System and method for forecasting high-sellers using multivariate bayesian time series

A system comprising:

• a user input device;
• a display device;
• one or more processing modules;
• and
• one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform the acts of
• receiving a set of stock keeping units (SKUs);
• creating a set of one or more clusters of SKUs from the set of SKUs;
• choosing a set of Bayesian multivariate dynamic linear models to be used to calculate a sales forecast for each SKU by clusters of SKUs;
• determining a set of training weight for each dynamic linear model in the set of dynamic linear models by retrospectively regressing historical sales on sales forecasts from each dynamic linear model by time-series cross-validation;
• using the set of training weights to calculate the sales forecast for all SKUs across all clusters of SKUs;
• and
• ordering inventory based on the sales forecasts for all SKUs across all clusters of SKUs.

At least one non-transitory memory storage module having computer instructions stored thereon executable by one or more processing modules to:

• receive a set of stock keeping units (SKUs);
• create a set of one or more clusters of SKUs from the set of SKUs;
• choose a set of Bayesian multivariate dynamic linear models to be used to calculate a sales forecast for each SKU by clusters of SKUs;
• determine a set of training weight for each dynamic linear model in the set of dynamic linear models by retrospectively regressing historical sales on sales forecasts from each dynamic linear model by time-series cross-validation;
• use the set of training weights to calculate the sales forecast for all SKUs across all clusters of SKUs;
• and
• order inventory based on the sales forecasts for all SKUs across all clusters of SKUs.