Abstract: An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. The model is utilized to calculate the promotional uplift from the coefficients of the regression equation. The methodology utilizes a mathematical formulation that transforms regression coefficients, a combination of additive and multiplicative coefficients, into a single promotional uplift coefficient that can be used directly in promotional demand forecasting calculations.

Abstract: An aggregate User Defined Function (UDF) processing used for multi-regression is provided. The aggregate UDF initializes storage space for multiple nodes of a database environment. Data is then extracted from a relational database and populated according to groupings on each of the nodes. Multiple rows or records are then processed to create a merge and multi-regression processed.

Abstract: An improved method for forecasting and modeling product demand. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. This improved forecasting methodology enhances the applicability of regression models when dealing with logistic variables. It provides a novel technique to transform such variables into numerical values, resulting in more accurate and more efficient regression models. Furthermore, the reduction in the number of variables improves the stability and predictive power of the regression models.

Abstract: An aggregate User Defined Function (UDF) processing used for multi-regression is provided. The aggregate UDF initializes storage space for multiple nodes of a database environment. Data is then extracted from a relational database and populated according to groupings on each of the nodes. Multiple rows or records are then processed to create a merge and multi-regression processed.

Abstract: An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. The model is utilized to calculate the promotional uplift from the coefficients of the regression equation. The methodology utilizes a mathematical formulation that transforms regression coefficients, a combination of additive and multiplicative coefficients, into a single promotional uplift coefficient that can be used directly in promotional demand forecasting calculations.