Nasdaq, Inc.
SYSTEMS AND METHODS OF PROCESSING DIVERSE DATA SETS WITH A NEURAL NETWORK TO GENERATE SYNTHESIZED DATA SETS FOR PREDICTING A TARGET METRIC

Abstract:

A computer system is provided that is programmed to receive data sets, a target metric, and a parameter that indicates a desired number of synthesized data sets. A memory stores instructions and data including the input data sets, the target metric, the parameter that indicates a desired number of synthesized data sets, and a neural network. The neural network includes a summing node and multiple processing nodes. At least one hardware processor is configured to perform operations where each processing node of a neural network weights input data set values, determines gating operations to select processing operations, and generates a node output by applying the gating operations to weighted input data set values. Weighted node outputs from the processing nodes produce a value for the target parameter. The neural network is iteratively trained by modifying the gating operations, the input weight values, and the node output weight value until the neural network converges. Then, one or more nodes is selected having a larger magnitude node output weight value. For each selected node, a subset of the input data sets and a subset of the gating operations are selected. The selected input data set values are processed with the selected processing nodes using the selected subset of gating operations to produce synthesized data sets. Human-understandable names are generated for each of the synthesized data sets based on names of the selected input data sets and the selected subset of gating operations.