Emadeldeen eldele (64 résultats)
Ai for Time Series : Unlocking Patterns With Deep Learning
Wu, Min (EDT); Eldele, Emadeldeen (EDT); Chen, Zhenghua (EDT); Pan, Shirui (EDT); Wen, Qingsong (EDT)
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Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
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EUR 68,20
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Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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EUR 73,91
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Vendeur : Rarewaves.com USA, London, LONDO, Royaume-UniRarewaves.com USA
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EUR 76,51
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Paperback. Etat : 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 advance algorithms that are transforming time series analysis across industrie…s. 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.
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Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books
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EUR 68,91
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Etat : New.
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Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-UniPBShop.store UK
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EUR 73,01
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Vendeur : Rarewaves USA, OSWEGO, IL, Etats-UnisRarewaves USA
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EUR 78,85
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Paperback. Etat : 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 advance algorithms that are transforming time series analysis across industrie…s. 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.
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Vendeur : Chiron Media, Wallingford, Royaume-UniChiron Media
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EUR 62,30
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paperback. Etat : New.
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Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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EUR 81,12
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Etat : New.
Ai for Time Series : Unlocking Patterns With Deep Learning
Wu, Min (EDT); Eldele, Emadeldeen (EDT); Chen, Zhenghua (EDT); Pan, Shirui (EDT); Wen, Qingsong (EDT)
- Couverture souple
Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Occasion - Comme neuf
EUR 80,79
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Etat : As New. Unread book in perfect condition.
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Vendeur : Rarewaves.com USA, London, LONDO, Royaume-UniRarewaves.com USA
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EUR 85,87
Frais de port gratuitsExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : 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 industr…ies. 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.
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Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
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EUR 82,35
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Etat : New.
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Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
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EUR 76,26
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Paperback. Etat : Brand New. 234 pages. 9.18x6.12x9.21 inches. In Stock.
Ai for Time Series : Unlocking Patterns With Deep Learning
Wu, Min (EDT); Eldele, Emadeldeen (EDT); Chen, Zhenghua (EDT); Pan, Shirui (EDT); Wen, Qingsong (EDT)
- Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
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EUR 70,26
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Etat : New.
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Vendeur : Chiron Media, Wallingford, Royaume-UniChiron Media
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EUR 73,30
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paperback. Etat : New.
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Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
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EUR 79,00
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Etat : New.
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Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-UniTHE SAINT BOOKSTORE
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EUR 76,42
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Paperback / softback. Etat : New. New copy - Usually dispatched within 3 working days.
Ai for Time Series : Unlocking Patterns With Deep Learning
Wu, Min (EDT); Eldele, Emadeldeen (EDT); Chen, Zhenghua (EDT); Pan, Shirui (EDT); Wen, Qingsong (EDT)
- Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Occasion - Comme neuf
EUR 81,51
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Etat : As New. Unread book in perfect condition.
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Vendeur : Speedyhen, Hertfordshire, Royaume-UniSpeedyhen
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EUR 57,82
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Etat : NEW.
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Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
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EUR 96,21
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Paperback. Etat : Brand New. 234 pages. 9.18x6.12x9.21 inches. In Stock.
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Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-UniTHE SAINT BOOKSTORE
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EUR 90,88
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Paperback / softback. Etat : New. New copy - Usually dispatched within 4 working days.
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Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail
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EUR 70,28
EUR 43,53 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : 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 acros…s 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.
- Couverture souple
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 49,99
EUR 62,23 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Taschenbuch. Etat : 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 acro…ss 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.
- Couverture souple
Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 105,00
EUR 11,77 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 2 disponible(s)
Paperback. Etat : Brand New. 246 pages. 9.18x6.12x9.21 inches. In Stock.
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Vendeur : moluna, Greven, Allemagnemoluna
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EUR 69,86
EUR 48,99 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 3 disponible(s)
Etat : 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), .
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Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 58,99
EUR 62,03 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Taschenbuch. Etat : 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 advanced algorithms that are transforming time series analysis ac…ross 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.
- Couverture souple
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-UnisRarewaves USA United
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 80,82
EUR 43,76 expéditionExpédition nationale : Etats-UnisQuantité disponible : 2 disponible(s)
Paperback. Etat : 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 advance algorithms that are transforming time series analysis across industrie…s. 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.
- Couverture souple
Vendeur : moluna, Greven, Allemagnemoluna
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 81,24
EUR 48,99 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Etat : 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.
- Couverture souple
Vendeur : Rarewaves.com UK, London, Royaume-UniRarewaves.com UK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 71,91
EUR 76,48 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 2 disponible(s)
Paperback. Etat : 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 advance algorithms that are transforming time series analysis across industrie…s. 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.
- Couverture souple
Vendeur : preigu, Osnabrück, Allemagnepreigu
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 78,05
EUR 70,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible(s)
Taschenbuch. Etat : Neu. AI for Time Series | Volume 2: Building Robust and Generalizable Models | Min Wu (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2026 | Taylor & Francis Ltd | EAN 9781041011033 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbie…ter: preigu.
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Vendeur : Rarewaves.com UK, London, Royaume-UniRarewaves.com UK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 80,09
EUR 76,48 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : 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 industr…ies. 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.
