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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.
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.
Additional information
| 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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