1. Time series and stationary processes. 2. Autocovariance and partial autocorrelation functions. 3. Tests for stationarity. 4. Linear stationary models, autoregressive models, moving average models, and mixed autoregressive-moving average models. 5. Model identification, estimation and testing. 6. Seasonal models. 7. Applications of times series forecasting.

CILO-1: Demonstrate basic structure of the time series, such as stationary processes, Autocovariance and partial autocorrelation. CILO-2: Analyze and apply appropriate models for time series forecasting. CILO-3: Utilize the Box-Jenkins Methodology to Improve Times Series Regression Models. CILO-4: Explain and apply autoregressive, moving average, autoregressive moving average models and related theory in the time series and implement the algorithms using R.