TimeFlow: Towards Stochastic-Aware and Efficient Time Series Generation via Flow Matching Modeling
This work solves the problem of efficient and stochastic-aware time series generation for researchers and practitioners in time series mining, representing an incremental improvement by integrating SDE-based flow matching with an encoder-only architecture.
The paper tackled the problem of generating high-quality time series data by addressing the inefficiency of diffusion models and the inability of conventional flow matching to capture stochasticity, resulting in a model that outperforms baselines in quality, diversity, and efficiency across diverse datasets.
Generating high-quality time series data has emerged as a critical research topic due to its broad utility in supporting downstream time series mining tasks. A major challenge lies in modeling the intrinsic stochasticity of temporal dynamics, as real-world sequences often exhibit random fluctuations and localized variations. While diffusion models have achieved remarkable success, their generation process is computationally inefficient, often requiring hundreds to thousands of expensive function evaluations per sample. Flow matching has emerged as a more efficient paradigm, yet its conventional ordinary differential equation (ODE)-based formulation fails to explicitly capture stochasticity, thereby limiting the fidelity of generated sequences. By contrast, stochastic differential equation (SDE) are naturally suited for modeling randomness and uncertainty. Motivated by these insights, we propose TimeFlow, a novel SDE-based flow matching framework that integrates a encoder-only architecture. Specifically, we design a component-wise decomposed velocity field to capture the multi-faceted structure of time series and augment the vanilla flow-matching optimization with an additional stochastic term to enhance representational expressiveness. TimeFlow is flexible and general, supporting both unconditional and conditional generation tasks within a unified framework. Extensive experiments across diverse datasets demonstrate that our model consistently outperforms strong baselines in generation quality, diversity, and efficiency.