Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New.
EUR 69,00
Quantità: 2 disponibili
Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Da: Majestic Books, Hounslow, Regno Unito
EUR 68,83
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
Da: California Books, Miami, FL, U.S.A.
EUR 81,03
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: Revaluation Books, Exeter, Regno Unito
EUR 66,52
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 9.18x6.12 inches. In Stock.
Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2026
ISBN 10: 1041010311 ISBN 13: 9781041010319
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 81,22
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: New. 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.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 64,40
Quantità: 10 disponibili
Aggiungi al carrelloCondizione: New.
Condizione: New.
Da: Chiron Media, Wallingford, Regno Unito
EUR 69,06
Quantità: 2 disponibili
Aggiungi al carrellopaperback. Condizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 72,48
Quantità: 10 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 79,00
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 75,82
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Da: Revaluation Books, Exeter, Regno Unito
EUR 84,72
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 9.18x6.12 inches. In Stock.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 85,71
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
Da: Chiron Media, Wallingford, Regno Unito
EUR 82,78
Quantità: 10 disponibili
Aggiungi al carrelloPF. Condizione: New.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 91,86
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
EUR 57,36
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: NEW.
Da: Chiron Media, Wallingford, Regno Unito
EUR 89,16
Quantità: 10 disponibili
Aggiungi al carrelloPF. Condizione: New.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 90,17
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2026
ISBN 10: 1041011032 ISBN 13: 9781041011033
Da: CitiRetail, Stevenage, Regno Unito
EUR 69,73
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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 advance algorithms that are transforming time series analysis across industries. The authors highlight the use 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 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. This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Da: Revaluation Books, Exeter, Regno Unito
EUR 105,04
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 246 pages. 9.18x6.12x9.21 inches. In Stock.
EUR 67,22
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. 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), .
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 109,50
Quantità: 15 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Springer, Berlin|Springer Nature Switzerland|Springer, 2023
ISBN 10: 3031221362 ISBN 13: 9783031221361
Da: moluna, Greven, Germania
EUR 74,71
Quantità: Più di 20 disponibili
Aggiungi al carrelloKartoniert / Broschiert. Condizione: New.
Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
EUR 77,45
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: New. 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.Zhen.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 121,02
Quantità: 15 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Springer, Berlin|Springer Nature Switzerland|Springer, 2023
ISBN 10: 3031220633 ISBN 13: 9783031220630
Da: moluna, Greven, Germania
EUR 83,50
Quantità: Più di 20 disponibili
Aggiungi al carrelloKartoniert / Broschiert. Condizione: New.
Lingua: Inglese
Editore: Taylor & Francis Ltd Jul 2026, 2026
ISBN 10: 1041011032 ISBN 13: 9781041011033
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 72,72
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - 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 advance algorithms that are transforming time series analysis across industries. The authors highlight the use 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 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.