Abstract: A repeatability score is described for determining the quality and reliability of product sales data for generating seasonal demand forecasts. The repeatability scores are calculated from seasonal sales data stored in a data warehouse. Products are sorted based on their reliability scores such that those products that are highly seasonal and have a reliable year-to-year demand pattern are used to form initial or unique demand models. Products that are determined to be less reliable based on their repeatability score are added to the unique demand models through an iterative matching process or left out of the unique demand models.

Abstract: A product demand forecasting methodology is presented that applies daily weight values to a weekly forecast to determine daily forecasts for a product or service. The method determines daily weight values for use in forecasting current product sales by blending daily weight values calculated from historical demand data for both recent weeks and year-prior weeks. Recent weeks are used to account for recent correlations and alternation effects, and year-prior weeks are used to account for seasonality effects. The method automatically calculates a measure of significance for the daily weights calculated from the recent weeks and year-prior weeks. The significance of each week is applied as a weighting factor during the blending of recent weeks and year-prior daily weight values.

Abstract: A method to select causal factors to be used within a causal product demand forecasting framework. The methodology determines the set of factors that have statistically significant effects on historical product demand, and hence are believed to be of greatest relevance in determining product demand changes in the future. The effects of all factors are determined simultaneously and the net effect of each variable is calculated. When several factors are operative at the same time, the net influence of each factor is calculated. Lesser and redundant factors in the causal forecasting model can be eliminated to improve the stability, scalability and efficiency of the model. The method is employed to optimize causal models to achieve maximum forecast accuracy.

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: Techniques for casual demand forecasting are provided. Information is extracted from a database and is preprocessed to produce adjusted input regression variables. The adjusted input regression variables are fed to a regression service to produce regression coefficients. The regression coefficients are then post processed to produce uplifts and adjustments to the uplifts for the regression coefficients.

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: Techniques for multi-variable analysis at an aggregate level are provided. Two or more datasets having different statistical data distributions and which are not capable of being aggregated are acquired. The values for variables in the two or more datasets are normalized to produce a single integrated dataset of normalized values. The normalized values are then used to produce a demand model that represents and integrates multiple disparate products or services from the two or more datasets into a single demand model.

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 improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs information about prior promotional demand forecasts, prior product sales, and the data dispersion and the number of data samples in a product class hierarchy to dynamically determine the optimal level at which to compute promotional uplift coefficients. The methodology calculates confidence values for promotional uplift coefficients for products at each level in a merchandise product hierarchy, and uses the confidence values as a filter to determine the optimal level for promotional uplift aggregation.

Abstract: A partitioning system that provides a fast, simple and flexible method for partitioning a dataset. The process, executed within a computer system, retrieves product and sales data from a data store. Data items are selected and sorted by a data attribute of interest to a user and a distribution curve is determined for the selected data and data attribute. The total length of the distribution curve is calculated, and then the curve is divided into k equal pieces, where k is the number of the partitions. The selected data is thereafter partitioned into k groups corresponding to the curve divisions.

Abstract: An improved method for forecasting and modeling product demand for a product. The forecasting methodology employs a causal methodology, based on multiple regression techniques, to model the effects of various factors on product demand, and hence better forecast future patterns and trends, improving the efficiency and reliability of the inventory management systems. The demand forecasting technique seeks to establish a cause-effect relationship between product demand and factors influencing product demand in a market environment. Such factors may include current and recent product sales rates, seasonality of demand, product price changes, promotional activities, weather forecasts, competitive information are examples of the other primary factors which can be modeled. A product demand forecast is generated by blending the various influencing factors in accordance with corresponding regression coefficients determined through the analysis of historical product demand and factor information.

Abstract: An improved method for forecasting and modeling product demand for a product. The forecasting methodology blends information about the future price of a product with historical sales data to better forecast the future product demand. This forecasting methodoloy takes into account three main parameters that may affect the future demand for a product: seasonality (using seasonal factors), recent sales trends (through average rate of sale analysis) and the product price (by estimating the price driven demand).