Generative modeling offers a promising solution to data scarcity and privacy challenges in time series analysis. However, the structural complexity of time series, characterized by multi-scale temporal patterns and heterogeneous components, remains insufficiently addressed. In this work, we propose a structure-disentangled multiscale generation framework for time series. Our approach encodes sequences into discrete tokens at multiple temporal resolutions and performs autoregressive generation in a coarse-to-fine manner, thereby preserving hierarchical dependencies. To tackle structural heterogeneity, we introduce a dual-path VQ-VAE that disentangles trend and seasonal components, enabling the learning of semantically consistent latent representations. Additionally, we present a guidance-based reconstruction strategy, where coarse seasonal signals are utilized as priors to guide the reconstruction of fine-grained seasonal patterns. Experiments on six datasets show that our approach produces higher-quality time series than existing methods. Notably, our model achieves strong performance with a significantly reduced parameter count and exhibits superior capability in generating high-quality long-term sequences. Our implementation is available at https://anonymous.4open.science/r/TimeMAR-BC5B.

翻译：生成建模为时间序列分析中的数据稀缺性和隐私挑战提供了一种有前景的解决方案。然而，时间序列的结构复杂性，其特征是多尺度时间模式和异质成分，仍未得到充分解决。在这项工作中，我们提出了一种用于时间序列的结构解耦多尺度生成框架。我们的方法将序列在多个时间分辨率下编码为离散标记，并以从粗到细的方式执行自回归生成，从而保留层次依赖关系。为了解决结构异质性，我们引入了一种双路径VQ-VAE，它将趋势成分和季节成分解耦，从而能够学习语义一致的潜在表示。此外，我们提出了一种基于引导的重建策略，其中粗粒度的季节信号被用作先验来指导细粒度季节模式的重建。在六个数据集上的实验表明，我们的方法比现有方法生成了更高质量的时间序列。值得注意的是，我们的模型以显著减少的参数数量实现了强大的性能，并在生成高质量长期序列方面表现出卓越的能力。我们的实现可在 https://anonymous.4open.science/r/TimeMAR-BC5B 获取。