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Summary MIDA
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- Random Variables Refresh: Mean, Variance, Standard Deviation, and properties of Random Vectors
- Stationary Stochastic Processes (SSP): Definition, White Noise, Covariance Function, and Spectral Representation
- Families of SSP: Moving Average Process (MA), Auto Regressive Process (AR), ARMA Process, and their properties
- Spectral Representation: Fourier Transform of the covariance function, spectrum properties, and complex spectrum
- Canonical Representation: Solving multiplicity of ARMA models using canonical spectral factor
- Prediction Problem: Fake and True Problems, Prediction Error Variance, and prediction with exogenous variables (ARX, ARMAX)
- Prediction Error Minimization Methods: Least Square method, optimization criteria, normal equations, and matrix properties
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