ICDM'23 Workshop-AI4TS: AI for Time Series Analysis: Theory, Algorithms, and Applications

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 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 focus on both the oretical and practical aspects of time series data analysis and aims to trigger research innovations in 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:

Time series forecasting and prediction
Spatio-temporal forecasting and prediction
Time series anomaly detection and diagnosis
Time series change point detection
Time series classification and clustering
Time series similarity search
Time series indexing
Time series compression
Time series pattern discovery
Interpretation and explanation in time series
Causal inference in time series
Bias and fairness in time series
Federated learning and security in time series
Benchmarks, experimental evaluation, and comparison for time series analysis tasks
Time series applications in various areas: E-commerce, Cloud computing, Transportation, Fintech, Healthcare, Internet of things, Wireless networks, Predictive maintenance, Energy, and Climate, etc.

Call for Papers

Contact with us: ai4ts.ijcai@gmail.com

We will assign 3 reviewers to each paper submission, and a meta-reviewer will be assigned to make the final decision.

Authors of accepted papers will be invited to give a 10-minute oral presentation (8 minutes presenttions + 2 minutes Q&A). All accepted papers will be invited to give poster presentations during the poster session.

Submission link: Submission Link

The review process is single-round and double-blind (submission files have to be anonymized). Concurrent submissions to other journals and conferences are acceptable. 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.

Submissions should be 4-10 pages long, including references, and follow ICDM-23 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).

Any questions may be directed to the workshop e-mail address: ai4ts.ijcai@gmail.com

Key Dates

Workshop Paper Submission Due Date: Sep 20th, 2023

Notification of Paper Acceptance: Sep 27th, 2023

Camera-ready Papers Due: October 1st, 2023

Date: December 1, 2023

The ICDMW Author Kit for camera-ready submissions is now available at: Submission link

Schedule

Note: Authors of accepted papers will be invited to give a 10-minute oral presentation (8 minutes presenttions + 2 minutes Q&A). All accepted papers will be invited to give poster presentations during the poster session.

Zoom Link: Link Password: 202312

Location: Room 5

Time(GMT+8)		Title
8:00 am - 8:10 am	Prof. Kai Yang	Opening Remarks
8:10 am - 9:00 am	Prof. Vincent S. Tseng	Keynote Talk 1: Time Series Deep Learning of Early Anomaly Classification/Prediction with Applications in Healthcare Domains (Virtual)
9:00 am - 9:50 am	Prof. Yuxuan Liang	Keynote Talk 2: When AI Meets Spatio-Temporal Data: Concepts, Methodologies, and Applications
9:50 am – 10:30 am	Paper Presentations 1-4	1. Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference Yan Wang, Zhixuan Chu, Tao Zhou, Caigao Jiang, Hongyan Hao, Minjie Zhu, Xindong Cai, Qing Cui, Longfei Li, James Y Zhang, Siqiao Xue, and Jun Zhou 2. Infinite forecast combinations based on Dirichlet Process Yinuo Ren, Feng Li, Yanfei Kang, and Jue Wang 3. Probabilistic Forecast Reconciliation with Kullback-Leibler Divergence Regularization Guanyu Zhang, Feng Li, and Yanfei Kang 4. Outlier Elimination and Reliability Assessment for Peak and Declining Time Series Datasets Jungeun Yoon, Aekyeung Moon, and Seung Woo Son
10:30 am – 11:00 am	Tea Break	Poster Session
11:00 am – 11:50 am	Dr. James Zhang	Keynote Talk 3: Time Series for Green Data Centers (Virtual)
11:50 am - 12:50 pm	Lunch Break	Poster Session
12:50 pm – 13:50 pm	Paper Presentation 5-10	5. Automatic Component Identification Based on Time Series Classification for Intelligent Devices Mingsen Du, Yanxuan Wei, Yupeng Hu, Xiangwei Zheng, and Cun Ji 6. Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores Fiete Lüer, Tobias Weber, Maxim Dolgich, and Christian Böhm 7. Towards graph-based forecasting with attention for real-world restaurant sales Henrique Duarte Moura, Leonid Kholkine, Lynn D'eer, Kevin Mets, and Tom De Schepper 8. Time Series Anomaly Detection using Diffusion-based Models Ioana Pintilie, Andrei Manolache, and Florin Brad 9. An Interpretable Distance Measure for Multivariate Non-Stationary Physiological Signals Sylvain W. Combettes, Charles Truong, and Laurent Oudre 10. INFRANET: Forecasting intermittent time series using DeepNet with parameterized conditional demand and size distribution Diksha Shrivastava, Sarthak Pujari, Yatin Katyal, Siddhartha Asthana, Chandrudu K, and Aakashdeep Singh
13:50 pm – 14:00 pm		Closing Remarks

