Advanced computational prediction models for heterogeneous data

A system, comprising:

• one or more processors;
• one or more memories;
• a sales data source comprising data for a plurality of items;
• and
• a clustering analysis engine stored in the one or more memories and executable by the one or more processors for operations comprising:
• differentiating a first set of items of the plurality of items from a second set of items of the plurality of items based on the data, the first set of items having good data and the second set of items having bad data, good data indicated by a prediction accuracy metric of the good data being below a threshold, bad data indicated by the prediction accuracy metric of the bad data being above the threshold;
• generating a first model for predicted demand levels of the first set of items, wherein the first model excludes cross-cluster effects with the second set of items;
• generating a second model for predicted demand levels of the second set of items, wherein the second model includes a residual correction;
• fitting at least one cluster-level regression model to estimate model coefficients associated with the first model and the second model;
• generating a predicted demand of a particular item of the plurality of items based on the at least one cluster-level regression model and at least one of the first model and the second model.

A method executable by one or more computing devices, the method comprising:

• receiving data for a plurality of items;
• differentiating a first set of items of the plurality of items from a second set of items of the plurality of items based on the data, the first set of items having good data and the second set of items having bad data, good data indicating a prediction accuracy metric of the data being below a threshold, bad data indicating the prediction accuracy metric of the data being above the threshold;
• generating a model for determining a predicted demand level of at least one item of the second set of items using the good data for the first set of items;
• fitting a cluster-level demand model of the one or more items using item-level attributes shared by one or more of the plurality of items;
• and
• generating a predicted demand of a particular item of the plurality of items based on the cluster-level demand model.