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This course introduces basic time series analysis and forecasting methods. Topics include stationary processes, ARMA models, modeling and forecasting using ARMA models, nonstationary and seasonal time series models, state-space models, and forecasting techniques. By the end of this course, students will be able to: - Describe important time series models and their applications in various fields. - Formulate real life problems using time series models. - Use statistical software to estimate models from real data and draw conclusions and develop solutions from the estimated models. - Use visual and numerical diagnostics to assess the soundness of their models. - Communicate the statistical analyses of substantial data sets through explanatory text, tables, and graphs. - Combine and adapt different statistical models to analyze larger and more complex data.