Ai for Time Series : Unlocking Patterns With Deep Learning

Wu, Min (EDT); Eldele, Emadeldeen (EDT); Chen, Zhenghua (EDT); Pan, Shirui (EDT); Wen, Qingsong (EDT)

ISBN 10: 104101032X / ISBN 13: 9781041010326

Published by CRC Press, 2026

About this Item

Seller Inventory # 51810517-n

Bibliographic Details

• Title: Ai for Time Series : Unlocking Patterns With Deep Learning
• Publisher: CRC Press
• Publication Date: 2026
• Language: English
• ISBN 10: 104101032X
• ISBN 13: 9781041010326
• Binding: Hardcover
• Condition: New

About this title

Synopsis

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.

In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. 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 Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.

The book can be used as 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, and climate.

About the Author

Min Wu is currently a Principal Scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore.

Emadeldeen Eldele is an Assistant Professor at Khalifa University, UAE.

Zhenghua Chen is a Senior Lecture (Associate Professor) at University of Glasgow, UK.

Shirui Pan is a Professor and an ARC Future Fellow with the School of Information and Communication Technology, Griffith University, Australia.

Qingsong Wen is currently the Head of AI & Chief Scientist at Squirrel Ai Learning.

Xiaoli Li is currently Head of the Information Systems Technology and Design (ISTD) Pillar at Singapore University of Technology and Design (SUTD).