Recently, tensor factorization models have shown superiority in solving traffic data imputation problem. However, these approaches have a limited ability to learn traffic data correlations and are easy to overfit when the pre-defined rank is large and the available data is limited. In this paper, we propose a Bayesian tensor ring decomposition model, utilizing Variational Bayesian Inference to solve the model. Firstly, tensor ring decomposition with an enhanced representational capability is used to decompose partially observed data into factor tensors to capture the correlation in traffic data. Secondly, to address the issue of selecting large pre-defined rank when data availability is limited, an automatic determination mechanism of tensor ring ranks is proposed. This mechanism can be implemented by pruning the zero-component horizontal and frontal slices of the core factors in each iteration, reducing the dimensions of the core factors and consequently lowering the tensor ring ranks. Finally, extensive experiments on synthetic data and four diverse types of real-world traffic datasets demonstrate the superiority of the proposed model. In the Guangzhou dataset, the maximum improvement in Mean Absolute Percentage Error can reach 15 % compared to the most competitive baseline model.

中文翻译：

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一种用于时空交通数据插补的自动秩确定的贝叶斯张量环分解模型


最近，张量分解模型在解决交通数据插补问题方面显示出优势。然而，这些方法学习流量数据相关性的能力有限，并且在预定义排名较大且可用数据有限时很容易过度拟合。在本文中，我们提出了一个贝叶斯张量环分解模型，利用变分贝叶斯推理来求解该模型。首先，使用具有增强表示能力的张量环分解，将部分观察到的数据分解为因子张量，以捕获交通数据中的相关性。其次，针对数据可用性有限时选择大预定义秩的问题，该文提出一种张量环秩自动判定机制。这种机制可以通过在每次迭代中修剪核心因子的零分量水平和正面切片来实现，减小核心因子的维度，从而降低张量环秩。最后，对合成数据和四种不同类型的真实世界交通数据集的广泛实验证明了所提出的模型的优越性。在广州数据集中，与最具竞争力的基线模型相比，平均绝对百分比误差的最大改进可以达到 15%。