Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

TL;DR

This paper introduces LATC, a novel tensor completion framework that incorporates autoregressive modeling to improve imputation of missing spatiotemporal traffic data, capturing both global and local structures.

Contribution

The paper proposes a low-rank autoregressive tensor completion method that integrates temporal variation regularization, enhancing imputation accuracy over traditional low-rank approaches.

Findings

LATC outperforms existing methods in various missing data scenarios.

The autoregressive regularization effectively captures temporal dynamics.

Extensive experiments validate the method's robustness and effectiveness.

Abstract

Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the full power of the data. Missing data imputation has been a long-standing research topic and critical application for real-world intelligent transportation systems. A widely applied imputation method is low-rank matrix/tensor completion; however, the low-rank assumption only preserves the global structure while ignores the strong local consistency in spatiotemporal data. In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing \textit{temporal variation} as a new regularization term into the completion of a third-order (sensor $\times$ time of day $\times$ day) tensor. The third-order tensor structure allows us to better capture the global consistency of traffic data, such as the inherent seasonality and day-to-day similarity. To achieve local consistency, we design the temporal variation by imposing an AR($p$) model for each time series with coefficients as learnable parameters. Different from previous spatial and temporal regularization schemes, the minimization of temporal variation can better characterize temporal generative mechanisms beyond local smoothness, allowing us to deal with more challenging scenarios such "blackout" missing. To solve the optimization problem in LATC, we introduce an alternating minimization scheme that estimates the low-rank tensor and autoregressive coefficients iteratively. We conduct extensive numerical experiments on several real-world traffic data sets, and our results demonstrate the effectiveness of LATC in diverse missing scenarios.