Time series analysis is one of several branches of statistics whose practical importance has increased with the availability of powerful computing tools. Methodology originally developed for specialized applications, for example in business forecasting or geophysical signal processing, is now widely available in general statistical packages. These computing developments have helped to bring the subject closer to the mainstream of applied statistics. This book is an introductory account of time-series analysis, written from the perspective of an applied statistician with a particular interest in biological applications. Separate chapters cover exploratory methods, the theory of stationary random processes, spectral analysis, repeated measurements, ARIMA modelling, forecasting, and bivariate time-series analysis. Throughout, analyses of data-sets drawn from the biological and medical sciences are integrated with the methodological development. The book is unique in its emphasis on biological and medical applications of time-series analysis. Nevertheless, its methodological content is more widely applicable, and it should be useful to both students and practitioners of applied statistics, whatever their specialization.

'Professor Diggle's writing is clear and to the point. This book is easy to read regardless of prior time series experience ... excellent addition to the time series literature in that it focuses on the analysis of real biomedical data.' Scott L. Zeger, School of Hygiene and Public Health, Johns Hopkins University, Statistics in Medicine 10:3
'a welcome attempt to provide an introductory account with a strong biological and medical flavour ... This modestly priced and well-written paperback must be a strong competitor as an introductory text to time series analysis whether for class use or for private reading.' Paul Davies, University of Birmingham, Royal Statistical Society News & Notes, October 1991
'The particular appeal of the book to readers of this journal will be the way in which real biological data sets are used to illuminate the theory.' Biometrics, December 1993