Synthetic Time Series Generation via Complex Networks

• URL: http://arxiv.org/abs/2601.22879v1
• Date: Fri, 30 Jan 2026 12:01:50 GMT
• Title: Synthetic Time Series Generation via Complex Networks
• Authors: Jaime Vale, Vanessa Freitas Silva, Maria Eduarda Silva, Fernando Silva,
• Abstract summary: We present a framework for generating synthetic time series by leveraging complex networks mappings.<n>We investigate whether time series transformed into Quantile Graphs (QG) -- and then reconstructed via inverse mapping -- can produce synthetic data.<n>Results indicate that our quantile graph-based methodology offers a competitive and interpretable alternative for synthetic time series generation.
• Score: 39.146761527401424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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