Deep Learning in Multi-step Prediction of Chaotic Dynamics
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2022
Series: Book 191 of 472 - SpringerBriefs in Applied Sciences and Technology
- Softcover
- New
Seller: moluna, Greven, Germanymoluna
AbeBooks seller since July 9, 2020
Condition: New
£ 46.69
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- Title
- Deep Learning in Multi-step Prediction of Chaotic Dynamics
- Author
- Sangiorgio, Matteo|Dercole, Fabio|Guariso, Giorgio
- Publisher
- Springer, Berlin|Springer International Publishing|Springer
- Publication year
- 2022
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3030944816
- ISBN 13
- 9783030944810
- Series
- Book 191 of 472: SpringerBriefs in Applied Sciences and Technology
- Seller catalogs
- Mathematik/Naturwissenschaften/Technik/Medizin
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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| Item | 26 to 60 business days | 26 to 60 business days |
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