Conditional Lagrangian Wasserstein Flow for Time Series Imputation

• URL: http://arxiv.org/abs/2410.07550v2
• Date: Wed, 07 May 2025 04:26:40 GMT
• Title: Conditional Lagrangian Wasserstein Flow for Time Series Imputation
• Authors: Weizhu Qian, Dalin Zhang, Yan Zhao, Yunyao Cheng,
• Abstract summary: Conditional Lagrangian Wasserstein Flow is a novel method for time series imputation.<n>We learn the velocity by minimizing the corresponding kinetic energy.<n>To enhance the model's performance, we estimate the gradient of a task-specific potential function.
• Score: 3.583527519172841
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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