Time series data are becoming ubiquitous in numerous realworld applications, e.g., IoT devices, healthcare, wearable devices, smart vehicles, financial markets, biological sciences, environmental sciences, etc. Given the availability of massive amounts of data, their complex underlying structures/distributions, together with the high-performance computing platforms, there is a great demand for developing new theories and algorithms to tackle fundamental challenges (e.g., representation, classification, prediction, causal analysis, etc.) in various types of applications. The goal of this workshop is to provide a platform for researchers and AI practitioners from both academia and industry to discuss potential research directions, key technical issues, and present solutions to tackle related challenges in practical applications. The workshop will cover on both the oretical and practical aspects of time series data analysis and aim to trigger research innovations in theories, algorithms, and applications. This year, we will have a particular focus on foundation models as well as large language models (LLMs), and would like to discuss their potential impact and how they can be applied to varieties of time series applications. We will invite researchers and AI practitioners from the related areas of machine learning, data science, statistics, econometrics, and many others to contribute to this workshop.
This workshop encourages submissions of innovative solutions for a broad range of time series analysis problems. Topics of interest include but are not limited to the following:
Submissions should be 4-7 pages long, excluding references, and follow IJCAI-24 template. Submissions are single-blind and author identity will be revealed to the reviewers. An optional appendix of arbitrary length is allowed and should be put at the end of the paper (after references).
Accepted papers will be presented as posters during the workshop and list on the website (non-archival/without proceedings). Besides, a small number of accepted papers will be selected to be presented as contributed talks. We also welcome submissions of unpublished papers, including those that are submitted/accepted to other venues if that other venue allows so.
Submission link: https://cmt3.research.microsoft.com/AI4TSconf2024
Any questions may be directed to the workshop e-mail address: ai4ts.ijcai@gmail.com
Workshop Paper Submission Due Date: May 15th May 25th, 2024(AoE)
Notification of Paper Acceptance: June 1st, 2024(AoE) Jun 2nd, 2024(AoE)
IJCAI-24 Workshops: August 3rd-5th, 2024 August 5th, 2024(GMT+9)
At least one author of each accepted paper *must* travel to the IJCAI venue in person, and that multiple submissions of the same paper to more IJCAI workshops are forbidden.
Time(GMT+9) | Speaker | Title |
---|---|---|
9:00 am - 9:10 am | Dr. Dongjin Song | Opening Remarks |
9:10 am - 10:00 am | Dr. Jeff Tao | Keynote Talk 1: TBA |
10:00 am - 10:40 am | Oral Presentations |
Paper 1: MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis Paper 2: Sequential Treatment Effect Estimation with Variational Transformers: Application to COVID-19 Infection Clusters Paper 3: Adaptive Uncertainty Quantification for Trajectory Prediction Under Distributional Shift |
10:40 am β 11:00 am | Coffee Break | |
11:00 am β 11:50 am | Dr. Fenglong Ma | Keynote Talk 2: TBA |
11:50 am - 2:00 pm | Lunch Break | |
2:00 pm - 2:50 pm | Mr. Wenjie Du | Keynote Talk 3: TBA |
2:50 pm - 3:00 pm | Dr. Haifeng Chen | Award Ceremony |
3:00 pm - 5:00 pm | Poster Session |
* MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis Jiexi Liu, Meng Cao, Songcan Chen
* Sequential Treatment Effect Estimation with Variational Transformers: Application to COVID-19 Infection Clusters Jinho Kang, Sungjun Lim, Hojun Park, Jaehun Jung, Jiyoung Jung, Kyungwoo Song
* Adaptive Uncertainty Quantification for Trajectory Prediction Under Distributional Shift Huiqun Huang, Sihong He, Fei Miao
* Trend learning based loss function for time-series forecasting
Haibin Liao, Yiyang Hua, Li Yuan
* Neural Architecture Search for Self-Supervised Representation Learning on
Time-Series Data
Seoyoung Kim, Doguk Kim
* TimewarpVAE: Simultaneous Time-Warping and
Representation Learning of Trajectories
Travers Rhodes, Daniel D. Lee
* Affinity-Driven Transfer Learning For Load Forecasting
Ahmed Rebei , Manar Amayri , Nizar Bouguila
* EEG-SSM: Leveraging State-Space Model for Dementia Detection
Xuan-The Tran, Linh Le, Quoc Toan Nguyen, Thomas Do, Chin-Teng Lin
* Physical Process Guided Graph Neural Networks for Anomaly Detection in CPSs
Mengzhou Gao, Zehao Liu, Yifan Lu, Pengfei Jiao
* Context-aware Distance for Time Series
ZhihuiWang, Changlian Tan
* Unveiling the Secrets: How Masking Strategies Shape Time Series Imputation
Linglong Qian, Zina Ibrahim , Wenjie Du, Yiyuan Yang, Richard JB Dobson
* A Bilevel Optimization Framework for Peak Trough Aware Time Series
Forecasting
Jungmin Kim, Jaesik Choi