Time Series and Forecasting: Course Outline

• Definition of time series.
• Stationary and non-stationary time series.
• How model arise.
• Meaning and estimation of ACF and PACF.
• Linear stationary and non-stationary models.
• Autoregressive form of ARMA model.
• Model identification.
• Minimum mean squares forecasts.
• Forecasting using state space model and use of Kalman filter series model.
• Estimation of models smoothing.
• Reliability analysis of forecast error.
• Transfer function.
• Intervention analysis and Spectral analysis.

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