Item 1. Explain the components of a time series data. Item 2. Explain the steps in Time Series Forecasting Process. Item 3. Explain stationarity of a time series data. How to determine the stationarity? Item 4. Explain the methods for converting a non-stationary time series data to stationary. Item 5. Explain auto correlation function for a time series data and how to use it Item 6. Explain 3 error metrics used for measuring the performance of a time series forecasting model: their definitions, formulas, pros, and cons Item 7. Explain the assumptions of a linear regression model. Item 8. Explain the Exponential Smoothing (ES) algorithms: Simple, Double and Triple ES. For what kind of time series data, each of these ES algorithms are suitable? Item 9. Explain the assumptions of ARIMA models and Vector ARIMA models. Item 10. Explain the assumptions of ARCH and GARCH models