Synthetic Time Series Generation via Complex Networks

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.

Machine Learning (cs.LG)

arXiv:2601.22879 [cs.LG]

(or arXiv:2601.22879v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2601.22879 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1007/s41060-026-01271-x Focus to learn more DOI(s) linking to related resources

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