Sangiorgio M., Dercole F., Guariso G. Deep Learning in Multi-step Prediction of Chaotic Dynamics

Springer Cham 2021. — 104 p. — ISBN: 978-3-030-94482-7.The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Different 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, which 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 to sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting by applying transfer-learning techniques such as domain adaptation.True PDF