Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems (PoliMI SpringerBriefs) - Softcover

Buch 190 von 322: SpringerBriefs in Applied Sciences and Technology

Sangiorgio, Matteo; Dercole, Fabio; Guariso, Giorgio

 
9783030944810: Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems (PoliMI SpringerBriefs)
Softcover
ISBN 10: 3030944816 ISBN 13: 9783030944810
Verlag: Springer, 2022

Inhaltsangabe

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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Verlag
Springer
Erscheinungsdatum
2022
Sprache
Englisch
ISBN 10 
3030944816
ISBN 13 
9783030944810
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
116
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