SAS Visual Forecasting Features

Large-scale time series analysis & forecasting in a distributed environment

Automatically generates large quantities of statistically based forecasts in a distributed, in-memory environment.
Scripting language enables distributed, in-memory time series analysis.
Shuffles the data so that each time series is copied into the memory of a single computing node.
Executes each time series on one thread of a node, and each node executes the compiled script for each of its assigned time series.
Is optimized for the machine on which it is running so users don’t have to rewrite code for different machines.

Neural network/machine learning modeling strategy nodes

Includes a panel series neural network framework with automatic feature generation and hyperparameter tuning (autotuning) capabilities.
Provides a multistage (neural network/regression + time series) framework for creating a forecasting methodology that combines signals from different types of models.
Addresses problems that have both time series characteristics and nonlinear relationships between dependent and independent variables using stacked model (neural network + time series) forecasting.

Deep learning capabilities

Produce forecasts with recurrent neural network (RNN), the long short-term memory (LSTM) unit network, and the gated recurrent unit (GRU) network.
Transactional data is formatted automatically for forecasting purposes with the above deep learning methods.
Recursive strategy is applied automatically for multistep forecasting.

Interactive modeling

Automatically produce analysis plots, including seasonal cycles, autocorrelation function (ACF), partial autocorrelation function (PACF) and white noise probability test for individual time series.
Compare models visually and by using the metric of choice in the in-sample and out-of-sample regions.
Develop custom exponential smoothing, ARIMA and subset (factored) ARIMA models for individual time series via a simple user interface.
Select your own model champions.

Flexible override facility

Enables customized forecast adjustments that aren't limited by the structure of the forecasting hierarchy.
Lets you select filters based on attributes, such as location, brand, category, size, color, sentiment, quality, etc.
Lets you define override specifications by filter and time period(s) for all time series contained within a filter.
Includes faceted search filters.
Allows disaggregation of override using optimization model.
Enables batch execution and incremental data updates.

Integration with open source

Includes External Language Package (EXTLANG), which distributes open source code from Python and R to run in parallel in the worker nodes of SAS Viya in the cloud.
Call SAS Visual Forecasting analytical actions from Python, R, Java, JavaScript and Lua.

Hierarchical reconciliation

Models and forecasts each series in the hierarchy individually.
Reconciles forecasts at multiple levels of the hierarchy.

Automatic segmentation based on data patterns

Prebuilt segmentation template based on time series patterns such as volume, volatility, and seasonality.
Automatic creation of nested, configurable pipelines with an appropriate modeling strategy for each segment selected by default for the prebuilt demand classification template.
Ability to import predefined segments by users, supporting up to 1,000 segments.

Derived attributes

Create predefined sets of derived attributes, including:
- Time series attributes (min, max, mean, missing, etc.).
- Forecasting attributes (model properties, statistics of fit).
- Demand classification attributes.
- Volume/volatility attributes.

Time series analysis

Autocorrelation analysis.
Cross-correlation analysis.
Seasonal decomposition and adjustment analysis.
Count series analysis.
Diagnostic tests for seasonality, stationarity, intermittency and tentative ARMA order selection.

Time frequency analysis

Windowing functions.
Fourier analysis for real and complex time series.
Short-time Fourier analysis.
Discrete Hilbert transform.
Pseudo Wigner-Ville distribution.

Time series modeling

ARIMA models (dynamic regression and transfer functions).
Exponential smoothing models.
Unobserved component models.
State-space models.
Intermittent demand models with Croston’s method.

Automatic time series modeling

Automatic time series model generation.
Automatic input variable and event selection.
Automatic model selection.
Automatic parameter optimization.
Automatic forecasting.

Singular spectrum analysis (SSA)

Univariate SSA decomposition and forecasting.
Multivariate SSA.
Automatic SSA.

Subspace tracking (SST)

Perform advanced monitoring (signal analysis) techniques for multiple time series.

Time interval evaluation

Evaluate a variable in an input table for suitability as a time ID variable.
Assess how well a time interval specification fits date/datetime values or observation numbers used to index a time series.
Can either be specified explicitly as input to PROC TSMODEL or inferred by the procedure based on values of the time ID variable.

Time series & forecast viewers

Provides a Time Series Viewer with a prebuilt set of time series attributes.
Provides a Forecast Viewer with a prebuilt set of forecasting attributes.
Includes envelope plots for viewing multiple series.
Lets you use faceted filters on descriptive statistics, model properties and statistics of fit.

Time series dimension reduction (TDR) package

Enables dimension reduction of transactional time series data in preparation for time series mining.
- Lets you then apply traditional data mining techniques (such as clustering, classification, decision trees and others).

Project sharing

Projects in Model Studio use the project sharing feature of SAS Drive.
When shared with read/write access, multiple users can make changes to the project at the same time.
Alternatively, projects can be shared with read-only access.

Distributed, accessible & cloud-ready

Runs on SAS^® Viya^®, a scalable and distributed in-memory engine.
Distributes analysis and data tasks across multiple computing nodes.
Provides fast, concurrent, multiuser access to data in memory.
Includes fault tolerance for high availability.
Lets you add the power of SAS analytics to other applications using SAS Viya REST APIs.

SAS Visual Forecasting Features List