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UBDA Training Course: Jump-start AI Training on Time Series Data

Training Course

  • Date

    25 May - 08 Jun 2022

  • Organiser

  • Time

    14:30 - 16:00

  • Venue



You are cordially invited to attend a training course spanning of 3 weeks. This training will introduce and demonstrate you to the characteristics of time series data, its different analyses, and Virtual Desktop in the UBDA HPC (High Performance Computing) platform. During the three weeks, the course includes UBDA HPC services, the pre-processing and feature extraction of time series data, the pre-trained models in ONNX and TF Hub, examples of classification models on time series data, introduction of the two classical forecasting methods (exponential smoothing and ARIMA), comparison the forecasting performance of these two methods with DL methods and discussion of how to use well-known data sets such as Heng Seng index, and Dow Jones index to conduct forecasting analysis. Participants are expected to have basic knowledge of Python. Please find the arrangements below.



Jump-start AI Training on Time Series Data (Poster)



25 May 2022 to 8 Jun 2022

Venue   Online via Microsoft Teams

Session activities

  • UBDA HPC Services (1st online session: 25 May 2022, 14:30 - 16:00)
  • Practical Transfer Learning (2nd online session: 1 Jun 2022, 14:30 - 16:00)
  • Time Series Forecasting (3rd online session: 8 Jun 2022, 14:30 - 16:00)
Consultation Sessions  
  • 27 May 2022 (online/ P505, 11:00 – 12:00)
  • 2 Jun 2022 (online/ P505, 11:00 – 12:00)
  • 10 Jun 2022 (online/ P505, 11:00 – 12:00)

Registration / Quota  


PolyU staff and students
100 (first-come-first serve via online registration)
Confirmation emails will be sent to the registrants on 19 May 2022 

Course Topics  
  • UBDA HPC services
    • Introduction to UBDA Facility
    • Pre-processing and feature extraction of Time Series Data
    • Virtual Desktop in the UBDA HPC
  • Practical Transfer Learning
    • Pre-trained models (ONNX, TF Hub)
    • Transfer Learning on classification of Time Series Data
  • Time Series Forecasting
    • Comparison of classical methods and recent packages
    • Using public datasets to conduct forecasting analysis

Should you have any enquiry, please contact Dr Vincent Ng at and Dr Heung Wong at

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