时间序列数据对众多应用至关重要，但高质量数据集的获取常受限于隐私顾虑、采集成本及标注难题。合成时间序列生成已成为应对上述限制的一种有前景的方法。本研究探讨利用复杂网络映射进行合成时间序列生成，重点关注分位数图（Quantile Graph, QG）表示及其逆映射。尽管逆QG映射此前已被提出，但其作为通用数据生成器的潜力尚未得到系统性评估。我们通过一项全面的实证研究填补这一空白，从保真度与实用性两方面评估逆分位数图（InvQG）框架所生成合成时间序列的性能。评估结合统计特征分析、基于网络的拓扑特性分析，以及在下游聚类与分类任务中的表现，并使用模拟数据集与真实世界数据集进行验证。结果表明，InvQG能有效保持多种模型下的边缘分布及短期时间依赖性，但在捕捉长程依赖或高阶动力学方面表现出可预期的局限性。

Time series data are essential for a wide range of applications, yet access to high-quality datasets is often constrained by privacy concerns, acquisition costs, and labelling challenges. Synthetic time series generation has emerged as a promising approach to address these limitations. In this work, we investigate the use of complex network mappings for synthetic time series generation, focusing on the Quantile Graph (QG) representation and its inverse. While the inverse QG mapping has been previously proposed, its potential as a general-purpose data generator has not been systematically evaluated. We address this gap through a comprehensive empirical study assessing both the fidelity and utility of synthetic time series generated by the Inverse Quantile Graph (InvQG) framework. The evaluation combines statistical feature analysis, network-based topological characteristics, and performance in downstream clustering and classification tasks, using simulated and real-world datasets. The results show that InvQG effectively preserves marginal distributions and short-term temporal dependencies across a wide range of models, while exhibiting predictable limitations in capturing long-range or higher-order dynamics.