Deep Learning in Multi-step Prediction of Chaotic Dynamics From Deterministic Models to Real-World Systems
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 man...
| Main Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2021, 2021
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| Edition: | 1st ed. 2021 |
| Series: | PoliMI SpringerBriefs
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
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