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Ari Zitin, Marc Huber, Chip Wells, George Fernandez, Jay Laramore, and Danny Modlin

Using SAS Visual Forecasting and other SAS tools, you will learn to explore time series, create and select features, build and manage a large-scale forecasting system, and use a variety of models to identify, estimate and forecast signal components of interest.

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What's inside

Three courses

Creating Features for Time Series Data

This course focuses on data exploration, feature creation, and feature selection for time series. Topics include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis.

Building a Large-Scale, Automated Forecasting System

In this course, you will learn to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. Emphasis is initially on selecting appropriate methods for data creation and variable transformations, model generation, and model selection. Then you will learn how to improve overall baseline forecasting performance by modifying default processes in the system.

Modeling Time Series and Sequential Data

In this course, you will learn to build, refine, extrapolate, and interpret models designed for a single, sequential series. Three modeling approaches are presented: the traditional Box-Jenkins approach, a Bayesian approach, and machine learning algorithms. The course concludes by considering how forecasting precision can be improved by combining the strengths of the different approaches.

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