用物理力学原理加速时间序列缺失值填补，推理更快更准。
Conditional Lagrangian Wasserstein Flow for Time Series Imputation
- 基于拉格朗日力学原理，通过最小化动能学习数据运动速度。
- 引入时变去噪自编码器估计任务相关势函数梯度，降低采样方差。
- 相比现有方法推理更快，适合高时效性时间序列填补场景。
时间序列填补在众多实际应用中至关重要。为克服基于扩散模型的填补方法在推理阶段收敛缓慢的缺陷，本文提出一种新型时间序列填补方法——条件拉格朗日沃尔沙伯流（Conditional Lagrangian Wasserstein Flow, CLWF）。该方法遵循拉格朗日力学中的最小作用量原理，通过最小化对应动能来学习数据的运动速度。同时，为提升模型性能，利用时变去噪自编码器估计特定任务的势函数梯度，并将其融入基础估计器以降低采样方差。实验表明，所提方法在多个基准数据集上表现优于当前最先进的填补方法。
原文摘要 · Abstract (English)
Time series imputation is important for numerous real-world applications. To overcome the limitations of diffusion model-based imputation methods, e.g., slow convergence in inference, we propose a novel method for time series imputation in this work, called Conditional Lagrangian Wasserstein Flow (CLWF). Following the principle of least action in Lagrangian mechanics, we learn the velocity by minimizing the corresponding kinetic energy. Moreover, to enhance the model's performance, we estimate the gradient of a task-specific potential function using a time-dependent denoising autoencoder and integrate it into the base estimator to reduce the sampling variance. Finally, the proposed method demonstrates competitive performance compared to other state-of-the-art imputation approaches.
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