AUTOMATICALLY GENERATING VOLUME FORECASTS FOR DIFFERENT HIERARCHICAL LEVELS VIA MACHINE 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.
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