Time Series Analysis: ARMA Models

This lecture delves deeper into time series analysis, focusing on ARMA models. The instructor explains the concept of mixed autoregressive moving average processes and demonstrates how to identify the orders p and q using sample ACF and PACF plots. Various packages for time series analysis are discussed, with a detailed explanation of using the auto.arima function to find and fit the best ARMA model. Model selection criteria based on AIC, AICC, and BIC values are explored, along with forecasting techniques and assessing forecast precision.