发表机构
East China Normal University（华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HALO提出超球面VAE约束连续潜变量并采用掩码自回归模型，解决时间序列生成中信息丢失与误差累积问题，实现最先进性能并大幅提升推理效率。
AI 中文摘要
大多数现有的时间序列生成器依赖于两阶段建模范式：第一阶段学习时间序列的离散潜变量表示；第二阶段通过下一个词元预测对这些离散潜变量进行自回归建模。然而，这种范式存在两个阶段特有的局限性：第一阶段在将连续时间序列离散化时可能导致信息丢失，而第二阶段在自回归生成过程中容易出现误差累积。为了解决这些局限性，我们的核心思想是在连续潜空间中采用更高效的自回归框架进行生成建模。我们提出了HALO，通过超球面潜变量和掩码自回归建模来增强时间序列生成，以实现这一目标，同时解决两个关键瓶颈：（1）连续潜变量的方差和尺度异质性；（2）在生成效率与时间相关性建模之间取得平衡的困难。HALO首先引入了一个超球面VAE，将连续潜变量约束在固定半径的超球面壳上，有效稳定了连续潜变量的数值波动。其次，我们开发了一种掩码自回归模型，在并行解码与时间相关性学习之间取得平衡，大幅减少了生成所需的推理步数，并提高了生成稳定性。我们的大量实验表明，HALO在实现最先进生成性能的同时，相较于现有先进基线，推理效率显著提升。
英文摘要
Most existing time series generators rely on a two-stage modeling paradigm: the first stage learns discrete latent representations of time series; the second stage performs autoregressive modeling on these discrete latents through next token prediction. However, this paradigm suffers from two stage-specific limitations: the first stage can lead to information loss when discretizing continuous time series, while the second stage is prone to error accumulation during autoregressive generation. To address these limitations, our core idea is to perform generative modeling in a continuous latent space with a more efficient autoregressive framework. We propose HALO, which enhances time series generation via Hyperspherical Latents and Masked Autoregressive modeling to achieve this goal by tackling two key bottlenecks: (1) variance and scale heterogeneity of continuous latent representations; (2) the difficulty of balancing generation efficiency with temporal correlation modeling. HALO first introduces a hyperspherical VAE that constrains continuous latents to a fixed-radius hyperspherical shell, effectively stabilizing the numerical fluctuations of continuous latent representations. Secondly, we develop a masked autoregressive model that balances parallel decoding and temporal correlation learning, substantially reducing the number of inference steps required for generation and improving generation stability. Our extensive experiments demonstrate that HALO achieves state-of-the-art generation performance while offering significantly improved inference efficiency over existing advanced baselines.
