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