Time series data are becoming ubiquitous in numerous real world 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 some 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 issues in practical applications. The workshop will focus on both the theoretical and practical aspects of time series data analysis and aims to trigger research innovations on theories, algorithms, and 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 4-6 pages long, excluding references, and follow IJCAI-22 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 posted on the workshop webpage. We welcome submissions of unpublished papers, including those that are submitted/accepted to other venues if that other venue allows so.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/AI4TS2022/
Any questions may be directed to the workshop e-mail address: email@example.com
Workshop Paper Submission Due Date: May 13, 2022
Notification of Paper Acceptance: June 3, 2022
Camera-ready Papers Due: June 17, 2022
IJCAI-21 Workshops: July 24, 2022
|9:00 am - 9:10 am
|Dr. Hangwei Qian
|9:10 am - 10:00 am
|Dr. Haifeng Chen
|Signals, Structure, and Dynamics: Unraveling the Unknown in Complex Systems
|10:00 am - 10:45 am
|Paper presentation 1-4
|10:45 am - 11:15 am
|11:15 am - 12:05 pm
|Dr. Jan Gasthaus
|Probabilistic Time Series Forecasting: Past, Present, and Future
|12:05 pm - 12:30 pm
|Paper presentation 5-6
|12:30 pm - 2:00 pm
|2:00 pm - 3:00 pm
|Dr. Kashif Rasul
|Transformers for Time Series Forecasting
|3:00 pm - 3:30 pm
|3:30 pm - 4:06 pm
|Paper presentation 7-9
|4:06 am - 4:10 pm
|Dr. Haifeng Chen
Department Head of Data Science
NEC Labs America
Dr. Haifeng Chen is heading the Data Science and System Security Department at NEC Laboratories America in Princeton, New Jersey. He received the BEng and MEng degrees in automation from Southeast University China, and the PhD degree in computer engineering from Rutgers University in 2004. He and his team members are working on various topics related to big data analytics, AI, software and system security, smart service and platforms. Dr. Chen has served in the program committee for a number of top AI conferences, and has been in the panel of National Science Foundation (NSF) programs. He is a member of the school of Systems and Enterprises Advisory Board in Stevens Institute of technology, New Jersey. Dr. Chen has co-authored more than a hundred conference/journal publications including the best paper runner-up at SigKDD’16, and has over 70 patents granted. Most of his research led to advanced solutions and products for various industrial domains including power plants, satellite, financial, retail, and so on.
Amazon Web Services
AWS AI Labs
Dr. Jan GasthausJan Gasthaus is a Principal Machine Learning Scientist in the Amazon AI Labs, working mainly on time series forecasting, anomaly detection, and large-scale probabilistic machine learning. He is passionate about developing novel machine learning solutions for addressing challenging business problems with scalable machine learning systems, all the way from scientific ideation to productization. Prior to joining Amazon, Jan obtained a BS in Cognitive Science from the University of Osnabrueck, an MS in Intelligent Systems from UCL, and a PhD from the Gatsby Unit, UCL, focusing on Nonparametric Bayesian methods for sequence data.
Dr. Kashif Rasul is a Research Scientist at Morgan Stanley where he works on deep learning based time-series forecasting problems. He also contributes to open source machine learning software. He studied Mathematics at Monash University in Australia and obtained his PhD from the Free University in Berlin, Germany.
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* Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer William T. Ng, K. Siu, Albert C. Cheung and Michael K. Ng.
* HiMLEdge-An Energy-Aware Optimization Framework for Hierarchical Machine Learning in Wireless Sensor Systems at the Edge Julio Wissing, Stephan Scheele, Aliya Mohammed, Dorothea Kolossa and Ute Schmid.
* LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning Sathvik Bhaskarpandit,Priyanka Gupta and Manik Gupta.
* Incremental Stock Volume Prediction with Gradient Distillation and Diversified Memory Selection Shicheng Li, Zhiyuan Zhang, Lei Li, Ruihan Bao, Keiko Harimoto and Xu Sun.
* Towards Safe Autonomy in Hybrid Traffic: The Power of Information Sharing in Detecting Abnormal Human Drivers Behaviors Jiangwei Wang, Lili Su, Songyang Han, Dongjin Song and Fei Miao.
* Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data Julian Lemmel, Zahra Babaiee, Marvin Kleinlehner, Ivan Majic, Philipp Neubauer, Johannes Scholz, Radu Grosu and Sophie A. (Gruenbacher) Neubauer.
* DTG-LSTM: A Delaunay Triangulation Graph for Human Trajectory Prediction in Crowd Scenes Zhimiao Shi and Yao Xiao.
* A Wavelet Decomposition based Ensemble Learning Framework for Short-Term Stock Prediction Hieu Nguyen, Jihye Moon, Dongjin Song and Joseph Johnson.