Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
Summary: arXiv:2604.05064v1 Announce Type: cross
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.
Introduction
As the demand for robust predictive analytics continues to grow across various industries, the need for high-quality synthetic data has never been more critical. Synthetic data serves as a valuable resource for developing and training foundation models for time series analysis. However, traditional synthetic data generators often overlook the complexities of multivariate time series, particularly the interdependencies that exist between different channels.
Understanding DynLMC
The DynLMC framework addresses these shortcomings by introducing a Dynamic Linear Model of Coregionalization. This innovative approach allows for the modeling of time-varying correlations and regime-switching behaviors, which are essential for capturing the dynamic nature of real-world time series data.
Key Features of DynLMC
- Time-Varying Correlations: Unlike conventional models that assume static relationships, DynLMC adapts to changes in correlation over time, providing a more realistic representation of multivariate interactions.
- Cross-Channel Lag Structures: The model incorporates lag effects between channels, allowing for a more nuanced understanding of how different variables influence one another across time.
- Enhanced Transferability: By accurately modeling dynamic inter-channel correlations, DynLMC-generated data significantly boosts the performance of foundation models in zero-shot forecasting scenarios.
Experimental Results
In a series of experiments, three foundational models were fine-tuned using data generated by DynLMC. The results were striking: across nine different forecasting benchmarks, the models exhibited consistent improvements in performance. This underscores the effectiveness of incorporating dynamic inter-channel correlations in training datasets.
Importance of Data-Centric Pretraining
The findings from the DynLMC approach highlight a crucial aspect of modern machine learning: the significance of high-quality, realistic synthetic data in the pretraining phase of foundation models. As the field continues to evolve, the reliance on data-centric strategies will likely become increasingly important in enhancing model efficacy and transferability.
Conclusion
DynLMC represents a significant advancement in the realm of synthetic data generation for multivariate time series. By capturing the intricacies of dynamic correlations and lag structures, this model not only produces highly realistic synthetic data but also sets a new standard for training foundation models in time series forecasting. As researchers and practitioners continue to explore the potential of synthetic data, frameworks like DynLMC will play an essential role in shaping the future of predictive analytics.