RecFlowTime: Self-Conditioned Rectified Flow for Time-Series Generation

Flow matching offers an efficient alternative to diffusion models by learning continuous transport paths from noise to data that can be integrated in relatively few steps. We introduce RecFlowTime, a rectified-flow framework for time-series generation that learns velocities along linear data–noise interpolations. A bidirectional Transformer with rotary position embeddings represents temporal relationships, while self-conditioning progressively refines the estimated clean sequence. During training, minibatch optimal-transport coupling replaces independent pairing, reducing path crossings and producing straighter sampling trajectories. A floored minimum signal-to-noise ratio (min-SNR) weight preserves supervision near the low-noise endpoint and reduces sample roughness. Across four synthetic, financial, sensor, and biomedical benchmarks, RecFlowTime outperforms recent baselines on key measures of distributional fidelity and temporal realism. In addition, RecFlowTime uses only 20 function evaluations, compared with 200–500 for the diffusion baselines, resulting in more than an order-of-magnitude faster sampling and up to a 79% reduction in training time. Although trained unconditionally, RecFlowTime also supports conditional generation for imputation and forecasting without retraining or additional model evaluations.