Nonlinear Time Series Analysis With R

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.

Ray Huffaker, Professor, Agricultural and Biological Engineering, University of Florida, USA,Marco Bittelli, Professor, Department of Agricultural Sciences, University of Bologna, Italy,Rodolfo Rosa, Professor, Department of Physics and Matter Technologies, National Research Council, Italy

Ray Huffaker is a professor in Agricultural and Biological Engineering at the University of Florida. He specializes in nonlinear time series analysis, biological and economic modelling of water and other ecosystem resources, economic dynamics, and natural resource and environmental law. He has taught graduate courses in nonlinear data diagnostics, mathematical optimization techniques, economic dynamics, and micro- and macroeconomic analysis; and undergraduate courses in natural resource and environmental law. He holds bachelor degrees in economics and Italian literature, a Ph.D. in agricultural economics, and a J.D. in law all from the University of California at Davis.

Marco Bittelli received the degree in Agricultural Sciences from the University of Bologna, Italy, and a M.S. and a Ph.D. degree in Soil Physics from Washington State University, USA. He spent one year as a postdoctoral scientist in the Physics Department at the University of Heidelberg, Germany. He is associate professor at the University of Bologna, where he teaches soil and environmental physics, hydrological modelling, philosophy of science and scientific methods courses, both at the undergraduate and graduate level.

Rodolfo Rosa received a degree in physics in 1968 and in philosophy in 1977. From 1969 to 1992, he was a researcher at the National Research Council-IMM Institute in Bologna. From 1992 to 2014 he was professor at the Faculty of Statistics at the University of Bologna, where he taught courses on Statistics for Experimental Research, Chaos and Complexity, Probability and Stochastic Processes. His research interests include Monte Carlo methods applied to atomic interactions in matter, statistical mechanics, organization of genetic information in coding sequences of DNA, philosophy of science, and chaos theory.