Abstract: A computer-implemented method of training machine learning models for making simulated estimations is provided. The method includes collecting, from a database, a set of historical data, applying one or more transformations to the set of historical data to create a set of model features, and separating the set of model features into one or more pools, each pool comprising one or more model features of the set that are homogeneous with respect to a common value. the method further includes, for each pool, dynamically creating a training set that includes the one or more sets of model features of the pool and at least some of the historical data. The method further includes, for each training set, training a machine learning model on the training set.

Abstract: Methods for estimating multiple types of retail business volume based on multiple types of data are described. Historical volume data, prior recorded business volume, characteristics of the store including departments, and geographical location are used. Historical data is transformed into multiple features that capture seasonality, trends, the effects of special events and other business characteristics. This data can be pooled based on business characteristics, and then machine learning regression models, e.g., multiple regression trees, are fitted to each pool of data. To estimate future volume, the same features are computed, and the regression model is applied. The estimates are presented back to the user, or transmitted electronically to other systems, including systems for creating worker schedules based on predicted volumes. Systems, apparatus and computer readable media are also described.

Abstract: A computer-implemented method of training machine learning models for making simulated estimations is provided. The method includes collecting, from a database, a set of historical data, applying one or more transformations to the set of historical data to create a set of model features, and separating the set of model features into one or more pools, each pool comprising one or more model features of the set that are homogeneous with respect to a common value. the method further includes, for each pool, dynamically creating a training set that includes the one or more sets of model features of the pool and at least some of the historical data. The method further includes, for each training set, training a machine learning model on the training set.

Abstract: Methods for estimating multiple types of retail business volume based on multiple types of data are described. Historical volume data, prior recorded business volume, characteristics of the store including departments, and geographical location are used. Historical data is transformed into multiple features that capture seasonality, trends, the effects of special events and other business characteristics. This data can be pooled based on business characteristics, and then machine learning regression models, e.g., multiple regression trees, are fitted to each pool of data. To estimate future volume, the same features are computed, and the regression model is applied. The estimates are presented back to the user, or transmitted electronically to other systems, including systems for creating worker schedules based on predicted volumes. Systems, apparatus and computer readable media are also described.