Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series

• URL: http://arxiv.org/abs/2604.05064v1
• Date: Mon, 06 Apr 2026 18:11:20 GMT
• Title: Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
• Abstract summary: We introduce DynLMC, a Dynamic Linear Model of Coregionalization, that incorporates time-varying, regime-switching correlations and cross-channel lag structures.<n>Our approach produces synthetic multivariate time series with correlation dynamics that closely resemble real data.
• Score: 11.889040575479372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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