Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems - Couverture souple

Sangiorgio, Matteo; Dercole, Fabio; Guariso, Giorgio

Couverture souple

ISBN 10 : 3030944816 ISBN 13 : 9783030944810

Editeur : Springer, 2022

Synopsis

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.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2022
• Langue: anglais
• ISBN 10: 3030944816
• ISBN 13: 9783030944810
• Reliure: Broché
• Numéro d'édition: 1
• Nombre de pages: 116
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne