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.


  • Bin Yang, Professor, PI
  • Chenjuan Guo, Associate Professor, Co-PI
  • Tung Kieu, Assistant Professor
  • Razvan-Gabriel Cirstea, PhD Fellow

  • David Gonzalo Chaves Campos, Master student, 2020 to 2021.
  • Mik Christensen, Master student, 2020 to 2021.
  • Anders Madsen, Master student, 2020 to 2021.
  • Frederik Baymler Mathiesen, 2020 to 2021.
  • Thomas Buhl Andersen, Master student, 2019 to 2020.
  • Rógvi Eliasen, Master student, 2019 to 2020.
  • Mikkel Jarlund, Master student, 2019 to 2020.



  1. Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, and Kai Zheng.
    Robust and Explainable Autoencoders for Time Series Outlier Detection.
    ICDE 2022, To appear.

  2. Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, and Shirui Pan.
    Towards Spatio-Temporal Aware Traffic Time Series Forecasting.
    ICDE 2022, To appear.

  3. Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, and Christian S. Jensen.
    Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders.
    ICDE 2022, To appear.

  4. Frederik Mathiesen, Bin Yang, and Jilin Hu.
    Hyperverlet: A Symplectic Hypersolver for Hamiltonian Systems.
    AAAI 2022, To appear.

  5. Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, and Christian S. Jensen.
    AutoCTS: Automated Correlated Time Series Forecasting.
    PVLDB 2022, to appear.

  6. David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, and Christian S. Jensen.
    Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles.
    PVLDB 2022, to appear.

  7. Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Bin Yang, and Sinno Jialin Pan.
    EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting.
    The 37th IEEE International Conference on Data Engineering (ICDE2021)

  8. 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)

  9. 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)

  10. 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)

  11. 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.


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.