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

Course code: FVVF01

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.

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 14 hours

Language: en

Price without VAT: 720 EUR


Starting date: Upon request

Type: Upon request

Course duration: 10h 30min

Language: en

Price without VAT: 1 200 EUR


Type Course
Language Price without VAT
Upon request E-learning 14 hours en 720 EUR Register
Upon request Upon request 10h 30min en 1 200 EUR Register
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Target group

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

Course structure

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.


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.

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