Time Series: Autoregressive Models

This lecture delves into autoregressive models for time series analysis, focusing on AR(1) and AR(2) processes. The instructor explains the concepts of weakly stationary processes, mean, variance, and autocorrelation functions. The lecture also covers the identification of AR models using the partial autocorrelation function plot and discusses the properties of AR(p) models. Moving on to MA processes, the lecture introduces MA(1) and MA(q) models, emphasizing the mean, variance, and autocorrelation properties. Practical examples are provided to illustrate the application of autoregressive and moving average models in time series analysis.