Patent ID: 8738421

Claim:
A method using driver moderator model for providing one-step-ahead stock keeping unit sales predictions in the presence of promotions the method comprising: pooling observations across SKUs and stores; using L1 norm regularized epsilon insensitive regression for simultaneously selecting a small and relevant subset of features; estimating driver-moderator model coefficients; transforming historical unit sales, price and promotion for SKU-stores by time period; calculating features for training data by using the data of historical unit sales, price and promotion for SKU-stores by time period and using the data of static SKU and store characteristics; determining parameters for the model training wherein the determining step uses validation dataset for each category; training the model for each category by accepting the parameters from the determining step and further accepting training data set for each category; and determining the model coefficients, utilizing a computer, based on the model training; wherein the computer determines the model coefficients using an objective function and reducing complexity by taking the difference of sum of the absolute error and a predetermined epsilon value, wherein the difference of sum of the absolute error and a predetermined epsilon value is calculated by the objective function which includes the following formula min w ⁢ { ∑ d = 1 D ⁢ ∑ k = 0 K ⁢  w dk  + λ ⁢ 1 n ⁢ ∑ ijt ⁢ max ⁡ (  e ijt  - ɛ , 0 ) } wherein: w dk : the interaction effect parameter for driver variable d moderated by moderator variable k; e ijt : error; i: normalized sales volume of SKU; j: store number; t: time period; λ: tradeoff parameter between accuracy in training dataset and the model complexity; ε: a predetermined error level; n: number of observation.