Use of object group models and hierarchies for output predictions

A system, comprising:

• a network node in data communication with one or more remote nodes, the network node including one or more processors;
• and
• one or more non-transitory computer-readable storage mediums containing instructions configured to cause the one or more processors to perform steps including:
• receiving, by the network node, past data stored in a multidimensional online analytical processing database, wherein the past data is organized according to a spatial hierarchy that includes a plurality of levels and an object hierarchy that includes a plurality of levels, wherein each level in each hierarchy includes a corresponding amount of detail, wherein the plurality of levels include one or more related object groups;
• evaluating a selection of a level in the spatial hierarchy and a level in the object hierarchy, wherein the selected levels in each hierarchy have a corresponding amount of detail;
• generating a cyclical aspect component using past data located at the selected levels in each hierarchy;
• evaluating a selection of a different level in the spatial hierarchy and a different level in the object hierarchy, wherein the different levels in each hierarchy have a greater corresponding amount of detail;
• generating a movement component using past data located at the different levels in each hierarchy;
• generating a base requirement component for a related object group in the plurality of levels using the cyclical aspect component and the movement component;
• generating an individual approximated global output fraction for a member of a related object group using the past object data and a global output fraction model, wherein the individual approximated global output fraction is a proportion of total outputs for the related object group expected for a particular object;
• and
• predicting approximated output for the particular object using the base requirement component and the individual approximated global output fraction for the particular object, wherein predicting includes multiplying the base requirement component by the individual approximated global output fraction.