This course introduces basic concepts and methods for time series analysis and forecasting, as well as related statistical software and real-world data applications. Topics include autocorrelation function, partial autocorrelation function, extended autocorrelation function, autoregressive-moving-average models, models for seasonal time series, unit-root test, integrated processes, distributed lag models, and transfer function models. Optional topics may include conditional heteroscedastic models and the Kalman filter. This course will be offered in 2025-2026 and alternating years thereafter.