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SAS® Visual Forecasting
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SAS® Visual Forecasting
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Access a resilient, distributed time series analysis and scripting environment for cloud computing. Generate large numbers of trustworthy forecasts, quickly and automatically, using cutting-edge forecasting techniques including time series, machine learning, hybrid (ML + time series), and deep learning. SAS Visual Forecasting provides automatic model generation, external driver and holiday/event selection, model parameter optimization, and model selection.  It also implements a simple way to segment the data based on demand patterns and scale open-source (Python and R) algorithms to run in a distributed manner in the cloud all within a consistent framework.
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What's new in the latest release

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Get Started

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Ready to generate forecasts from your historical data? These resources are a good place to start.
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Learn about the capabilities and functionality of SAS Visual Forecasting.

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Learn the basics

Learn how to get started with SAS Visual Forecasting with these tutorials:

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Stay connected

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Be a part of the community for SAS Visual Forecasting. We offer a variety of ways for you to interact with users and experts.
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Tutorials

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Browse our library of free SAS Visual Forecasting tutorials to learn something new or sharpen your skills.
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More How-To Videos

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Accumulate Transactional Data into Time Series

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Create and Fit a User-specified Model

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Learn about the pipeline functionality in SAS Visual Forecasting.

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Learn about modeling and forecasting time series automatically in SAS Visual Forecasting.

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Using Pipelines

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Training Courses

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Learn SAS technology quickly and efficiently by taking a course from the analytics experts.
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Forecasting Using Model Studio in SAS Viya

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.

Models for Time Series and Sequential Data

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.

Large Scale Forecasting Using SAS Viya: A Programming Approach

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.

Time Series Feature Mining and Creation

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.

Visit the learning path for Forecasting and Econometrics

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Documentation

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Find user's guides and other technical documentation for SAS Forecasting.
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SAS Visual Forecasting on the SAS Viya Platform

SAS Visual Forecasting: Help Center

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Previous Versions

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SAS Visual Forecasting Blogs & Communities

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Connect with other SAS users by joining a users group or attending an upcoming event.