ARIMA and Seasonal ARIMA Models

This tutorial delves a bit deeper into statistical models. Study it to better understand the ARIMA and seasonal ARIMA models. Consider closely the discussion of how to apply the ACF and PACF to estimate the order parameters for a given model. In practical circumstances, this is an important question as it is often the case that such parameters would initially be unknown.

Identifying and Estimating ARIMA Models; Using ARIMA Models to Forecast Future Values

This week, we'll learn some techniques for identifying and estimating non-seasonal ARIMA models. We'll also look at the basics of using an ARIMA model to make forecasts. We'll look at seasonal ARIMA models next week. Lesson 3.1 gives the basic ideas for determining a model and analyzing residuals after a model has been estimated. Lesson 3.2 gives a test for residual autocorrelations. Lesson 3.3 gives some basics for forecasting using ARIMA models. We'll look at other forecasting models later in the course. This all relates to Chapter 3 in the book, although the authors give quite a theoretical treatment of the topic(s).

Source: The Pennsylvania State University, https://online.stat.psu.edu/stat510
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 License.