Generate large numbers of trustworthy forecasts – quickly and automatically.

An open forecasting ecosystem that lets you quickly and automatically producing a large number of reliable forecasts.

Scripting language for distributed processing.

A resilient, distributed and optimized generic time series analysis scripting environment supports fast, in-memory time series analysis. The scripting language is optimized and compiled for the machine it's running on – no need to rewrite code for different machines.

Automatic time series analysis & forecasting

TSMODEL procedure includes several function packages that perform a particular task in the time series analysis process. Execute user-defined programs to convert time-stamped transactional data into a time series format, then generate forecast models automatically.

External segmentation of project data

Partition project input data into segments of similar time series based on the nature of the data (e.g., slow moving, new or seasonal items). Then model each segment separately in the project pipelines. This lets you tune modeling strategies to better model patterns or characteristics of the time series in each segment.

Neural networks (NNs) & machine learning

Modeling strategy nodes incorporate NNs through a panel series, multistage (NN/regression + time series), or stacked model (NN + time series) framework. Generate features and train NNs, create a forecasting methodology that combines signals from different model types, and address problems with both time series characteristics and a nonlinear relationship between dependent and independent variables.

Add events to models

Model the effect of events (holidays, retail promotions, natural disasters, etc.) on dependent time series to improve model accuracy. Includes default prebuilt events (e.g., major holidays), and you can add others from an external event repository.

Highly flexible forecast override

A powerful manual override capability lets you make customized adjustments to specific filters or groups of time series defined by attributes, not just by hierarchical variables.

Support for APIs and other programming languages

Includes a broad range of built-in forecasting models and lets you customize models that work well with your data. Use public REST APIs to add SAS Analytics to other applications. Call analytical actions, procedures and APIs from SAS, Python, R, Java and Lua.

Hierarchical reconciliation

Models and forecasts each series in a hierarchy individually, then reconciles top-down at multiple levels. Adjust a forecast at any level, and apportion it to lower levels to keep the hierarchy consistent. Individual forecasts (by products, locations, etc.) roll up to the top number.

Additional forecasting procedures

Includes access to SAS/ETS® and SAS® Forecast Server procedures, enabling you to address virtually any forecasting and time series analysis challenge.

Better plan for the future with fast, reliable forecasting.

SAS Visual Forecasting showing segmentation pipeline on desktop monitor

Streamline and automate your forecasting process.

Automatically produce large-scale time series analyses and hierarchical forecasts – without human involvement. Reduced manual intervention means there's less chance of personal bias in the forecasting process. Fewer resources are required, and because forecast analysts don't have to build and monitor forecasting models for every time series, they can focus on more strategic, high-value forecasts or problems that aren't suitable for automation.

Plan better for the future.

Manage your organizational planning challenges by generating forecasts on an enterprise scale – quickly, automatically and as accurately as you can reasonably expect, given the nature of the behavior being forecast. The software delivers results for millions of forecasts at breakthrough speeds, enabling you to plan more efficiently and effectively for the future.

SAS Visual Forecasting showing time series modeling on desktop monitor
SAS Visual Forecasting showing applied overrides on desktop monitor

Produce forecasts that reflect reality.

Business drivers, holidays or events that affect the forecasting process are selected automatically from variables supplied to the system in the visual modeling process. You also have the flexibility to manually override forecasts based on groups that are defined using attributes, not just hierarchical variables. The resulting forecasts better reflect the intricacies of the situation.

Get to Know SAS® Visual Forecasting

SAS Viya


This solution runs on SAS® Viya®, which has the breadth and depth to conquer any analytics challenge, from experimental to mission critical. SAS Viya extends the SAS Platform to enable everyone – data scientists, business analysts, developers and executives alike – to collaborate and realize innovative results faster, with flexible licensing and pricing options to accommodate your current and future needs.

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The Forrester Wave™: Multimodal Predictive Analytics And Machine Learning (PAML) Platforms, Q3 2018



SAS is a Leader in The Forrester Wave: Multimodal Predictive Analytics And Machine Learning (PAML) Platforms, Q3 2018.


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Find out why forecasts are often wrong, and learn how you can improve them.


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