SYSTEM AND METHOD FOR FORECASTING WITH SPARSE TIME PANEL SERIES USING DYNAMIC LINEAR MODELS

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.