Automatically generating volume forecasts for different hierarchical levels via machine learning models

A method for autonomously generating a volume forecast, the method comprising:

• accessing, using a volume forecasting engine, one or more volume information units from a volume forecast data management tool, wherein the one or more volume information units comprise volume forecast data, and wherein the volume forecast data comprises one or more of package received time or package information;
• extracting, using the volume forecasting engine, one or more features from the volume information units, wherein the one or more features are representative of one or more of the package received time or the package information;
• and
• generating, using a volume forecast learning model and the one or more features, an output comprising the volume forecast for a particular hierarchical level.

A non-transitory computer readable storage medium storing computer-readable program instructions that, when executed, cause a computer to:

• access volume forecast data associated with at least one shipment of one or more parcels;
• feed the volume forecast data through at least one volume forecast learning model;
• and
• based at least in part on the feeding of the volume forecast data through the at least one volume forecast learning model, predict a first quantity of parcels that will arrive at a destination for a particular time period at a particular hierarchical level, the particular hierarchical level being a category of generated volume forecast.