通过图结构神经流建模多变量时间序列的变量间交互关系，提升分类性能。
One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
- 引入双辅助轨迹自监督策略，增强变量间交互学习。
- 在五个真实数据集上达到顶尖分类效果，训练效率高。
- 适合处理不规则多变量时间序列的建模与分类任务。
神经流通过神经网络直接学习常微分方程（ODE）解轨迹，高效建模不规则多变量时间序列，无需逐步数值求解。然而，现有方法多将变量独立处理，忽视变量间交互；且其单步映射机制使交互建模困难，缺乏迭代优化过程。为此，本文提出一阶段图结构神经流（GSNF），引入两种辅助轨迹自监督策略：(i) 通过重初始化实现感知交互的轨迹生成，诱导轨迹发散以暴露图结构带来的交互效应，并给出发散的理论下界；(ii) 反向时间轨迹生成，利用流的可逆性强制前后向一致性，正则化图学习。在五个真实世界数据集上的实验表明，GSNF在分类性能上达到当前最优，同时保持极低的训练时间和内存开销。
原文摘要 · Abstract (English)
Neural Flows efficiently model irregular multivariate time series by directly learning ODE solution trajectories with neural networks, bypassing step-by-step numerical solvers. Despite their efficiency, many existing approaches treat variables independently, leaving inter-variable interactions underexplored. Moreover, their one-step mapping makes interaction modeling inherently challenging, as it removes the iterative refinement of interactions during learning. To address this challenge, we propose one-step Graph-Structured Neural Flows (GSNF), which introduce two auxiliary-trajectory self-supervision strategies to strengthen interaction learning: (i) interaction-aware trajectory generation via re-initialization, which induces trajectory divergence to expose graph-induced interactions, with a theoretically derived lower bound on divergence; and (ii) reverse-time trajectory generation, which enforces forward-backward consistency to regularize graph learning, enabled by flow invertibility. Experiments on five real-world datasets show that GSNF achieves state-of-the-art classification performance with highly competitive training time and memory usage.
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