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.
