Optimal transport for time-series imputation
Wang, Hao Li, Zhengnan Li, Haoxuan Chen, Xu Gong, Mingming Chen, Bin Chen, Zhichao
Wang, Hao
Li, Zhengnan
Li, Haoxuan
Chen, Xu
Gong, Mingming
Chen, Bin
Chen, Zhichao
Supervisor
Department
Machine Learning
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Conference proceeding
Date
2025
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Language
English
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Abstract
Missing data imputation through distribution alignment has demonstrated advantages for non-temporal datasets but exhibits suboptimal performance in time-series applications. The primary obstacle is crafting a discrepancy measure that simultaneously (1) captures temporal patterns-accounting for periodicity and temporal dependencies inherent in time-series-and (2) accommodates non-stationarity, ensuring robustness amidst multiple coexisting temporal patterns. In response to these challenges, we introduce the Proximal Spectrum Wasserstein (PSW) discrepancy, a novel discrepancy tailored for comparing two sets of time-series based on optimal transport. It incorporates a pairwise spectral distance to encapsulate temporal patterns, and a selective matching regularization to accommodate non-stationarity. Subsequently, we develop the PSW for Imputation (PSW-I) framework, which iteratively refines imputation results by minimizing the PSW discrepancy. Extensive experiments demonstrate that PSW-I effectively accommodates temporal patterns and non-stationarity, outperforming prevailing time-series imputation methods. Code is available at https://github.com/FMLYD/PSW-I. © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.
Citation
H. Wang et al., “Optimal Transport for Time Series Imputation,” International Conference on Representation Learning, vol. 2025, pp. 828–852, May 2025, Accessed: Jul. 22, 2025
Source
13th International Conference on Learning Representations, ICLR 2025
Conference
13th International Conference on Learning Representations, ICLR 2025
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Source
13th International Conference on Learning Representations, ICLR 2025
Publisher
International Conference on Learning Representations, ICLR