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• Week 1; Time series , its types, Objectives and Components
• Week 2; Stochastic Process
• Week 3; Filters and Operators
• Week 4; Stationary and Non Stationary Time Series
• Week 5; Transformation of Time Series Data
• Week 6; Important Models of Time Series (Purely Random Process and Random Walk )
• Week 7; Auto-regressive and Moving Average Models with properties and Diagnostic Checking
• Week 8; ARIMA and ARMA Model (ARIMA,ARMA, Seasonal ARMA, Seasonal ARIMA Models)
• Week 9; Model Selection Criteria and Model Diagnostic Checking
• Week 10; The Box Jenkins Method
• Week 11; ARCH, GARCH Models
• Week 12; State Space Models and Kalman Filters
• Week 13; Granger Causality
• Week 14; Vector Autoregressive Models
• Week 15; Time Series Forecasting
• Week 16; Cointegration Analysis and Johansen Approach