Time Series: Linear Filtering and Spectral Estimation

This lecture by the instructor covers topics related to time series analysis, focusing on linear filtering and spectral estimation. The content includes understanding models in the linear filter framework, determining the DFT of signals, analyzing moving average and autoregressive processes, and exploring spectral density functions. The lecture also delves into second-order stationarity, cross-covariance and cross-correlation sequences, and bivariate processes. Estimation methods for mean and auto-covariance sequences are discussed, along with spectral estimation techniques using Fourier frequencies and the Fejer kernel.