ChronoFlow: 用于不规则时间序列生成的分层流匹配
ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation
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中文总结 AI 辅助
针对不规则时间序列生成，提出统一分层流匹配框架ChronoFlow，按粗到细粒度依次生成观测计数、频率、时间、共观测模式及值，在五个基准上显著提升生成保真度。
中文摘要 AI 辅助
生成建模的最新进展已大幅提升了时间序列生成的质量，然而大多数现有方法要么假设规则的时间网格，要么专注于给定采样结构下的特征动态。这使得它们不适合以其原生形式生成不规则时间序列，因为模型不仅要捕捉特征值，还要捕捉观测发生的次数、发生的时间以及哪些特征被同时观测到。为解决这一异构生成问题，我们提出了ChronoFlow，一个按统计粒度组织的统一分层流匹配框架。遵循从粗到细的层级，ChronoFlow首先生成观测计数和特征级频率，然后联合生成观测时间和特征共观测模式，最后在实现模式条件下生成值。这将一个复杂的联合生成问题转化为结构对齐的子问题，同时保留其依赖关系。为评估完整的不规则时间序列生成，我们引入了涵盖样本真实性、采样结构、值保真度以及时间和跨特征依赖性的补充指标，并通过受控损坏验证了这些指标。在五个基准测试中，ChronoFlow在生成保真度上较现有基线取得了显著改进，而分解研究支持了所提出的层级结构。我们的代码可在以下网址获取：https://this URL。
英文摘要
Recent advances in generative modeling have substantially improved time series generation, yet most existing methods either assume a regular temporal grid or focus on feature dynamics under a given sampling structure. This makes them illsuited for generating irregular time series in their native form, where a model must capture not only feature values, but also how many observations occur, when they occur, and which features are observed together. To address this heterogeneous generation problem, we propose ChronoFlow, a unified hierarchical flow matching framework organized by statistical granularity. Following a coarse-to-fine hierarchy, ChronoFlow first generates observation counts and feature-wise frequencies, then jointly generates observation times and feature co-observation patterns, and finally generates values conditioned on the realized pattern. This turns a complex joint generation problem into structurally aligned subproblems while preserving their dependencies. To evaluate complete irregular time series generation, we introduce complementary metrics spanning sample realism, sampling structure, value fidelity, and temporal and cross-feature dependencies, and validate them through controlled corruptions. Across five benchmarks, ChronoFlow achieves strong improvements in generation fidelity over existing baselines, while factorization studies support the proposed hierarchy. Our code is available at https://anonymous.4open.science/r/ChronoFlow.
