Systems and methods for data stream simulation

A cloud computing system for generating a synthetic data stream, comprising:

• at least one processor;
• and
• at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the cloud computing system to perform operations comprising:
• receiving, from an interface, a request to generate a synthetic data stream, the request indicating a reference data stream from a stream source;
• receiving, from the interface, a correlation metric for the reference synthetic data stream;
• and
• generating a synthetic data stream that tracks the reference data stream, wherein the generating comprises repeatedly updating data models of the reference data stream at predetermined time intervals, a repeat comprising:
• retrieving, from a model storage, a current data model of the reference data stream, the current data model comprising a generative adversarial network that generates an output resembling current characteristics of the reference data stream;
• generating the synthetic data stream using the current data model of the reference data stream;
• generating a new data model of the reference data stream;
• evaluating performance criteria of the new data model by one or more duplicate elements in the synthetic data stream and the reference data stream, a prevalence of a common value in the synthetic data stream and the reference data stream, a maximum difference of rare values in the synthetic data stream and the reference data stream, and differences in schema between the synthetic data stream and the reference data stream;
• storing, in the model storage, the new data model;
• and
• updating, in the model storage, the current data model with the new data model.