SAS^® VISUAL FORECASTING SOFTWARE

Data access, preparation & quality

Access, profile, cleanse and transform data using an intuitive interface that provides self-service data preparation capabilities with embedded AI.

Custom chatbot creation

Create and deploy custom, natural language chatbots via an intuitive, low-code visual interface for chatbot-enabled insights and conversational user experiences​.

Data visualization

Visually explore data and create and share smart visualizations and interactive reports through a single, self-service interface. Augmented analytics and advanced capabilities accelerate insights and help you uncover stories hidden in your data​.

Scripting language for distributed processing

A 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

Several function packages 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

Partition 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. You can tune models appropriate to the patterns or characteristics of the time series in each segment.

Add events to models

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

Neural networks (NNs) & machine learning

Incorporate 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.

Highly flexible forecast override

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 & other programming languages

Use the broad range of built-in forecasting models, and/or 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

Model and forecast each series in a hierarchy individually, then reconcile 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

Address virtually any forecasting and time series analysis challenge with access to SAS/ETS^® and SAS Forecast Server procedures.

Powered by SAS^® Viya^®

SAS Viya has a completely redesigned architecture that is compact, cloud native and fast. Whether you prefer to use the SAS Cloud or a public or private cloud provider, you'll be able to make the most of your cloud investment.