Deep learning in multi-step prediction of chaotic dynamics : from deterministic models to real-world systems / Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso.
2021
Q172.5.C45 S36 2021
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Title
Deep learning in multi-step prediction of chaotic dynamics : from deterministic models to real-world systems / Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso.
Author
Sangiorgio, Matteo, author.
ISBN
9783030944827 (electronic bk.)
3030944824 (electronic bk.)
9783030944810
3030944816
3030944824 (electronic bk.)
9783030944810
3030944816
Published
Cham : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource (111 pages) : illustrations (some color).
Item Number
10.1007/978-3-030-94482-7 doi
Call Number
Q172.5.C45 S36 2021
Dewey Decimal Classification
003/.857015118
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Source of Description
Description based upon print version of record.
Added Author
Dercole, Fabio, author.
Guariso, Giorgio, author.
Guariso, Giorgio, author.
Series
SpringerBriefs in applied sciences and technology. PoliMI SpringerBriefs.
Available in Other Form
Deep Learning in Multi-Step Prediction of Chaotic Dynamics
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Table of Contents
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.
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.