Abstract: A machine learning system is trained to predict resource usage by cells or network slices of a mobile network. For example, a computing system obtains respective datasets for the cells or network slices. Each dataset comprises time steps and respective values for a performance metric of the corresponding one of the cells or network slices. The computing system groups, based on a clustering algorithm applied to (1) the datasets, or (2) the cells or network slices, the datasets into clusters of datasets. The computing system applies, to a subset of most-recent time steps and corresponding values of each dataset of a first cluster of the clusters, a transformation to obtain a set of time steps and corresponding standardized values with which a machine learning system is trained to generate predicted values at future time steps of the datasets of the first cluster.

Abstract: A device may receive network traffic data that includes network traffic packet sizes, and may transform the network traffic data into transformed data. The device may process the transformed data, with a machine learning model, to generate an embedding, and may obtain a similarity metric for the embedding. The device may create a graph with nodes and edges based on the embedding and the similarity metric, and may process the graph, with a community detection model, to identify network traffic categories for the network traffic data. The device may perform one or more actions based on the network traffic categories.

Abstract: A device may receive real network data associated with a network, and may receive a random latent vector and a random process sample. The device may utilize the random latent vector with a generative adversarial network (GAN) model to generate synthetic network data, and may train the GAN model with the real network data and the synthetic network data to generate a trained GAN model. The device may utilize the random process sample with a random process to generate simulated network data, and may 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. The device may combine the weighted real network data, the weighted synthetic network data, and the weighted simulated network data to generate interpolated network data, and may perform actions based on the interpolated network data.

Abstract: A device may receive network traffic data that includes network traffic packet sizes, and may transform the network traffic data into transformed data. The device may process the transformed data, with a machine learning model, to generate an embedding, and may obtain a similarity metric for the embedding. The device may create a graph with nodes and edges based on the embedding and the similarity metric, and may process the graph, with a community detection model, to identify network traffic categories for the network traffic data. The device may perform one or more actions based on the network traffic categories.

Abstract: An example system for performing root cause analysis for a plurality of network devices includes one or more processors implemented in circuitry and configured to: receive telemetry data from the plurality of network devices; apply an artificial intelligence (AI) anomaly detection model, trained on historical telemetry data to detect anomalies in the historical telemetry data, to the received telemetry data to detect one or more anomalies in the received telemetry data; and apply an AI root cause analysis mode, trained on historical data, to the anomalies to determine a root cause of an issue causing the one or more anomalies.

Abstract: A device may receive time series data, and may define a first quantity of steps into past data utilized to make future predictions, a second quantity of steps into the future predictions, and a third quantity of steps to skip in the future predictions. The device may determine whether the second quantity is equal to the third quantity. When the second quantity is equal to the third quantity, the device may process the time series data, with a plurality of machine learning models, to generate a plurality of future predictions that do not overlap, may merge the plurality of future predictions into a list of future predictions, and may provide the list for display. When the second quantity is not equal to the third quantity, the device may process the time series data, with the plurality of machine learning models, to generate another plurality of future predictions that do overlap.

Abstract: A device may receive site data identifying raw data or key performance indicators associated with a plurality of sites, and may calculate a similarity score matrix based on the site data. The device may group the site data into data clusters based on the similarity score matrix, and may identify training data and validation data based on the data clusters. The device may generate a meta model, and may train the meta model based on the training data. The device may validate the meta model based on the validation data, and may create site-specific models, for each of the plurality of sites, based on the meta model and the site data. The device may utilize the site-specific models with corresponding new site data of the plurality of sites to generate predictions for the plurality of sites.