arXiv:2601.22879v2 Announce Type: replace 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-purp
The increasing demand for high-quality, diverse datasets across various AI domains, coupled with growing privacy concerns, makes synthetic data generation a critical area of research at this moment.
This development offers a method to overcome significant bottlenecks in data acquisition and access, enabling faster development and deployment of AI models especially in sensitive applications.
The ability to generate high-fidelity synthetic time series data using complex network mappings reduces reliance on scarce or private real-world data, facilitating broader AI innovation.
- · AI/ML researchers
- · Healthcare and finance industries
- · Data privacy solution providers
- · Companies with limited proprietary datasets
- · Traditional data brokers
- · Organizations heavily reliant on proprietary real data advantage
Improved model generalizability and robustness due to diverse training data.
Accelerated AI development cycles and reduced costs associated with data collection and labeling.
New ethical challenges concerning the indistinguishability of synthetic data from real data and its potential misuse in generating misinformation.
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