Overview: Meaning of Time Series, Objectives of Time Series Analysis, Simple Time Series Models, Different components of time series, Estimation and Elimination of Trend and Seasonal Components.
Stationary Processes: Basic Properties, Linear Processes, ARMA Processes, Properties of Sample Mean and Autocorrelation Function. Unit Roots in Time Series Models.
ARMA Models: ARMA (P, Q) Process, ACF and PACF of ARMA (P, Q) Process, Forecasting ARMA Process Problems.
Modeling and Forecasting with ARMA Process: Preliminary Estimation, Diagnostic Checking, Forecasting.
Non-stationary and Seasonal Time Series Models: ARIMA Models for Nonstationary Time Series, Forecasting ARIMA Models, Seasonal ARIMA Models.
Forecasting Technique: Exponential smoothing, Holt-Winters Algorithm.