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20132025
most citedBayesian Temporal Factorization for Multidimensional Time Series Prediction

268 citations · 1.7k across the 47 of their papers we have counts for

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Showing 2022Show all

8 papers · 1 filter

cs.LG2022★ 7 cited

Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting

Seongjin Choi, Nicolas Saunier, Vincent Zhihao Zheng +2

Deep learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss fun…

cs.LG2022★ 41 cited

Laplacian Convolutional Representation for Traffic Time Series Imputation

Xinyu Chen, Zhanhong Cheng, HanQin Cai +2

Spatiotemporal traffic data imputation is of great significance in intelligent transportation systems and data-driven decision-making processes. To perform efficient learning and a…

cs.LG2022

Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression

Xinyu Chen, Chengyuan Zhang, Xiaoxu Chen +2

The problem of broad practical interest in spatiotemporal data analysis, i.e., discovering interpretable dynamic patterns from spatiotemporal data, is studied in this paper. Toward…

stat.AP2022★ 12 cited

Bayesian calibration of traffic flow fundamental diagrams using Gaussian processes

Zhanhong Cheng, Xudong Wang, Xinyuan Chen +2

Modeling the relationship between vehicle speed and density on the road is a fundamental problem in traffic flow theory. Recent research found that using the least-squares (LS) met…

stat.ML2022

Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data

Mengying Lei, Aurelie Labbe, Lijun Sun

Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications. As real-world spatiotemporal data often exhibits complex dependencies th…

stat.AP2022★ 3 cited

Probabilistic forecasting of bus travel time with a Bayesian Gaussian mixture model

Xiaoxu Chen, Zhanhong Cheng, Jian Gang Jin +2

Accurate forecasting of bus travel time and its uncertainty is critical to service quality and operation of transit systems; for example, it can help passengers make better decisio…

cs.LG2022

Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization

Xinyu Chen, Chengyuan Zhang, Xi-Le Zhao +2

Movement speed data from urban road networks, computed from ridesharing vehicles or taxi trajectories, is often high-dimensional, sparse, and nonstationary (e.g., exhibiting season…

stat.AP2022★ 5 cited

Bayesian inference for link travel time correlation of a bus route

Xiaoxu Chen, Zhanhong Cheng, Lijun Sun

Estimation of link travel time correlation of a bus route is essential to many bus operation applications, such as timetable scheduling, travel time forecasting and transit service…