Titlebook: Stochastic Models for Time Series; Paul Doukhan Textbook 2018 Springer International Publishing AG, part of Springer Nature 2018 60G10 37

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Non-linear Processesr is specific to this monograph so that we do not provide a global reference. However Rosenblatt (Stationary processes and random fields. Birkhäuser, Boston, 1985) performs an excellent approach to modelling. Generalized linear models are presented in Kedem and Fokianos (Regression models for time s

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Dependencence. Birkhaüser, Boston, 2002b) for long-range dependence, and Doukhan (Mixing: properties and examples. Lecture notes in statistics. Springer, New York, 1994) and Dedecker et al. (Weak dependence: with examples and applications. Lecture notes in statistics. Springer, New York, 2007) for weak-depend

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Non-linear Processesnearity to more general settings. From linear processes it is natural to build polynomial models or their limits. Then we consider more general Bernoulli shift models to define recurrence equations besides the standard Markov setting.

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Textbook 2018er long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as w