Time Series

Intended for students and researchers, this text employs basic techniques of univariate and multivariate statistics for the analysis of time series and signals. It provides a broad collection of theorems, placing the techniques on firm theoretical ground. The techniques, which are illustrated by data analyses, are discussed in both a heuristic and a formal manner, making the book useful for both the applied and the theoretical worker. An extensive set of original exercises is included. Time Series: Data Analysis and Theory takes the Fourier transform of a stretch of time series data as the basic quantity to work with and shows the power of that approach. It considers second- and higher-order parameters and estimates them equally, thereby handling non-Gaussian series and nonlinear systems directly. The included proofs, which are generally short, are based on cumulants.

David R. Brillinger is a Professor in the Department of Statistics at the University of California, Berkeley, and is also chair of the Bernoulli Society's Committee on Probability and Statistics in the Physical Sciences. A native Canadian, he is President of the Statistical Society of Canada. Dr. Brillinger is the recipient of many awards in mathematics, has served on the editorial boards of several distinguished journals, and is a frequent lecturer