Forecasting Using SAS® Forecast Server Software
FSTU : FST42
This course prepares you to generate large volumes of forecasts automatically using the SAS Forecast Studio interactive interface. This course includes practice data and exercises.
This course supports both the desktop and client/server versions. Additional topics for students that license the client/server version of SAS Forecast Studio include producing reports using sample stored processes and a demonstration of SAS Time Series Studio. The self-study e-learning includes annotatable course notes in PDF format and virtual lab time to practice.
This course supports both the desktop and client/server versions. Additional topics for students that license the client/server version of SAS Forecast Studio include producing reports using sample stored processes and a demonstration of SAS Time Series Studio. The self-study e-learning includes annotatable course notes in PDF format and virtual lab time to practice.
Learn How To
- automatically create and fit custom forecast models to large-scale time series data sets
- identify series that do not have acceptable forecast accuracy
- refine forecast models to improve forecast accuracy
- reconcile hierarchical forecasts
- generate forecast data sets for deployment
- accommodate data updates in large-scale time series data sets.
Who Should Attend
Forecasters and analysts in any industry, including retail, financial services, manufacturing, and pharmaceuticals
Prerequisites
Before attending this course, you should
- be familiar with business forecasting problems
- have experience with a Windows computing environment
- have experience using a product such as Microsoft Excel to enter or transfer data and to perform elementary analyses such as computing row totals, column totals, and averages, and producing charts and plots.
SAS Products Covered
SAS Forecast Server;SAS/STAT
Course Outline
Introduction to SAS Forecast StudioForecasting Using the Default Functionality in SAS Forecast Studio
- creating a SAS Forecast Studio project
- accuracy statistics and forecast model selection
- families of models supported and functionality issues
- scenario analysis
- hierarchical forecasting
- statistical forecast reconciliation
- accumulation and aggregation options
- reconciliation effects
- manual overrides to reconciled forecasts
- custom models: generated and pre-specified
- event variables
- honest assessment
- outlier variables and other model inputs
- creating and using event variables based on calendar effects
- generated data sets
- accommodating data updates
- tools for project maintenance and management
- alternative assessment approaches
- combined model forecasts
- using stored processes to produce reports
- discussing sample stored processes that are provided with the client/server version of SAS Forecast Server
- using SAS Time Series Studio to aid in the data creation process
Live Class Schedule
Duration: 14 hours
Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
This course isn't publicly scheduled, but private training and mentoring may be available. Contact us to explore options.
Private Training
Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
Mentoring Services
Take your training to the next level with personalized mentoring. While private training offers structured coursework, mentoring provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.