An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods, filtering and adjustment, spectral estimation, bivariate time series models. The course will cover the following topics: Theory of stationary processes, linear processes; Elements of inference in time domain with applications; Spectral representation of stationary processes; Elements of inference in frequency domain with applications; Theory of prediction (forecasting) with applications > ARMA processes, inference, and forecasting; Non-stationarity and seasonality, ARIMA, and SARIMA processes. Further topics, time permitting: multivariate models; GARCH models; state-space models.