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

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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.

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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.

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Book Format

Hardcover, Softcover

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Book information

Edition
1st Edition
ISBN [Softcover]
9783030944810
Publisher
Springer
Year
2021
Pages
XII, 104 p.
Series Title
PoliMI SpringerBriefs
Language
English
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