International Workshop on Temporal Analytics

@PAKDD2024

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 8, 2024

Workshop day: May 7, 2024

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

Submissions

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 the Lecture Notes in Artificial Intelligence (Springer LNAI) proceedings (past PAKDD workshops, example1, example2).

lnai

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:

https://cmt3.research.microsoft.com/IWTA2024/




Camera Ready Instructions

We would like to thank you sincerely for your valuable contribution to our International Workshop on Temporal Analytics at PAKDD 2024. The accepted submissions will be published in Springer Workshop Proceedings (example1, example2).

Please read and follow carefully the following instructions for preparing the camera-ready version.

Author’s guide provided by Springer.

Files:

  • Please prepare all of your source files (LaTeX files with all the associated style files, special fonts and eps files, or Word files) in a folder and name the folder as source-paperID. For papers prepared using LaTeX, authors should supply the bbl files to avoid the omission of data during conversion from bib to bbl. Please note that hyperlinks cannot be included in references.
  • In addition, please prepare a final pdf of your paper and name it as PAPER-paperID.pdf.
  • The License-to-Publish form (available to download here) must be signed by the corresponding author and scanned as LTP-paperID.pdf. Please be aware that digital signatures are currently not acceptable. If you have any copyright-related inquiries, please reach out to Springer well in advance of publication.

For example, as shown in the following figure, if your paper ID is 1234, you should have a folder named source-1234 containing all the source files and another PAPER-1234.pdf which is the final version of your paper. Additionally, a signed LTP-1234.pdf by the corresponding author should be attached as well. Finally, please zip the above folder and two pdf files as paper-1234.zip (paper-PaperID.zip) and upload using the submission system by March 8 2024. In case of technical difficulties, please send the zip file to the workshop chair, chang.tan [AT] monash.edu.

Finally, please register for the PAKDD conference or workshop, the early-bird deadline is March 10 2024




Registrations and VISA

Authors are required to register before March 13. Detailed information about registration is available at https://pakdd2024.org/registration/.

For those needing Visa application to enter Taiwan, PAKDD 2024 has provided guidelines at https://pakdd2024.org/visa-information/. Provided any questions, please contact the Conference Secretariat at secretariat.reg@pakdd2024.org

Program

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 held in person.

*All times are given in CST (GMT+8, Taipei time).

 0930-0940:

  • Workshop introduction

 0940-1000:

  • Finding Foundation Models for Time Series Classification with a PreText Task
    • Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber and Germain Forestier

 1000-1020:

  • Next Item and Interval Prediction of New Users using Meta-learning on Dynamic Network
    • Jun-Hong Cai, YiHang Tsai,Chia-Ming Chang and San-Yih Hwang

 1020-1040:

  • Adaptive Knowledge Sharing in Multi-Task Learning: Insights from Electricity Data Analysis
    • Yu-Hsiang Chang, Lo Pang-Yun Ting, Wei-Cheng Yin, Ko-Wei Su and Kun-Ta Chuang

 1040-1100:

  • Handling Concept Drift in Non-Stationary Bandit through Predicting Future Rewards
    • Yun-Da Tsai and Shou-De Lin

 1100-1105: coffee break

 1105-1155:

  • Invited talk: Towards Knowledge Informed Time Series Forecasting
    • Professor Dongjin Song, University of Connecticut

 1155-1200:

  • Workshop closing

Keynote Speaker

Professor Dongjin Song, University of Connecticut (UConn)

dongjin_song

Title: Towards Knowledge Informed Time Series Forecasting

Abstract:

Time series forecasting has been widely applied across various domains such as healthcare, traffic control, energy management, and finance. Recently, there has been increasing demand to integrate the knowledge of various forms into deep forecasting models. In this talk, we will delve into the integration of knowledge in the form of structures or pre-trained word token embeddings to bridge the gap between data-driven time series predictions and domain-centric understanding. Specifically, I will first introduce a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform multivariate time series forecasting within a continual learning paradigm, which leverages structural knowledge to steer the forecasting model toward identifying and adapting to different regimes. Next, I will introduce a Semantic Space Informed Prompt learning with a Large Language Model to align the pre-trained word token embeddings with time series embeddings and perform time series forecasting based on learned prompts over the joint space. Finally, I will conclude the talk by highlighting challenges and future directions.

Bio:

Dongjin Song has been an assistant professor in the School of Computing at the University of Connecticut since Fall 2020. He was previously a research staff member at NEC Labs America in Princeton, NJ. He received his Ph.D. degree in the ECE Department from the University of California San Diego (UCSD) in 2016. His research interests include machine learning, data science, deep learning, and related applications for time series data analysis and graph representation learning. Papers describing his research have been published at top-tier data science and artificial intelligence conferences, such as NeurIPS, ICML, KDD, ICDM, SDM, AAAI, IJCAI, ICLR, CVPR, ICCV, etc. He has served as Associate Editors for Pattern Recognition, Neurocomputing, and Senior PC for AAAI, IJCAI, and CIKM. He received the prestigious NSF CAREER award in 2024 and the UConn Research Excellence Research (REP) Award in 2021. He has co-organized the AI for Time Series (AI4TS) Workshop at IJCAI, AAAI, ICDM, SDM, and MiLeTS workshops at KDD.


Workshop Organizers

changwei
Chang Wei Tan
Monash University
 		charlotte
Charlotte Pelletier
Univ. Bretagne Sud (UBS)
 		jason
Jason Lines
University of East Anglia
 		mahsa
Mahsa Salehi
Monash University
 		mahsa
Lynn Miller
Monash University
 		navid
Navid Foumani
Monash University

Advisory Committee

tony
Professor Anthony Bagnall
University of Southhampton
 		geoff
Professor Geoff Webb
Monash University

Program Committee

The workshop chairs thank the program committee members who contribute to the quality of the workshop.

  • Christoph Bergmeir, Monash University, Australia
  • Angus Dempster, Monash University, Australia
  • Ben D Fulcher, University of Sydney, Australia
  • David Guijo-Rubio, Universidad de Córdoba, Spain
  • Dominique Gay, Université de La Réunion, Reunion
  • Germain Forestier, University of Haute Alsace, France
  • Jianzhong Qi, The University of Melbourne, Australia
  • Mustafa Baydoğan, Boğaziçi University, Turkey
  • Patrick Schäfer, Humboldt-Universität zu Berlin, Germany
  • Romain Tavenard, Univ. Rennes, LETG / IRISA, France
  • Thach Le Nguyen, University College Dublin, Ireland
  • Thu Trang Nguyen, University College Dublin, Ireland
  • Timilehin B Aderinola, University College Dublin, Ireland
  • Zahraa Abdallah, University of Bristol, England