A Bayesian tensor ring decomposition model with automatic rank determination for spatiotemporal traffic data imputation

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.