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 for generating features and training a neural network.
  • 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.

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.

Support for open source languages

  • Includes External Language Package (EXTLANG), which supports open source code – Python and R.
  • Lets you call analytical actions from Python, R, Java, JavaScript and Lua.

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. 

Hierarchical reconciliation

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

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.
  • Attributes are available for use in faceted search in the viewers.

External segmentation & demand classification

  • Provides support for 1,000 segments.
  • Can utilize predefined segments in time series.
  • Includes a prebuilt segmentation template based on Demand Classification.

Time series & forecast viewers

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

Distributed, accessible & cloud-ready

  • Runs on SAS® Viya®, a scalable and distributed in-memory engine of the SAS Platform.
  • 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.

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