Time series data are essential for a wide range of applications, particularly in developing robust machine learning models. However, access to high-quality datasets is often limited due to privacy concerns, acquisition costs, and labeling challenges. Synthetic time series generation has emerged as a promising solution to address these constraints. In this work, we present a framework for generating synthetic time series by leveraging complex networks mappings. Specifically, we investigate whether time series transformed into Quantile Graphs (QG) -- and then reconstructed via inverse mapping -- can produce synthetic data that preserve the statistical and structural properties of the original. We evaluate the fidelity and utility of the generated data using both simulated and real-world datasets, and compare our approach against state-of-the-art Generative Adversarial Network (GAN) methods. Results indicate that our quantile graph-based methodology offers a competitive and interpretable alternative for synthetic time series generation.

翻译：时间序列数据在众多应用领域中至关重要，特别是在开发鲁棒机器学习模型时。然而，由于隐私问题、采集成本和标注挑战，高质量数据集的获取往往受限。合成时间序列生成已成为应对这些限制的一种有前景的解决方案。本研究提出一种利用复杂网络映射生成合成时间序列的框架。具体而言，我们探究了将时间序列转换为分位数图，再通过逆映射重构后，能否生成保持原始数据统计与结构特性的合成数据。我们使用模拟和真实世界数据集评估生成数据的保真度与实用性，并将本方法与最先进的生成对抗网络方法进行对比。结果表明，基于分位数图的方法为合成时间序列生成提供了一种具有竞争力且可解释的替代方案。