International Workshop on Temporal Analytics


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.

This workshop will discuss a wide variety of topics including but are not limited to:

  • Time series analysis tasks such as classification, extrinsic regression, forecasting, clustering, annotation, segmentation, anomaly detection and pattern mining
  • Time series analysis with no or few supervision
  • LLMs for time series data
  • Early classification of time series
  • Deep learning for time series (e.g. generative, discriminative)
  • Learning representation for time series
  • Modelling temporal dependencies
  • Spatial-temporal statistical analysis
  • Functional data analysis methods
  • Scalable time series analytics methods
  • Explainable time series analysis methods
  • Time series that are sparse, involve irregular sampling
  • Time series with missing values and variable lengths
  • Multivariate time series with high dimensionality and heterogenous
  • Interdisciplinary time series analysis applications

Important Dates

Paper submission deadline: February 13, 2024

Paper acceptance notification: February 28, 2024

Paper camera-ready deadline: March 6, 2024

Workshop day: May 7, 2024

*All deadlines are 23:59 Pacific Standard Time (PST)


Paper submission must be in English. All papers will be double-blind reviewed by the Program Committee based on technical quality, relevance to data mining, originality, significance, and clarity. All paper submissions will be handled electronically. Papers that do not comply with the Submission Policy will be rejected without review.

Each submitted paper must include an abstract up to 200 words and be between 6-12 single-spaced pages with 10pt font size (including references, appendices, etc.). Authors must use Springer LNCS/LNAI manuscript submission guidelines for their submissions. All papers must be submitted electronically through the CMT paper submission system in PDF format only. Supplementary material may be submitted as a separate PDF file, but reviewers are not obligated to consider this, and your manuscript should, therefore, stand on its own merits without any supplementary material. Supplementary material will not be published in the proceedings. Each accepted paper will be invited to propose a camera ready version of their article taking into account the reviewers recommendations. The accepted submissions will be published in Springer Workshop Proceedings (example1, example2). We are also seeking collaborations with Springer journals so that authors could submit the extended papers to journals.

The submitted papers must not be previously published anywhere but may be under consideration by any other conference or journal during the workshop review process. Submitting a paper to the conference means that if the paper was accepted, at least one author will complete the regular registration and attend the workshop to present the paper.

Submission website:


This workshop will be held in conjunction with the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024) on the 7th May 2024. It will be organized in person.