Generate large numbers of trustworthy forecasts – quickly and automatically.



An open forecasting ecosystem for quickly and automatically producing a large number of reliable forecasts.

Scripting language for distributed processing

Provides a scripting environment that 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

Includes several function packages that perform specific tasks in the time series analysis process. Convert time-stamped transactional data into a time series format, then generate forecast models automatically.

Segmentation of project data

Partitions data into segments of similar time series based on the nature of the data (e.g., slow moving, new or seasonal items). Or use a prebuilt segmentation template based on Demand Classification attributes. Model each segment separately in the project pipelines. This lets you tune models appropriate to the patterns or characteristics of the time series in each segment.

Neural networks (NNs) & machine learning

Incorporates NNs through a panel series, multistage (NN/regression + time series), or stacked model (NN + time series) framework via modeling strategy nodes. 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

Models 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

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

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 demand classification nested 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 use of saved filters in override on desktop monitor
SAS Visual Forecasting showing forecast viewer envelope plot 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® 可视化预测演示

SAS产品营销经理Mike Gilliland演示,SAS可视化预测如何提供灵活的分布式优化时序分析脚本环境,支持 自动模型生成、自动变量和事件选择以及自动模型选择。

SAS Viya


这款最新推出的解决方案在现代开放式平台SAS Viya上运行,可在广度和深度两方面轻松应对任何分析方面的挑战。SAS Viya是一个基于云平台的统一环境,可以被数据科学家、业务分析师、应用程序开发人员和管理人员等使用。它具有稳定可靠、可扩展、安全管理管控等一系列敏捷IT所必需的特性。全球分析领域领导者 — SAS满足您对系统性能的要求。

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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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