This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book is mainly concerned with discrete time series, where the observations are taken at equal intervals. It describes various approaches to time series analysis. The book introduces a variety of probability models for time series, and discusses ways of fitting these models to time series. It examines a function called the spectral density function, which describes how the variation in a time series may be accounted for by cyclic components at different frequencies. The book shows how to estimate the spectral density function by means of a procedure called spectral analysis. It explores the analysis of bivariate time series. The book outlines the Kalman filter, which is a general method of updating the best estimate of the ‘signal’ in a time series in the presence of noise.