Forecasting Using Model Studio in SAS Viya

There is a new version of this course. Please see Forecasting Using Model Studio in SAS Viya.

This course provides a hands-on tour of the forecasting functionality in Model Studio, a component of SAS Viya. The course begins by showing how to load the data into memory and visualize the time series data to be modeled. Attribute variables are introduced and implemented in the visualization. The course then covers the essentials of using pipelines for generating forecasts and selecting champion pipelines in a project. It also teaches you how to incorporate large-scale forecasting practices into the forecasting project. These include the creation of data hierarchies, forecast reconciliation, overrides, and best practices associated with forecast model selection.

Learn how to

• Automatically create and fit custom forecast models using structured analytic workflows or pipelines.
• Visualize modeling data using attribute variables.
• Refine forecast models to improve forecast accuracy.
• Apply overrides-generated forecasts.
• Generate forecast data sets for deployment.
• Build and share custom pipelines for large-scale forecasting analyses.

Who should attend

Forecasters and analysts in any industry, including retail, financial services, manufacturing, and pharmaceuticals

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	1.5 days

Before attending this course, you should have an understanding of basic statistical concepts. You can gain this experience by completing the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. Programming experience is not required.

This course addresses SAS Visual Forecasting software.

Introduction and Data Visualization

• SAS Drive overview.
• Creating a forecasting project and loading the data.
• Visualizing the modeling data using attribute variables.

Pipeline Essentials

• Definition and creation of a time series.
• Fundamental concepts in time series modeling.
• Classes of time series models
• Model comparison using honest assessment.
• Pipeline templates and pipeline comparison.

Hierarchical Forecasting

Post-forecasting Functionality

• Overrides and exporting generated tables.

Inline Code Access and Overview (Appendix)

• Code overview.