SDFlow: Similarity-Driven Flow Matching for Time Series Generation

• URL: http://arxiv.org/abs/2605.05736v2
• Date: Mon, 11 May 2026 06:04:49 GMT
• Title: SDFlow: Similarity-Driven Flow Matching for Time Series Generation
• Abstract summary: We propose SDFlow, a non-autoregressive framework that operates entirely in the frozen VQ latent space.<n>We show that SDFlow achieves state-of-the-art performance, improving Discriminative Score and substantially reducing Context-FID.
• Score: 42.61146250886606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vector quantization (VQ) with autoregressive (AR) token modeling is a widely adopted and highly competitive paradigm for time-series generation. However, such models are fundamentally limited by exposure bias: during inference, errors can accumulate across sequential predictions, leading to pronounced quality degradation in long-horizon generation. To address this, we propose SDFlow ($\textbf{S}$imilarity-$\textbf{D}$riven $\textbf{Flow}$ Matching), a non-autoregressive framework that operates entirely in the frozen VQ latent space and enables parallel sequence generation via flow matching. We tackle three key challenges in making this transition: (1) eliminating exposure bias by replacing step-wise token prediction with a global transport map; (2) mitigating the high-dimensionality of VQ token spaces via a low-rank manifold decomposition with a learned anchor prior over the latent manifold; and (3) incorporating discrete supervision into continuous transport dynamics by introducing a categorical posterior over codebook indices within a variational flow-matching formulation. Extensive experiments show that SDFlow achieves state-of-the-art performance, improving Discriminative Score and substantially reducing Context-FID, particularly for challenging long-sequence generation. Moreover, SDFlow provides significant inference speedups over autoregressive baselines, offering both high fidelity and computational efficiency. Code is available at https://anonymous.4open.science/r/SDFlow-D6F3/

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