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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning in Multi-Step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems

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

Dercole, Fabio
Guariso, Giorgio
Sangiorgio, Matteo

Paperback

Technology & EngineeringGeneral ComputersGeneral Mathematics

ISBN10: 3030944816
ISBN13: 9783030944810
Publisher: Springer
Published: Feb 15 2022
Pages: 104
Weight: 0.39
Height: 0.25 Width: 6.14 Depth: 9.21
Language: English

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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