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Forecasting Using Model Studio in SAS(R) Viya(R) 3.4

Course Details
Code: FVVF8I
Tuition (USD): $1,300.00 • Classroom (2 days)

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.

Skills Gained

  • 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 Can Benefit

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


  • Before attending this course, you should be familiar with applied forecasting concepts. You do not need formal training in statistics to benefit from this course. Programming experience is also not required.

Course Details

Introduction and Data Visualization

  • Overview of SAS Home.
  • Creating a forecasting project and loading the data.
  • Visualizing the modeling data using attribute variables.

Pipeline Essentials

  • Basic modeling with pipelines.
  • Pipeline templates and pipeline comparison.
  • Accuracy statistics and forecast model selection.
  • Families of models supported.

Hierarchical Forecasting

  • Hierarchical forecasting.
  • Time series data creation and forecast reconciliation.
  • Combined models.
  • Honest assessment.

Post-forecasting Functionality

  • Overrides.
  • Exporting generated tables.
  • Adjustments to statistical forecasting.

In-Line Code Access and Overview (Appendix)

  • Code overview.

Accommodating Event Variables in a Model Studio Project (Appendix)

  • Adding event variables in the TSMODEL procedure.
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