传统Neural CDE快15倍，适合高维多变量长期预测场景
不规则采样时间序列的概率预测在医疗和金融等领域至关重要，但仍是重大挑战。现有神经控制微分方程（Neural CDE）方法虽能有效建模连续动态，但计算固有串行，限制可扩展性且难以获取全局上下文。我们提出UFO（U-Former ODE），一种新型架构，无缝整合U-Net的并行多尺度特征提取、Transformer的强大全局建模能力以及Neural CDE的连续时间动态特性。通过构建完全因果且可并行化的模型，UFO在保持对局部时间动态强敏感性的同时实现全局感受野。在五个标准基准（涵盖规则与不规则采样时间序列）上的大量实验表明，UFO始终优于十个先进神经基线，在预测精度上表现优异。此外，UFO相比传统Neural CDE推理速度最高提升15倍，对长序列和高维多变量序列均保持稳定高性能。
原文摘要 · Abstract (English)
Probabilistic forecasting of irregularly sampled time series is crucial in domains such as healthcare and finance, yet it remains a formidable challenge. Existing Neural Controlled Differential Equation (Neural CDE) approaches, while effective at modelling continuous dynamics, suffer from slow, inherently sequential computation, which restricts scalability and limits access to global context. We introduce UFO (U-Former ODE), a novel architecture that seamlessly integrates the parallelizable, multiscale feature extraction of U-Nets, the powerful global modelling of Transformers, and the continuous-time dynamics of Neural CDEs. By constructing a fully causal, parallelizable model, UFO achieves a global receptive field while retaining strong sensitivity to local temporal dynamics. Extensive experiments on five standard benchmarks -- covering both regularly and irregularly sampled time series -- demonstrate that UFO consistently outperforms ten state-of-the-art neural baselines in predictive accuracy. Moreover, UFO delivers up to 15$\times$ faster inference compared to conventional Neural CDEs, with consistently strong performance on long and highly multivariate sequences.
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