Abstract: A system and method for forecasting the sales of a new item, i.e., one with no historical sales data, is presented. Two matrices are presented, a feature matrix and a sales matrix. The matrices are divided into training matrices and prediction matrices. The training matrices are decomposed, then regression analysis is performed to determine the weight of various columns of the training feature matrix. Thereafter, the weights could be used on the training prediction matrix to predict sales. The sales predictions can be used to order SKUs for a retailer or distributor. Other embodiments are also disclosed herein.

Abstract: A system and method for grouping units for forecasting purposes is presented. A plurality of stock keeping units (SKUs) is presented to an embodiment. Initial medoids are chosen based on a vertex within a set of vertices, each of which represent a SKU. Then, each vertex within the set of vertices is associated with its closest medoid to form initial clusters. There can be a cap on the number of vertices in each cluster. Thereafter, an iterative algorithm is performed wherein a probability is assigned to each vertex. One or more vertices are randomly chosen, with the weights of the vertices weighting the random choice. The chosen one or more vertices are moved to another cluster. The algorithm is performed until no further improvements result from moving one or more vertices to another cluster. Other embodiments are also disclosed herein.

Abstract: A system and method for grouping units for forecasting purposes is presented. A sales forecast for a set of stock keeping units (SKUs) is desired. The SKUs are separated into clusters based on the similarity of the SKUs. Then a set of Bayesian multivariate dynamic linear models is chosen to be used to calculate a sales forecast for each of the clusters of SKUs. The accuracy of each dynamic linear model is determined in a training procedure and a set of weights for each dynamic linear model is calculated. Thereafter, the weights can be used with the dynamic linear models to create a weighted average forecast model. The training procedure can be run periodically to maintain the accuracy of the weights. Each procedure can operate on a sliding window of data. Other embodiments are also disclosed herein.

Abstract: A system and method for forecasting sales is presented. A method might begin by receiving a request to produce a demand forecast for a stock keeping unit (SKU). Then, the SKU is placed in one or more clusters. A cluster seasonality profile is calculated for each of the one or more clusters. An item seasonality profile is calculated for the SKU. Then the demand forecast for the SKU is generated. The demand forecast is adjusted using the cluster seasonality profile for each of the one or more clusters and the item seasonality profile for the SKU. Then inventory can be ordered based on the adjusted demand forecast. Other embodiments are also disclosed herein.

Abstract: A system and method for forecasting sales is presented. A set of stock keeping units (SKUs) is received, then placed into a plurality of clusters of SKUs. A set of dynamic linear models and associated parameters are chosen to create a forecast for each cluster in the plurality of clusters of SKUs. A sequential learning algorithm is used to create a weighting of each dynamic linear model in the set of dynamic linear models. The weighting of each dynamic linear model is updated using a particle learning algorithm. The particle learning algorithm comprises performing a resampling the set of dynamic linear models using a set of weights, propagating a set of state vectors through the set of dynamic linear models based on the resampling, and performing a sampling to determine parameters for the set of dynamic linear models. Then a sales forecast is generated and inventory can be ordered. Other embodiments are also disclosed herein.

Abstract: A system and method for grouping units for forecasting purposes is presented. A sales forecast for a set of stock keeping units (SKUs) is desired. The SKUs are separated into clusters based on the similarity of the SKUs. Then a set of Bayesian multivariate dynamic linear models is chosen to be ‘21retfgvd5xzrtfgvbyhsdcused to calculate a sales forecast for each of the clusters of SKUs. The accuracy of each dynamic linear model is determined in a training procedure and a set of weights for each dynamic linear model is calculated. Thereafter, the weights can be used with the dynamic linear models to create a weighted average forecast model. The training procedure can be run periodically to maintain the accuracy of the weights. Each procedure can operate on a sliding window of data. Other embodiments are also disclosed herein.

Abstract: A system and method for forecasting the sales of a new item, i.e., one with no historical sales data, is presented. Two matrices are presented, a feature matrix and a sales matrix. The matrices are divided into training matrices and prediction matrices. The training matrices are decomposed, then regression analysis is performed to determine the weight of various columns of the training feature matrix. Thereafter, the weights could be used on the training prediction matrix to predict sales. The sales predictions can be used to order SKUs for a retailer or distributor. Other embodiments are also disclosed herein.

Abstract: A system and method for grouping units for forecasting purposes is presented. A plurality of stock keeping units (SKUs) is presented to an embodiment. Initial medoids are chosen based on a vertex within a set of vertices, each of which represent a SKU. Then, each vertex within the set of vertices is associated with its closest medoid to form initial clusters. There can be a cap on the number of vertices in each cluster. Thereafter, an iterative algorithm is performed wherein a probability is assigned to each vertex. One or more vertices are randomly chosen, with the weights of the vertices weighting the random choice. The chosen one or more vertices are moved to another cluster. The algorithm is performed until no further improvements result from moving one or more vertices to another cluster. Other embodiments are also disclosed herein.

Abstract: A system and method for forecasting sales is presented. A method might begin by receiving a request to produce a demand forecast for a stock keeping unit (SKU). Then, the SKU is placed in one or more clusters. A cluster seasonality profile is calculated for each of the one or more clusters. An item seasonality profile is calculated for the SKU. Then the demand forecast for the SKU is generated. The demand forecast is adjusted using the cluster seasonality profile for each of the one or more clusters and the item seasonality profile for the SKU. Then inventory can be ordered based on the adjusted demand forecast. Other embodiments are also disclosed herein.