Synthetic Time Series Generation via Complex Networks
This addresses data scarcity issues in machine learning applications, but it is incremental as it builds on existing synthetic generation techniques.
The paper tackled the problem of limited access to high-quality time series data by proposing a framework for synthetic generation using complex networks mappings, specifically Quantile Graphs, and found it offers a competitive alternative to GAN methods.
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.