About author

Hi, I am Xinyu Chen (陈新宇), a Ph.D. candidate at Polytechnique Montreal affiliated with University of Montreal in Canada. Currently, I am leading an innovative and interesting GitHub project with Prof. Lijun Sun and Prof. Nicolas Saunier. That is transdim (GitHub repository: https://github.com/xinychen/transdim), which is a problem-oriented project for transportation data imputation and prediction. This open-source project focuses on (1) handling missing data problems with various missing patterns in the spatiotemporal settings, (2) performing time series forecasting on large-scale, high-dimensional, and multidimensional data, and (3) performing time series forecasting in the presence of missing values. The goals of this open-source project include (1) providing some well-defined data modeling problems, (2) building a platform for gathering some open-source data sets, and (3) providing some Python implementation for machine learning models. Until now, it has covered the Python implementation of a manifold of machine learning models. In the meanwhile, my collaborators have developed some tensor learning models with me for spatiotemporal data imputation and forecasting.



  • Several publicly available spatiotemporal data sets, and most of them are collected from transportation systems. (data set list)
  • Two standard data modeling problems (imputation & prediction).
  • A number of machine learning solutions (baseline models & newly proposed solutions).
  • Well-documented Python implementation on Jupyter Notebook (mainly relied on Numpy).
  • Offering ideas for spatiotemporal traffic data modeling.

Credit: Xinyu Chen would like to thank the Institute for Data Valorisation (IVADO) for providing the PhD Excellence Scholarship to support this open-source project.