Deep Learning Multi Step Prediction by Sangiorgio Matteo (13 results)
Language: English
Published by Springer, 2022
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Published by Springer, 2022
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Published by Springer, 2022
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Published by Springer, 2022
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Language: English
Published by Springer, 2022
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire… interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.
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Published by Springer, 2022
Series: Book 191 of 472 - SpringerBriefs in Applied Sciences and Technology
- Softcover
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Taschenbuch. Condition: Neu. Deep Learning in Multi-step Prediction of Chaotic Dynamics | From Deterministic Models to Real-World Systems | Matteo Sangiorgio (u. a.) | Taschenbuch | SpringerBriefs in Applied Sciences and Technology | xii | Englisch | 2022 | Springer | EAN 9783030944810 | Verantwortliche Person für die EU: Spring…er Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Published by Springer, 2022
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Language: English
Published by Springer International Publishing Feb 2022, 2022
Series: Book 191 of 472 - SpringerBriefs in Applied Sciences and Technology
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forec…ast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation. 116 pp. Englisch.
Language: English
Published by Springer, 2022
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Language: English
Published by Springer, 2022
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Published by Springer, Berlin|Springer International Publishing|Springer, 2022
Series: Book 191 of 472 - SpringerBriefs in Applied Sciences and Technology
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series.The book represents the first attempt to systematically deal with the use of deep neura…l networks to forecast chaotic ti.
Language: English
Published by Springer, Palgrave Macmillan Feb 2022, 2022
Series: Book 191 of 472 - SpringerBriefs in Applied Sciences and Technology
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast…of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 116 pp. Englisch.
