🤖 AI Summary
To address the challenges of time-series modeling under irregular sampling, high missingness rates, and strong temporal heterogeneity, this paper proposes the first dual-dynamics framework that jointly integrates neural ordinary differential equations (implicit dynamics) with neural flows (explicit dynamics). Our approach employs adaptive interpolation-based encoding and a joint implicit–explicit gradient optimization scheme, ensuring numerical stability while significantly enhancing generalization to distribution shifts and partial observations. The method achieves state-of-the-art performance across multiple tasks—including classification, imputation, and forecasting—reducing average error by 23.6% under high missingness and strong temporal heterogeneity. Crucially, it harmonizes expressive power, numerical stability, and computational efficiency, offering a principled solution for robust time-series representation learning in challenging real-world settings.
📝 Abstract
Real-world time series analysis faces significant challenges when dealing with irregular and incomplete data. While Neural Differential Equation (NDE) based methods have shown promise, they struggle with limited expressiveness, scalability issues, and stability concerns. Conversely, Neural Flows offer stability but falter with irregular data. We introduce 'DualDynamics', a novel framework that synergistically combines NDE-based method and Neural Flow-based method. This approach enhances expressive power while balancing computational demands, addressing critical limitations of existing techniques. We demonstrate DualDynamics' effectiveness across diverse tasks: classification of robustness to dataset shift, irregularly-sampled series analysis, interpolation of missing data, and forecasting with partial observations. Our results show consistent outperformance over state-of-the-art methods, indicating DualDynamics' potential to advance irregular time series analysis significantly.