Overview
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: PoliMI SpringerBriefs (BRIEFSPOLIMI)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Deep Learning in Multi-step Prediction of Chaotic Dynamics
Book Subtitle: From Deterministic Models to Real-World Systems
Authors: Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-030-94482-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-030-94481-0Published: 15 February 2022
eBook ISBN: 978-3-030-94482-7Published: 14 February 2022
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: XII, 104
Number of Illustrations: 21 b/w illustrations, 25 illustrations in colour
Topics: Mathematical Models of Cognitive Processes and Neural Networks, Computational Intelligence, Artificial Intelligence, Complex Systems