Abstract
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.
Abstract (translated)
时间序列生成（TSG）在医疗保健等众多领域中扮演着关键角色。然而，大多数现有方法假设观察数据是规则采样的，并且输出分辨率是固定的，这往往与现实世界场景不一致，在现实世界场景中，数据通常是不规则和稀疏的采样。这种偏差特别令人担忧的应用是在临床监测等领域，其中不规则测量必须支持下游任务所需的连续性和高分辨率的时间序列。神经控制微分方程（NCDEs）显示出强大的潜力来建模不规则时间序列，但仍面临捕捉复杂动态模式以及支持持续TSG方面的挑战。 为了克服这些局限性，我们提出了MN-TSG框架，该框架探索了基于混合专家(MoE)的NCDE架构，并将它们与现有的TSG模型集成在一起，以处理不规则和连续生成任务。MN-TSG的核心是一个具有动态参数化专家功能的MoE-NCDE架构和一个解耦设计，有助于更有效地优化MoE动力学。此外，我们利用现有的时间序列生成模型来学习混合专家及其产生的时间序列之间的联合分布。这不仅使框架能够生成新的样本，还能够为每个样本提供适当的专家配置，从而支持精细的连续TSG。 在十个公开和合成数据集上的广泛实验表明了MN-TSG的有效性，在不规则到规则以及不规则到持续的时间序列生成任务上始终优于强大的基线模型。
URL
https://arxiv.org/abs/2601.13534
