Missing values are prevalent in spatio-temporal traffic data, undermining the quality of data-driven analysis. While prior works have demonstrated the promise of tensor completion methods for imputation, their performance remains limited for complicated composite missing patterns. This paper proposes a novel imputation framework combining tensor factorization and rank minimization, which is effective in capturing key traffic dynamics and eliminates the need for exhaustive rank tuning. The framework is further supplemented with time series decomposition to account for trends, spatio-temporal correlations, and outliers, with the intention of improving the robustness of imputation results. A Bregman ADMM algorithm is designed to solve the resulting multi-block nonconvex optimization efficiently. Experiments on four real-world traffic state datasets suggest that the proposed framework outperforms state-of-the-art imputation methods, including the context of complex missing patterns with high missing rates, while maintaining reasonable computation efficiency. Furthermore, the robustness of our model in extreme missing data scenarios, as well as under perturbation in hyperparameters, has been validated. These results also underscore the potential benefits of incorporating temporal modeling for more reliable imputation.

中文翻译：

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基于 Tucker 分解的张量完成，用于稳健的流量数据插补

时空交通数据中普遍存在缺失值，从而损害了数据驱动分析的质量。虽然之前的工作已经证明了张量补全方法在插补方面的前景，但它们的性能对于复杂的复合缺失模式仍然有限。本文提出了一种结合张量分解和秩最小化的新型插补框架，该框架可以有效捕获关键流量动态，并且无需进行详尽的秩调整。该框架进一步补充了时间序列分解，以解释趋势、时空相关性和异常值，旨在提高插补结果的稳健性。Bregman ADMM 算法旨在有效地解决由此产生的多块非凸优化。对四个真实世界交通状态数据集的实验表明，所提出的框架优于最先进的插补方法，包括具有高缺失率的复杂缺失模式的背景，同时保持合理的计算效率。此外，我们的模型在极端缺失数据场景以及超参数扰动下的稳健性已经得到验证。这些结果还强调了结合时间模型以获得更可靠插补的潜在好处。