ARIMA

4 hours of instruction

Learn how to apply seasonal analysis and ARIMA models and how to decompose and identify seasonal and non- seasonal factors all while learning the nuances of building sophisticated time series models.

OBJECTIVES

1. Build time series models to identify and predict trends
2. Define key metrics of complex time series models
3. Develop models that account for seasonal trends and other factors
4. Build and evaluate ARIMA models

PREREQUISITES

Introduction to Time Series Analysis

SYLLABUS & TOPICS COVERED

1. Modeling
   • AR, MA, and ARIMA models in a nutshell
   • Implement AR, MA, and ARIMA on a time series

SOFTWARE REQUIREMENTS

You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.