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arXiv · 2604.05064

Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series

Annita Vapsi·Penghang Liu·Saheed Obitayo·Aakriti·Manoj Cherukumalli·Prathamesh Patil·Amit Varshney·Nicolas Marchesotti·Elizabeth Fons·Vamsi K. Potluru·Manuela Veloso
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Abstract

Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typically missing realistic inter-channel dependencies. We introduce DynLMC, a Dynamic Linear Model of Coregionalization, that incorporates time-varying, regime-switching correlations and cross-channel lag structures. Our approach produces synthetic multivariate time series with correlation dynamics that closely resemble real data. Fine-tuning three foundational models on DynLMC-generated data yields consistent zero-shot forecasting improvements across nine benchmarks. Our results demonstrate that modeling dynamic inter-channel correlations enhances FMTS transferability, highlighting the importance of data-centric pretraining.

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Annita Vapsi, Penghang Liu, Saheed Obitayo, Aakriti, Manoj Cherukumalli, Prathamesh Patil, Amit Varshney, Nicolas Marchesotti, Elizabeth Fons, Vamsi K. Potluru, Manuela Veloso. 2026-04-06. Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series. https://arxiv.org/abs/2604.05064

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