AI for Time Series: Volume 1: Unlocking Patterns with Deep Learning
Language: English
Published by Taylor & Francis Ltd, 2026
- Softcover
- New

Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
AbeBooks seller since June 14, 2006
Condition: New
US$ 83.00
Quantity: 1 available
Add to basketItem description from seller
New copy - Usually dispatched within 4 working days.
Seller Inventory # B9781041010319
- Title
- AI for Time Series: Volume 1: Unlocking Patterns with Deep Learning
- Author
- Min Wu
- Publisher
- Taylor & Francis Ltd
- Publication year
- 2026
- Condition
- New
- Binding
- Paperback / softback
- Language
- English
- ISBN 10
- 1041010311
- ISBN 13
- 9781041010319
- Item weight
- 526 grams
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.
"Synopsis" may belong to another edition of this title.
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).
"About the title" may belong to another edition of this title.
THE SAINT BOOKSTORE
Southport, United Kingdom
AbeBooks seller since June 14, 2006
Shipping rates from United Kingdom to U.S.A.
| Item | 7 to 28 business days | 7 to 28 business days |
|---|---|---|
| First item | US$ 21.42 | US$ 24.09 |
Payment methods
Store description
The Saint Bookstore has a range of over 1 million titles available.
Specialty
GeneralSeller's business information
SB ONLINE LTD
50 Devonshire Road
Southport, United Kingdom PR9 7BZ
Terms of sale
Please order through the Abebooks checkout. We only take orders through Abebooks - We don't take direct orders by email or phone.
Refunds or Returns: A full refund of the purchase price will be given if returned within 30 days in undamaged condition.
As a seller on abebooks we adhere to the terms explained at http://www.abebooks.co.uk/docs/HelpCentral/buyerIndex.shtml - if you require further assistance please email us at orders@thesaintbookstore.co.uk
Shipping terms
Most orders usually ship within 1-3 business days, but some can take up to 7 days.