arXiv：cs.LG· Jaime Vale, Vanessa Freitas Silva, Maria Eduarda Silva, Fernando Silva·· 3 小时前
基于复杂网络的合成时间序列生成：InvQG 框架评估
Synthetic Time Series Generation via Complex Networks
AI 导读
研究系统评估了逆分位图（InvQG）框架作为通用合成时间序列生成器的潜力，通过统计特征、网络拓扑特性及下游聚类与分类任务，在模拟和真实数据集上检验其保真度与实用性。结果表明，InvQG 能在多种模型上有效保留边际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期的局限。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2601.22879 [cs.LG] |
| (or arXiv:2601.22879v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2601.22879 arXiv-issued DOI via DataCite |
|
| Journal reference: | International Journal of Data Science and Analytics 22, 299 (2026) |
| Related DOI: | https://doi.org/10.1007/s41060-026-01271-x
DOI(s) linking to related resources |
Submission history
From: Vanessa Silva Freitas [view email]
[v1]
Fri, 30 Jan 2026 12:01:50 UTC (23,945 KB)
[v2]
Tue, 7 Jul 2026 22:16:38 UTC (62,528 KB)
[v3]
Thu, 8 Oct 2026 07:48:07 UTC (62,529 KB)
来源：arXiv：cs.LG · arxiv.org