Time Series Analysis and Forecasting: Course Outline

• Stochastic Process.
• Stationery time-Series: auto-correlation and auto-covariance.
• Estimates functions and standard error of the auto-correlation function (ACF).
• Spectral Analysis: Periodogram. 
• Spectral density functions. 
• Comparison with ACF.
• Linear stationery models: Auto-regressive. 
• Moving average and mixed models. 
• Non-stationery models.
• General ARIMA notation and models. 
• Introduction to forecasting.
• Important considerations for forecasting: objective. 
• Cost function.
• Model specification.
• Forecast construction using ARMA models. 
• Forecasting trend.
• Other types of forecast: exponential smoothing. 
• Forecast Evaluation: recursive estimation. 
• Model specification. 
• Model comparison and testing.
• Spurious Regressions. 
• Introduction to cointegration.
• Error correction representation.
• Granger representation theorem.

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