SAS^® Visual Forecasting

Get a hands-on tour of the forecasting functionality in Model Studio, a component of SAS Viya. You'll 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 and more.

Learn to build, refine, and, in some cases, interpret time series models. Three modeling approaches are presented. The traditional ARIMAX or time series regression approach, a Bayesian approach where the basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects, and finally machine learning algorithms for time series are explored. Examples are provided to build intuition on the effective use of these algorithms. The course concludes by considering how forecasting precision can be improved by creating combined (or ensemble) and hybrid model forecasts.

Learn to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. For the course project, students build and then refine a large-scale forecasting system. Emphasis is initially on selecting appropriate methods for data creation and variable transformations, model generation, and model selection. Students are then asked to improve overall baseline forecasting performance by modifying default processes in the system.

Learn about data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis.