Classes will be held online via ZOOM at the scheduled time (Tuesday 14:00-15:30). Please see the Content section for details and a web link. A time series is a sequence (x_t) of data where the subscript t indicates the time at which the datum x_t was observed. The course provides an introduction to the theory and practice of statistical time series analysis. Topics covered include stationary and non-stationary stochastic processes, autoregressive and moving average (ARMA) models, model selection and estimation, state-space models and the Kalman filter, forecasting and forecast evaluation, and an outline of spectral techniques. Knowledge of the contents of modules M-MATH-101321 (Introduction to Stochastics) and M-MATH-101322 (Probability Theory) is essential. Furthermore, familiarity with the contents of module M-MATH-103220 (Statistics) is strongly recommended.