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Time series analysis is one of several branches of statistics whose practical importance has increased with the availability of powerful computational tools. Methodology that was originally developed for specialized applications, for example in finance or geophysics, is now widely available within general statistical packages. The second edition of Time Series: A Biostatistical Introduction is an introductory account of time series analysis, written from the perspective of applied statisticians whose interests lie primarily in the biomedical and health sciences. This edition has a stronger focus on substantive applications, in which each statistical analysis is directed at a specific research question. Separate chapters cover simple descriptive methods of analysis, including time-plots, smoothing, the correlogram and the periodogram; theory of stationary random processes; discrete-time models for single series; continuous-time models for single series; generalized linear models for time series of counts; models for replicated series; spectral analysis, and bivariate time series. The book is unique in its focus on biomedical and health science applications, which has been strengthened in this second edition. Nevertheless, the methods described are more widely applicable. It should be useful to teachers and students on masters-level degree courses in statistics, biostatistics and epidemiology, and to biomedical and health scientists with a knowledge of statistical methods at undergraduate level. Throughout, examples based on real datasets show a close interplay between statistical method and substantive science. This book will also describe the implementation of the methods in the R computing environment and provide access to R code and datasets.