Spectral Analysis: Integrated Spectrum and Autocovariance

This lecture by the instructor covers the spectral analysis of discrete-time stationary stochastic processes, focusing on the integrated spectrum and autocovariance. The lecture explains the properties of the orthogonal increment process, the covariance of complex random variables, and the relation sequence. It delves into the Fourier Transform, spectral density functions, and the Lebesgue decomposition theorem. Additionally, it discusses scenarios of purely continuous, purely discrete, and mixed spectra, along with the estimation of time series models and convergence to the mean square.