Abstract: A system and method are disclosed to train machine learning models, generate predictions, and evaluate the predictions as individual probability density functions. Embodiments include a computer comprising a processor and memory and configured to train a first machine learning model to predict a mean demand of one or more items. Embodiments train a second machine learning model to predict a variance associated with the predicted mean demand. Embodiments use the first and second machine learning models and received current sales data to predict a negative binomial variance of demand of the one or more items, comprising a confidence interval specifying a stocking level for the one or more items that will satisfy a defined number of estimated outcomes. Embodiments generate an individual probability density function using the predicted mean demand of one or more items and the predicted negative binomial variance of demand, and evaluate the individual probability density function.

Abstract: A system and method are disclosed for detecting and reacting to unseen events in demand forecasting, comprising preparing, by a server, data from a supply chain domain and entity to predict a demand, selecting features in the prepared data and training a machine learning model, generating a demand prediction with residual time series corrections, using the machine learning model, monitoring the demand prediction and data from the supply chain domain and entity to detect an occurrence of an unseen event; and in response to detecting the occurrence of the unseen event, revising the demand prediction by updating the machine learning model. The system and method further comprises detecting that a greater than threshold increase has occurred in a prediction of a target variable, and revising the demand prediction further comprises performing a second residual time series corrections that incorporates time information associated with the unseen event.