Enhancing the quality of simulated network data using generative adversarial networks

A device, comprising:

• one or more memories;
• and
• one or more processors to:
• receive real network data associated with a network,
• wherein the real network data include a multivariate dataset,
• wherein the multivariate dataset set includes multivariate temporal behavior of network traffic throughput;
• receive a random latent vector and a random process sample;
• utilize the random latent vector with a generative adversarial network (GAN) model to generate synthetic network data;
• train the GAN model with the real network data and the synthetic network data to generate a trained GAN model;
• utilize the random process sample with a random process to generate simulated network data;
• apply weights to the real network data, the synthetic network data, and the simulated network data to generate weighted real network data, weighted synthetic network data, and weighted simulated network data;
• combine the weighted real network data, the weighted synthetic network data, and the weighted simulated network data to generate interpolated network data;
• and
• perform one or more actions based on the interpolated network data.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive real network data associated with a network;
• receive a random latent vector and a random process sample;
• utilize the random latent vector with a generative adversarial network (GAN) model to generate synthetic network data,
• wherein the GAN model is a Wasserstein recurrent GAN model;
• train the GAN model with the real network data and the synthetic network data to generate a trained GAN model;
• utilize the random process sample with a random process to generate simulated network data,
• wherein the simulated data network includes network traffic data at one frequency;
• apply, based on the trained GAN, weights to the real network data, the synthetic network data, and the simulated network data to generate weighted real network data, weighted synthetic network data, and weighted simulated network data;
• combine the weighted real network data, the weighted synthetic network data, and the weighted simulated network data to generate interpolated network data;
• and
• perform one or more actions based on the interpolated network data.