Synthetic data generation for predictive models

A computing system for generating a synthetic data set for use with a predictive model comprising:

• a processor;
• and
• a memory in communication with the processor and comprising instructions which, when executed by the processor, program the processor to:
• receive structured electronic data comprising a plurality of data items, wherein each data item comprises a plurality of features;
• perform a first clustering process to generate a first set of clusters based on similarities and differences of the features of the data items;
• train a machine learning (ML) model using the first set of clusters to predict an assignment of a new data item to a cluster in the first set of clusters;
• receive a new data item comprising a plurality of features;
• provide the features of the new data item as input to the trained ML model and receive as output from the trained ML model a first cluster of the first set of clusters to which the new data item is assigned;
• perform a second clustering process on the first cluster to generate a plurality of time-series clusters for the data items included in the first cluster;
• calculate a representative time-series data set based on the plurality of time-series clusters;
• generate a data signal representing a trend in a data value over a recent period of time;
• and
• construct the synthetic data set based on the representative time-series data set and the data signal.

A computer program product for generating a synthetic data set for use with a predictive model, the computer program product comprising a non-transitory computer-readable medium having computer readable program code stored thereon, the computer readable program code configured to:

• receive structured electronic data comprising a plurality of data items, wherein each data item comprises a plurality of features;
• perform a first clustering process to generate a first set of clusters based on similarities and differences of the features of the data items;
• train a machine learning (ML) model using the first set of clusters to predict an assignment of a new data item to a cluster in the first set of clusters;
• receive a new data item comprising a plurality of features;
• provide the features of the new data item as input to the trained ML model and receive as output from the trained ML model a first cluster of the first set of clusters to which the new data item is assigned;
• perform a second clustering process on the first cluster to generate a plurality of time-series clusters for the data items included in the first cluster;
• calculate a representative time-series data set based on the plurality of time-series clusters;
• generate a data signal representing a trend in a data value over a recent period of time;
• and
• construct the synthetic data set based on the representative time-series data set and the data signal.