用复杂网络生成时间序列，能保留分布和短期依赖。
Synthetic Time Series Generation via Complex Networks
- 将时间序列转为分位数图，再逆映射生成新数据。
- 生成数据在统计特征和短期依赖上与真实数据接近。
- 适合需要隐私保护的数据增强场景。
时间序列数据在众多应用中至关重要，但高质量数据集常受限于隐私、采集成本和标注难题。合成时间序列生成成为解决该问题的有前景方法。本文研究基于复杂网络的时间序列生成，聚焦分位数图（QG）表示及其逆映射。尽管逆分位数图（InvQG）映射已有提出，其作为通用数据生成器的潜力尚未系统评估。我们通过全面的实证研究，评估了由InvQG框架生成的合成时间序列在保真度和实用性方面的表现。评估结合统计特征分析、基于网络的拓扑特性以及下游聚类与分类任务的表现，使用模拟和真实世界数据集。结果表明，InvQG在多种模型下有效保留了边缘分布和短期时序依赖，但在捕捉长程或高阶动态方面存在可预测的局限性。
原文摘要 · Abstract (English)
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.
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