Time series analysis is an effective statistical methodology for modelling time series data (a series of observations collected over time) and forecasting future observations in many areas; economics, the social sciences, the physical and environmental sciences, medicine, and signal processing. For example, monthly unemployment rates in economics, yearly birth rates in social science, global warming trends in environmental studies, and magnetic resonance imaging of brain waves in medicine. This course presents the fundamental principles of time series analysis including mathematical modeling of time series data and methods for statistical inference. Topics covered will include modeling and inference for linear autoregressive time series models; i.e., autoregressive (AR) and autoregressive moving a verage (ARMA) models, (nonseasonal/seasonal) autoregressive integrated moving average (ARIMA) models, unit root and differencing, transfer function noise models, intervention analysis and state-space models. If time permits, additional topics will include spectral analysis, (generalized) autoregressive conditionally heteroscedastic (ARCH) models, Kalman filtering and smoothing, and signal extraction. Prerequisites: Probability and Statistics at a calculus-based graduate level such as Stat 607 and Stat 608 (concurrent), a previous course on regression analysis covering multiple linear regression (e.g., Stat 505, BioEpi744, RESEC702) with some exposure to regression models in matrix form. Prior computing experience with R is desirable.