用流匹配方法实现多变量时间序列的概率预测，能捕捉复杂分布且无需模拟。
Probabilistic Forecasting via Autoregressive Flow Matching
- 通过共享流变换建模未来轨迹的条件分布，分步生成。
- 在多个动态系统和真实任务上表现优异，不确定性估计更准确。
- 适合需要高精度概率预测的场景，如交通、金融等复杂系统。
本文提出FlowTime，一种用于多变量时间序列概率预测的生成模型。给定历史观测值及可选的未来协变量，将预测问题转化为从学习到的未来轨迹条件分布中采样。具体地，将未来观测的联合分布分解为一系列条件密度，每个密度由共享的流变换实现，该变换将简单先验分布映射为下一个观测的分布，条件于已知协变量。为此，我们采用流匹配（Flow Matching, FM）框架，实现可扩展且无需模拟的学习。结合这种因子化结构与FM目标，FlowTime兼具自回归模型的优势——包括强外推能力、模型紧凑和校准良好的不确定性估计，同时还能捕捉现代基于运输的生成模型中的复杂多模态条件分布。我们在多个动力系统和真实世界预测任务上验证了FlowTime的有效性。
原文摘要 · Abstract (English)
In this work, we propose FlowTime, a generative model for probabilistic forecasting of multivariate timeseries data. Given historical measurements and optional future covariates, we formulate forecasting as sampling from a learned conditional distribution over future trajectories. Specifically, we decompose the joint distribution of future observations into a sequence of conditional densities, each modeled via a shared flow that transforms a simple base distribution into the next observation distribution, conditioned on observed covariates. To achieve this, we leverage the flow matching (FM) framework, enabling scalable and simulation-free learning of these transformations. By combining this factorization with the FM objective, FlowTime retains the benefits of autoregressive models -- including strong extrapolation performance, compact model size, and well-calibrated uncertainty estimates -- while also capturing complex multi-modal conditional distributions, as seen in modern transport-based generative models. We demonstrate the effectiveness of FlowTime on multiple dynamical systems and real-world forecasting tasks.
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