Welcome to the Astra project

AnalyticS of Time seRies in spAtial networks

With the advance of sensor technologies, complex cyber-physical systems (CPSs) produce large amount of time series from spatial networks, which record the time-varying statuses of spatially distributed entities. Examples include traffic time series from road networks and chemical concentration time series from sewage pipe networks.

The Astra project aims at enabling accurate and scalable machine learning algorithms on such correlated time series, which improve the efficiency of the operations of the CPSs, e.g., reducing travel time in transportation systems and early warning of high concentration of toxic chemicals in sewage systems. In particular, the project invents learning algorithms to appropriately capture complex spatio-temporal dependencies among multiple time series and invents accurate and scalable analytics algorithms using the captured spatio-temporal dependencies. The accuracy, scalability, and relevance of the proposed algorithms are validated through industrial use cases.

Astra is a project at Center for Data-Intensive Systems (Daisy), Aalborg University. Astra is funded by the Independent Research Fund Denmark (IRFD) under agreement 8022-00246B and by the Department of Computer Science, Aalborg University.

Team

  • Bin Yang, Principal Investigator, Professor
  • Chenjuan Guo, Associate Professor
  • Tung Kieu, Postdoc
  • Razvan-Gabriel Cirstea, PhD Fellow

  • Thomas Buhl Andersen, Master student, 2019 to 2020.
  • Rógvi Eliasen, Master student, 2019 to 2020.
  • Mikkel Jarlund, Master student, 2019 to 2020.

Collaborators

Publications

  1. Nicolaj Casanova Abildgaard, Casper Weiss Bang, Jonas Hansen, Tobias Lambek Jacobsen, Thomas Højriis Knudsen, Nichlas Ørts Lisby, Chenjuan Guo, Bin Yang.
    A Correlated Time Series Forecast System.
    The 21st IEEE International Conference on Mobile Data Management (MDM2020)

  2. Razvan-Gabriel Cirstea, Chenjuan Guo, and Bin Yang.
    Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting.
    5th Workshop on Mining and Learning from Time Series @ KDD'19 (MileTS19)

  3. Tung Kieu, Bin Yang, Chenjuan Guo, and Christian S. Jensen.
    Outlier Detection for Time Series with Recurrent Autoencoder Ensembles.
    28th International Joint Conference on Artificial Intelligence (IJCAI2019)

  4. Razvan-Gabriel Cirstea, Darius-Valer Micu, Gabriel-Marcel Muresan, Chenjuan Guo, and Bin Yang.
    Correlated Time Series Forecasting using Multi-Task Deep Neural Networks.
    27th ACM International Conference on Information and Knowledge Management (CIKM2018)

Master Theses

  1. Thomas Buhl Andersen, Rógvi Eliasen, Mikkel Jarlund.
    Force Myography Hand Gesture Recognition Using Transfer Learning
    Aalborg University, 2020.

Contact

If you are interested in hearing more about the Astra project or a possible partnership, please contact Bin Yang by email byang@cs.aau.dk or by phone +45 9940 9976.