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  3. Matematisk modellering

Deep Learning in Multi-step Prediction of Chaotic Dynamics

- From Deterministic Models to Real-World Systems
Af: Giorgio Guariso, Matteo Sangiorgio, Fabio Dercole Engelsk Paperback

Deep Learning in Multi-step Prediction of Chaotic Dynamics

- From Deterministic Models to Real-World Systems
Af: Giorgio Guariso, Matteo Sangiorgio, Fabio Dercole Engelsk Paperback
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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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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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Produktdetaljer
Sprog: Engelsk
Sider: 104
ISBN-13: 9783030944810
Indbinding: Paperback
Udgave:
ISBN-10: 3030944816
Kategori: Matematisk modellering
Udg. Dato: 15 feb 2022
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 15 feb 2022
Forfatter(e): Giorgio Guariso, Matteo Sangiorgio, Fabio Dercole
Forfatter(e) Giorgio Guariso, Matteo Sangiorgio, Fabio Dercole

Kategori Matematisk modellering

ISBN-13 9783030944810

Sprog Engelsk

Indbinding Paperback

Sider 104

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 15 feb 2022

Oplagsdato 15 feb 2022

Forlag Springer Nature Switzerland AG

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