Abstract: Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

Abstract: Embodiments are disclosed for autonomously generating volume forecasts. An example method includes accessing volume information units from a volume forecast data management tool. The example method further includes extracting features from volume information units, wherein the features are representative of one or more of a package received time, or package information. The features can be categorized by different hierarchical level information. The example method further includes generating, using a volume forecast learning model and the features, an output comprising a volume forecast for a particular hierarchical level. Corresponding apparatuses and non-transitory computer readable storage media are also provided.