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Time Series Analysis : With Applications in R
Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticity, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text.
Nonlinear Time Series Analysis in the Geosciences : Applications in Climatology, Geodynamics and Solar-Terrestrial Physics
This book presents recent developments in nonlinear time series which have been motivated by present day problems in geosciences. Modern methods of spatio-temporal data analysis, time-frequency analysis, dimension analysis, nonlinear correlation and synchronization analysis and other nonlinear concepts are used to study emerging questions in climatology, geophysics, solar-terrestrial physics and related scientific disciplines. This volume collects contributions of some of the world's leading experts in geoscientific time series analysis.
Nonlinear Dynamics in Geosciences
Nonlinear Dynamics in Geosciences is comprised of the proceedings of "20 Years of Nonlinear Dynamics in Geosciences", held June 11-16, 2006 in Rhodes, Greece as part of the Aegean Conferences. The volume brings together the most up-to-date research from the atmospheric sciences, hydrology, geology, and other areas of geosciences, and discusses the advances made in the last two decades and the future directions of nonlinear dynamics. Topics covered include predictability, ensemble prediction, nonlinear prediction, nonlinear time series analysis, low-dimensional chaos, nonlinear modeling, fractals and multifractals, bifurcation, complex networks, self-organized criticality, extreme events, and other aspects of nonlinear science.
