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＜電子ブック＞
Deep Learning in Multi-step Prediction of Chaotic Dynamics : From Deterministic Models to Real-World Systems / by Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso
(PoliMI SpringerBriefs. ISSN:22822585)

版	1st ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XII, 104 p. 46 illus., 25 illus. in color : online resource
著者標目	*Sangiorgio, Matteo author Dercole, Fabio author Guariso, Giorgio author SpringerLink (Online service)
件　名	LCSH:Neural networks (Computer science)  LCSH:Computational intelligence LCSH:Artificial intelligence LCSH:System theory FREE:Mathematical Models of Cognitive Processes and Neural Networks FREE:Computational Intelligence FREE:Artificial Intelligence FREE:Complex Systems
一般注記	Introduction to chaotic dynamics’ forecasting,. Basic concepts of chaos theory and nonlinear time-series analysis -- Artificial and real-world chaotic oscillators -- Neural approaches for time series forecasting -- Neural predictors’ accuracy -- Neural predictors’ sensitivity and robustness -- Concluding remarks on chaotic dynamics’ forecasting 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 HTTP:URL=https://doi.org/10.1007/978-3-030-94482-7

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