Forecasting & Long Memory: Time Series

This lecture covers autoregressive and moving average processes for forecasting in time series analysis. It delves into the implications of MA processes, invertibility, and forecasting for ARMA processes. Additionally, it explores the properties of forecast errors, predictive intervals, and error metrics. The second part of the lecture discusses long memory in stationary processes, the decay of autocovariances, and the concept of long-memory processes with polynomial decay.