Pan Shirui (71 results)
Ai for Time Series : Unlocking Patterns With Deep Learning
Wu, Min (EDT); Eldele, Emadeldeen (EDT); Chen, Zhenghua (EDT); Pan, Shirui (EDT); Wen, Qingsong (EDT)
- Softcover
Seller: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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Ai for Time Series : Unlocking Patterns With Deep Learning
Wu, Min (EDT); Eldele, Emadeldeen (EDT); Chen, Zhenghua (EDT); Pan, Shirui (EDT); Wen, Qingsong (EDT)
- Softcover
Seller: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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Condition: As New. Unread book in perfect condition.
- Softcover
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: California Books, Miami, U.S.A.California Books
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- Softcover
Seller: Rarewaves.com USA, London, United KingdomRarewaves.com USA
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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 ind…ustries. 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.
Ai for Time Series : Unlocking Patterns With Deep Learning
Wu, Min (EDT); Eldele, Emadeldeen (EDT); Chen, Zhenghua (EDT); Pan, Shirui (EDT); Wen, Qingsong (EDT)
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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US$ 72.90
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Condition: New.
- Softcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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- Softcover
Seller: Rarewaves USA, OSWEGO, U.S.A.Rarewaves USA
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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 ind…ustries. 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.
- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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US$ 79.41
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paperback. Condition: 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)
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
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Paperback. Condition: Brand New. 9.18x6.12 inches. In Stock.
- Softcover
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Condition: New. In.
- Softcover
Seller: Biblios, frankfurt am main, GermanyBiblios
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- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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PF. Condition: New.
- Softcover
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- Softcover
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Condition: New. In.
- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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PF. Condition: New.
- Softcover
Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.
- Softcover
Seller: Speedyhen, Hertfordshire, United KingdomSpeedyhen
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- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 246 pages. 9.18x6.12x9.21 inches. In Stock.
- Softcover
Seller: Books Puddle, New York, U.S.A.Books Puddle
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
- Softcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, IrelandKennys Bookshop and Art Galleries Ltd.
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Condition: New.
Language: English
Published by Springer, Berlin|Springer Nature Switzerland|Springer 2023
- Softcover
Seller: moluna, Greven, Germanymoluna
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US$ 89.36
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Kartoniert / Broschiert. Condition: New.
- Softcover
Seller: moluna, Greven, Germanymoluna
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US$ 92.63
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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.
- Softcover
Seller: Books Puddle, New York, U.S.A.Books Puddle
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
- Softcover
Seller: Rarewaves USA United, OSWEGO, U.S.A.Rarewaves USA United
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US$ 102.23
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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 ind…ustries. 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.
- Softcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, IrelandKennys Bookshop and Art Galleries Ltd.
Contact seller5-star sellerCondition: New
US$ 144.75
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Condition: New.
Language: English
Published by Springer, Berlin|Springer Nature Switzerland|Springer 2023
- Softcover
Seller: moluna, Greven, Germanymoluna
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Kartoniert / Broschiert. Condition: New.
Advanced Data Mining and Applications: 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 2830, 2022, Proceedings, Part II: 13726 (Lecture Notes in Computer Science, 13726)
Chen, Weitong (Editor) / Yao, Lina (Editor) / Cai, Taotao (Editor) / Pan, Shirui (Editor) / Shen, Tao (Editor) / Li, Xue (Editor)
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 509 pages. 9.25x6.10x1.02 inches. In Stock.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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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 analysi…s 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.
