This course aims to introduce basic concepts and modeling techniques for time series data. It emphasizes implementation of the modeling techniques and their practical application in analyzing actuarial and financial data. The open source program R will be used. Chapter 7, 8 and 9 of "Regression Modeling with Actuarial and Financial Applications", by E.W. Frees, Cambridge University Press, 2010 will be covered, if time allows. This course satisfies the VEE (Validation by Educational Experience) requirement set by the SOA (Society of Actuaries) in time series of the Applied Statistical Methods topic. Specifically, SOA requires the following educational experience in time series and forecasting: Linear time series models; Moving average, autoregressive and/or ARIMA models; Est imation, data analysis and forecasting with time series models; Forecast errors and confidence intervals. This course will cover the above topics and more advanced models like exponential smoothing, Box-Jenkins and ARCH/GARCH, if time permits.