Speakers

Prof. Vincent S. Tseng

IEEE Fellow, Chair Professor
National Yang Ming Chiao Tung University

Title: Time Series Deep Learning of Early Anomaly Classification/Prediction with Applications in Healthcare Domains

Abstract: In this talk, I will introduce recent developments on the topic of Early Anomaly Classification/Prediction through deep learning on time series, especially the new frameworks that integrate various kinds of techniques to optimize multiple metrics like accuracy and earliness simultaneously. Evaluations and applications in real-world healthcare domains for early classification/predictions on critical anomalies like arrythmia and sepsis using various vital signs (like ECG, etc) will also be illustrated to show the potential of this emerging field.

Dr. Yuxuan Liang

The Hong Kong University of Science and Technology (Guangzhou)
Assistant Professor

Title: When AI Meets Spatio-Temporal Data: Concepts, Methodologies, and Applications

Abstract: With the rapid advances in new-generation information technologies such as the Internet of Things, 5G, and mobile Internet, Spatio-Temporal (ST) data are growing explosively in urban areas. In contrast to image, text, and voice data, ST data often present unique spatio-temporal characteristics, including spatial distance and hierarchy, as well as temporal closeness, periodicity, and trend. Spatio-Temporal AI is a proprietary AI technology for ST data, where AI meets conventional city-related fields, like transportation, civil engineering, environment, and economy, in the context of urban spaces. This talk first introduces the concept of Spatio-Temporal AI, discussing its general framework and key challenges from the perspective of computer science. Secondly, we classify the applications of spatio-temporal AI into four categories, consisting of modeling ST trajectories, ST grid data, ST graphs, and ST series. We also present representative scenarios in each category. Thirdly, we delineate our recent progress in the methodologies of the above four categories in various applications. Finally, we outlook on the future of spatio-temporal AI, suggesting a few research topics that are somehow missing in the community.

Dr. James Zhang

Managing Director
Ant Group

Title: Time Series for Green Data Centers

Abstract: The exponential growth of cloud computing has led to a disturbing escalation in carbon emissions from data centers, which now contribute to over 3% of global greenhouse gas emissions. This pressing issue calls for urgent action to mitigate their escalating impact on the environment, as well as the strain they impose on the global climate. As part of the efforts towards Ant Group's goal towards carbon neutrality by 2030 and the general ESG (Environmental, Social and Governance) strategy, we focus on improving resource utilization in order to save electricity usage of data centers. Our proposed methodologies, such as Full Scaling Automation (FSA), NeuralReconciler for Hierarchical Time Series and Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies (SLOTH), not only forecast the resource demands effectively, but can also dynamically adapt resources to accommodate changing workloads in large-scale cloud computing clusters, enabling the clusters in data centers to maintain their desired CPU utilization target and thus improve energy efficiency. Our approaches achieve significant performance improvement compared to the existing work in real-world datasets. These methods have been deployed on large-scale cloud computing clusters in industrial data centers, and according to the certification of the China Environmental United Certification Center (CEC), a reduction of 947 tons of carbon dioxide, equivalent to a saving of 1538,000 kWh of electricity, was achieved during the Double 11 shopping festival of 2022, marking a critical step for our company’s strategic goal towards carbon neutrality. Our proposed systems been running robustly and continuously since then, contributing to the energy efficiency of Ant Group's cloud services. We have further open-sourced our solution under Kapacity, and we remain committed to continuously innovating and developing new solutions to contribute towards the creation of a more sustainable technological infrastructure.