AI for Time Series
CRC Press (Verlag)
978-1-041-01103-3 (ISBN)
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TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.
The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.
Dr. Min Wu is currently a Principal Scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore. He received his Ph.D. degree in Computer Science from Nanyang Technological University (NTU), Singapore, in 2011 and B.E. degree in Computer Science from University of Science and Technology of China (USTC) in 2006. He received the best paper awards in IEEE ICIEA 2022, IEEE SmartCity 2022, InCoB 2016 and DASFAA 2015. He also won the CVPR UG2+ challenge in 2021 and the IJCAI competition on repeated buyers prediction in 2015. He has been serving as an Associate Editor for journals like Neurocomputing, Neural Networks and IEEE Transactions on Cognitive and Developmental Systems, as well as conference area chairs of leading AI and machine learning conferences, such as ICLR, NeurIPS, etc. His current research interests focus on AI and machine learning for time series data, such as deep learning, self-supervised learning, domain adaptation, and knowledge distillation for time series data. Prof. Emadeldeen Eldele received his B.Sc. and M.Sc. degrees in Computer Engineering from the Faculty of Engineering, Tanta University, Egypt, in 2012 and 2018, respectively. He was awarded the Singapore International Graduate Award (SINGA) to pursue his Ph.D. at the School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore, which he completed in 2023. Currently, he is an Assistant Professor at Khalifa University, UAE. He received the IEEE Engineering in Medicine and Biology Prize Paper Award in 2023. He serves as a Guest Editor for Sensors Journal and as a Program Committee member for top conferences such as ICLR, AAAI, and ICDM. His research interests include the robustness of deep learning models against challenges such as label scarcity and domain shift. He is also interested in time series data and its applications in neuroscience and predictive maintenance. Prof. Zhenghua Chen received the B.Eng. degree in mechatronics engineering from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2011, and Ph.D. degree in electrical and electronic engineering from Nanyang Technological University (NTU), Singapore, in 2017. Currently, he is a Senior Lecture (Associate Professor) at University of Glasgow, UK. He has won several competitive awards, such as First Place Winner for CVPR 2021 UG2+ Challenge, A*STAR Career Development Award, First Runner-Up Award for Grand Challenge at IEEE VCIP 2020, Best Paper Award at IEEE ICIEA 2022 and IEEE SmartCity 2022, etc. He serves as Associate Editor-in-Chief for Neurocomputing, and Associate Editor for IEEE Transactions on Industrial Informatics, IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Industrial Cyber-Physical Systems, IEEE Sensors Journal, and Springer Discover Artificial Intelligence. He is currently the Chair of IEEE Sensors Council Singapore Chapter and IEEE Senior Member. His research interests include data-efficient and model-efficient learning with related applications in smart city and smart manufacturing. Prof. Shirui Pan is a Professor and an ARC Future Fellow with the School of Information and Communication Technology, Griffith University, Australia. He is a Co-Director of TrustAGI Lab. Before joining Griffith in 2022, he was Senior Lecturer (Associate Professor) with the Faculty of Information Technology, Monash University. He received his Ph.D degree in computer science from University of Technology Sydney (UTS), Australia. He is a Fellow of Queensland Academy of Arts and Sciences (FQA). Shirui's research focuses on artificial intelligence and machine learning. His research has been published in top conferences and journals including Nature Machine Intelligence, NeurIPS, ICML, KDD, TPAMI, TNNLS, and TKDE, attracting over 30k citations. He is recognised as one of the AI 2000 AAAI/IJCAI Most Influential Scholars, and one of the World’s Top 2% Scientists. His research received the 2020 IEEE ICDM Best Student Paper Award (2020) and the 2024 IEEE CIS TNNLS Outstanding Paper Award. Dr. Qingsong Wen is currently the Head of AI & Chief Scientist at Squirrel Ai Learning, and PhD Supervisor at University of Oxford. Before that, he worked at Alibaba, Qualcomm, Marvell, etc., and received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology, USA. His research interests include machine learning, data mining, and signal processing, especially AI for Time Series, AI for Education, LLM & AI Agent. He has published over 150 top-ranked AI conference and journal papers, had multiple Oral/Spotlight Papers at NeurIPS, ICML, ICLR, ACL, AAAI, had multiple Most Influential Papers at IJCAI, received multiple IAAI Innovative Application Awards at AAAI, and won First Place of SP Grand Challenge at ICASSP. He also regularly serves as Area Chair of the top conferences including NeurIPS, ICML, ICLR, KDD, IJCAI, ICASSP, etc, and Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence. Prof. Xiaoli Li is currently Head of the Information Systems Technology and Design (ISTD) Pillar at Singapore University of Technology and Design (SUTD). Before joining SUTD, he was the Department Head and Senior Principal Scientist at the Institute for Infocomm Research, A*STAR, Singapore. With a diverse range of research interests, Xiaoli focuses on cutting-edge areas such as AI, data mining, machine learning, and bioinformatics. His contributions to these fields are evident through his extensive publication record, boasting over 350 peer-reviewed papers, and the recognition he has received, including over ten best paper awards. He has been serving as Editor-in-chief of the Annual Review of Artificial Intelligence and an Associate Editor for prestigious journals like IEEE Transactions on Artificial Intelligence and Knowledge and Information Systems, as well as conference chairs and area chairs of leading AI, machine learning, and data science conferences, such as AAAI, IJCAI, ICLR, NeurIPS, KDD, ICDM etc. Beyond academia, Xiaoli possesses extensive industry experience, where he has successfully spearheaded over 10 R&D projects in collaboration with major industry players across diverse sectors, such as aerospace, telecom, insurance, and professional service companies. Xiaoli is an IEEE Fellow and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA). He has been recognized as one of the world's top 2% scientists in the AI domain by Stanford University.
Section 1 Introduction on AI for Time Series Analysis 1. Introduction Chapter – Domain Adaptation and Foundation Models Section 2 AI for General Time Series Analysis 2. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis 3. TSLANet: Rethinking Transformers for Time Series Representation Learning Section 3 AI for Distribution Shift in Time Series 4. OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling 5. SEA++: Multi-Graph-based Higher-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation 6. AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data Section 4 Time Series Foundation Models 7. Time-LLM: Time Series Forecasting by Reprogramming Large Language Models 8. LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization 9. Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting 10. Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts 11. EEG Foundation Model 12. PHM Foundation Model
| Erscheint lt. Verlag | 3.7.2026 |
|---|---|
| Zusatzinfo | 87 Line drawings, black and white; 87 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-041-01103-2 / 1041011032 |
| ISBN-13 | 978-1-041-01103-3 / 9781041011033 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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