Lecture
Forecasting & Long Memory: Time Series
Description
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.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.