NOTE: All workshops are conducted fully online

Thursday 25

  • W1: First International Workshop on Temporal Analytics (9:00-11:30)
  • Workshop on Learning Data Representation for Clustering (LDRC 2023) Canceled
  • First International Workshop on Multimodal Machine Learning for Edge Devices (MMLE 2023) Canceled
  • First International Workshop on Small Precision for Machine Learning (SPINAL 2023> Canceled

W1: First International Workshop on Temporal Analytics (9:00–11:30)


Chang Wei Tan (Monash University), Charlotte Pelletier (Univ. Bretagne Sud (UBS)), Jason Lines (University of East Anglia), Mahsa Salehi (Monash University), and Navid Foumani (Monash University)


The rapid advancement in sensing technologies and computational power have led to an unprecedented growth of data, especially temporal data, or what is also commonly known as time series data. They are ubiquitous and are now seen in almost all applications including remote sensing, medicine, finance, engineering, smart cities and many more. Temporal analytics is an important field that has been studied extensively for the last decade, with hundreds to thousands of papers being published each year in various domains. Despite the advancement of new approaches and superiority of state-of-the-art algorithms, modern time series data continue to pose significant challenges to existing approaches. Some of these challenges include the high dimensionality of the data (in terms of number of variables and timesteps), noisiness and irregularity of the data. Additionally, most existing algorithms are not explainable, do not scale and require high-quantity of labelled data, which we do not always have access to. The importance of scalability becomes more prominent as the number of data increases. Therefore, designing new approaches and solutions that are able to tackle these challenges is critical. The objective of this workshop is to bring researchers in this area to discuss new and existing challenges in temporal analytics, which covers a wide range of tasks including classification, regression, clustering, anomaly detection, retrieval, feature extraction and learning representations. The solutions can be algorithmic, theoretical or systems-based in nature.

Invited talk

Backdoor Attacks on Time Series: A Generative Approach

— Prof. James Bailey (The University of Melbourne)

Accepted Papers

  • Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting
    Zheng Sun, Yi Wei, Wenxiao Jia and Long Yu
  • Transformer-based Conditional Generative Adversarial Network for Multivariate Time Series Generation
    Abdellah Madane, Mohamed-Djallel Dilmi, Florent Forest, Hanane Azzag, Mustapha Lebbah and Jérôme Lacaille
  • Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification
    Trent Henderson, Annie G. Bryant and Ben D. Fulcher

Detailed Schedule