TEL:
+708-771-4567
FAX:
+708-771-4569
EMAIL:
sca@scausa.com
Forecasting

The SCA Statistical System is a methodologically advanced software system that provides comprehensive capabilities for business and industrial forecasting applications.

It includes a sophisticated expert system modeling environment for integrated applications that require a high degree of automation. It also includes a complete set of identification and diagnostic tools to facilitate user-directed modeling.

SCA's expert system addresses single series univariate models as well as multivariate models. Using a sophisticated and highly robust expert system approach, the SCA System derives parsimonious models that facilitate reliable and accurate forecasting.

The SCA System also provides advanced outlier detection and adjustment capabilities that allows for the joint estimation of outlier effects and model parameters. Using this advanced feature of the SCA System, the model parameters are automatically desensitized from structural changes in the data (e.g., abnormal pulses, temporary changes, and level shifts).

In addition, SCA's outlier handling capabilities are extended into forecasting. Through this state-of-the art procedure, forecasts are desensitized from the effect of outliers. If outliers are not handled appropriately, especially near the forecasting origin, the impact of anomolous data can be severly detrimental to forecasting accuracy and reliability.

It is recognized that the forecasting process is more than the calculation of forecast values. In a era of change and uncertainty, knowledge of system structure and the interplay of variables is important. Quantitative forecasting methods, such as those provided by SCA, are invaluable in providing such knowledge.

To facilitate quantitative forecasting, SCA provides a myriad of forecasting and modeling methods to address any business application where forecasting accuracy is of paramount importance. A representative sample of the methods addressed by SCA are categorized below.

Univariate methods
Box-Jenkins ARIMA models Intervention/impact adjustment models General exponential smoothing methods Simple methods (e.g., moving average)
Multiple-input methods
Multiple-input transfer function models General regression methods Traditional econometric models (2SLS, 3SLS, etc.) Multivariate Adaptive Regression Splines (MARSPLINE) Generalized Additive Models (GAM) Alternating Conditional Expectations (ACE) Projection Pursuit Regression (PPREG)
Multivariate methods
Simultaneous transfer function models Vector autoregressive models Vector ARMA models State Space models/Kalman Filtering


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TEL: +708-771-4567  FAX: +708-771-4569 EMAIL: sca@scausa.com