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AI for Time Series
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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AI for Time Series
Language: English
Published by Taylor and Francis Ltd, GB, 2026
ISBN 10: 1041010311 ISBN 13: 9781041010319
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: 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.
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Ai for Time Series : Unlocking Patterns With Deep Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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AI for Time Series
Language: English
Published by Taylor and Francis Ltd, GB, 2026
ISBN 10: 1041010311 ISBN 13: 9781041010319
Paperback. Condition: 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.
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Ai for Time Series : Unlocking Patterns With Deep Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Add to basketPaperback. Condition: Brand New. 9.18x6.12 inches. In Stock.
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Advanced Data Mining and Applications: 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, Proceedings, Part II (Lecture Notes in Artificial Intelligence)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Advanced Data Mining and Applications: 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, Proceedings, Part I (Lecture Notes in Artificial Intelligence)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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AI for Time Series: Volume 1: Unlocking Patterns with Deep Learning
Language: English
Published by Taylor & Francis Ltd, 2026
ISBN 10: 1041010311 ISBN 13: 9781041010319
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
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Add to basketPaperback. Condition: Brand New. 246 pages. 9.18x6.12x9.21 inches. In Stock.
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Advanced Data Mining And Applications :
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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Advanced Data Mining and Applications
Language: English
Published by Springer, Berlin|Springer Nature Switzerland|Springer, 2023
ISBN 10: 3031221362 ISBN 13: 9783031221361
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Condition: 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.
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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AI for Time Series
Language: English
Published by Taylor and Francis Ltd, GB, 2026
ISBN 10: 1041010311 ISBN 13: 9781041010319
Paperback. Condition: 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.
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Advanced Data Mining And Applications :
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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Advanced Data Mining and Applications
Language: English
Published by Springer, Berlin|Springer Nature Switzerland|Springer, 2023
ISBN 10: 3031220633 ISBN 13: 9783031220630
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Add to basketPaperback. Condition: Brand New. 509 pages. 9.25x6.10x1.02 inches. In Stock.
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AI for Time Series : Volume 1: Unlocking Patterns with Deep Learning
Language: English
Published by Taylor & Francis Ltd Mai 2026, 2026
ISBN 10: 1041010311 ISBN 13: 9781041010319
Taschenbuch. Condition: 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 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.
