ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation

2026-09-27

AI Summary

This study addresses the challenge that existing methods struggle to natively generate irregular time series encompassing sampling structures, observation frequencies, and feature dependencies. To this end, it proposes a unified hierarchical flow matching framework that decomposes the complex joint generation process into structurally aligned subproblems. By sequentially generating observation counts, timestamps, patterns, and feature values from coarse to fine statistical granularity, the approach effectively preserves multi-scale dependencies. Furthermore, this work introduces novel evaluation metrics that capture both sample fidelity and dependency preservation. Extensive experiments across five benchmark datasets demonstrate that the proposed method achieves significantly superior generation fidelity compared to existing baselines, thereby validating the effectiveness of the hierarchical decomposition strategy.

Abstract

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.

Problem

• Irregular Time Series
• Time Series Generation
• Heterogeneous Generation
• Flow Matching

Innovation

• Hierarchical Flow Matching
• Irregular Time Series Generation
• Coarse-to-fine Hierarchy
• Evaluation Metrics
• Joint Generation