Time series generation (TSG) plays a critical role in a wide range of domains, such as healthcare. However, most existing methods assume regularly sampled observations and fixed output resolutions, which are often misaligned with real-world scenarios where data are irregularly sampled and sparsely observed. This mismatch is particularly problematic in applications such as clinical monitoring, where irregular measurements must support downstream tasks requiring continuous and high-resolution time series. Neural Controlled Differential Equations (NCDEs) have shown strong potential for modeling irregular time series, yet they still face challenges in capturing complex dynamic temporal patterns and supporting continuous TSG. To address these limitations, we propose MN-TSG, a novel framework that explores Mixture-of-Experts (MoE)-based NCDEs and integrates them with existing TSG models for irregular and continuous generation tasks. The core of MN-TSG lies in a MoE-NCDE architecture with dynamically parameterized expert functions and a decoupled design that facilitates more effective optimization of MoE dynamics. Furthermore, we leverage existing TSG models to learn the joint distribution over the mixture of experts and the generated time series. This enables the framework not only to generate new samples, but also to produce appropriate expert configurations tailored to each sample, thereby supporting refined continuous TSG. Extensive experiments on ten public and synthetic datasets demonstrate the effectiveness of MN-TSG, consistently outperforming strong TSG baselines on both irregular-to-regular and irregular-to-continuous generation tasks.

翻译：时间序列生成（TSG）在医疗健康等诸多领域发挥着关键作用。然而，现有方法大多假设观测数据为规则采样且输出分辨率固定，这与现实场景中数据不规则采样和稀疏观测的情况往往不符。这种不匹配在临床监测等应用中尤为突出，其中不规则的测量数据必须支持需要连续高分辨率时间序列的下游任务。神经控制微分方程（NCDE）在建模不规则时间序列方面已展现出强大潜力，但在捕捉复杂动态时序模式以及支持连续TSG方面仍面临挑战。为应对这些局限，我们提出MN-TSG，一个新颖的框架，它探索基于专家混合（MoE）的NCDE，并将其与现有TSG模型集成，以处理不规则和连续的生成任务。MN-TSG的核心在于一个具有动态参数化专家函数和解耦设计的MoE-NCDE架构，该设计有助于更有效地优化MoE动态。此外，我们利用现有TSG模型学习专家混合与生成时间序列的联合分布。这使得该框架不仅能生成新样本，还能为每个样本生成合适的专家配置，从而支持精细化的连续TSG。在十个公开和合成数据集上的大量实验证明了MN-TSG的有效性，其在“不规则到规则”和“不规则到连续”的生成任务上均持续优于强TSG基线。