Nonlinear Time Series Analysis With R

Huffakar

ISBN 10: 0198782934 / ISBN 13: 9780198782933

Verlag: Oxford University Press, 2017

Beschreibung

Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide. Bestandsnummer des Verkäufers ABNR-233412

Bibliografische Details

Titel: Nonlinear Time Series Analysis With R

Verlag: Oxford University Press

Erscheinungsdatum: 2017

Einband: Softcover

Zustand: New

Auflage: Internationale Ausgabe

Über diesen Titel

Inhaltsangabe:

Nonlinear time series analysis with r editado por Oxford UP

Reseña del editor:

Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthroughs in nonlinear dynamics raise another possibility: highly complex dynamics can emerge endogenously from astoundingly parsimonious deterministic nonlinear models. Nonlinear Time Series Analysis (NLTS) is a collection of empirical tools designed to aid practitioners detect whether stochastic or deterministic dynamics most likely drive observed complexity. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach.

This book is targeted to professionals and graduate students in engineering and the biophysical and social sciences. Its major objectives are to help non-mathematicians―with limited knowledge of nonlinear dynamics―to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the book makes readers active learners with hands-on computer experiments in R code directing them through NLTS methods and helping them understand the underlying logic. The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework―condensed from sound empirical practices recommended in the literature―that details a step-by-step procedure for applying NLTS in real-world data diagnostics.