arXiv:2606.05264v1 Announce Type: new Abstract: Training robust multivariate time series forecasting models requires large, diverse corpora, yet many real-world domains provide only a handful of observed sequences. Existing generators fail to resolve this mismatch: prior-based approaches (e.g., CauKer, TimePFN) produce domain-agnostic samples, while data-driven methods (e.g., TimeGAN) treat references as black-box supervision, forfeiting explicit control over periodic structure, local variability, and cross-variable dynamics. We propose ReGeN, a reference-guided generative pipeline that treats
The increasing demand for robust and data-intensive AI models for forecasting in various domains highlights the current limitations of real-world data availability and existing synthetic data generation methods.
This development allows for the training of more resilient and accurate multivariate time series forecasting models, crucial for optimizing operations, risk assessment, and strategic planning across industries, even with limited real-world data.
The ability to generate high-fidelity, controllable synthetic time series data, specifically addressing periodic structure, local variability, and cross-variable dynamics, fundamentally alters how organizations can develop and deploy forecasting AI.
- · AI/ML researchers
- · Finance industry
- · Supply chain logistics
- · Predictive maintenance sectors
- · Organizations reliant on proprietary, scarce datasets
- · Models struggling with data sparsity
Improved forecasting accuracy leads to better decision-making and operational efficiencies in various sectors.
Reduced barriers to entry for AI model development in data-scarce domains, fostering innovation and competition.
Enhanced resilience of critical infrastructure and financial systems through more reliable predictive analytics, potentially impacting systemic risk.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG