Abstract: Systems and methods include receiving a machine learning model that is configured to detect anomalies in network devices operating in a multi-layer network, wherein the machine learning model is trained via unsupervised learning that includes training the machine learning model with unlabeled data that describes an operational status of the network devices over time; receiving live data related to a current operational status of the network devices; analyzing the live data with the machine learning model; and detecting an anomaly related to any of the network device based on the analyzing.

Abstract: Systems and methods include receiving a machine learning model that is configured to detect anomalies in network devices operating in a multi-layer network, wherein the machine learning model is trained via unsupervised learning that includes training the machine learning model with unlabeled data that describes an operational status of the network devices over time; receiving live data related to a current operational status of the network devices; analyzing the live data with the machine learning model; and detecting an anomaly related to any of the network device based on the analyzing.

Abstract: Systems and methods implemented by a computer to detect abnormal behavior in a network include obtaining Performance Monitoring (PM) data including one or more of production PM data, lab PM data, and simulated PM data; determining a model based on machine learning training with the PM data; receiving live PM data from the network; utilizing the live PM data with the model to detect an anomaly in the network; and causing an action to address the anomaly.

Abstract: Systems and methods of automated feature selection and pattern discovery of multi-variate time-series include obtaining a multi-variate times-series from a network; preprocessing the multi-variate times-series to account for sampling intervals and missing data in the multi-variate times-series; determining a distance matrix for the multi-variate times-series which estimates correlation among features in the multi-variate times-series; performing clustering on the distance matrix; reducing dimensionality of the multi-variate times-series based on the clustering to provide a lower-dimensionality time-series; and providing the lower-dimensionality time-series to one or more applications configured to analyze the multi-variate times-series from the network, wherein the lower-dimensionality time-series provides similar information as the multi-variate time-series with fewer dimensions thereby improving computational complexity of the one or more applications.

Abstract: Systems and methods of automated feature selection and pattern discovery of multi-variate time-series include obtaining a multi-variate times-series from a network; preprocessing the multi-variate times-series to account for sampling intervals and missing data in the multi-variate times-series; determining a distance matrix for the multi-variate times-series which estimates correlation among features in the multi-variate times-series; performing clustering on the distance matrix; reducing dimensionality of the multi-variate times-series based on the clustering to provide a lower-dimensionality time-series; and providing the lower-dimensionality time-series to one or more applications configured to analyze the multi-variate times-series from the network, wherein the lower-dimensionality time-series provides similar information as the multi-variate time-series with fewer dimensions thereby improving computational complexity of the one or more applications.

Abstract: A system to predict events in a telecommunications network includes a processor; and memory storing instructions that, when executed, cause the processor to, responsive to obtained Performance Monitoring (PM) data over time from the telecommunications network, reduce an n-dimensional time-series into a 1-dimensional distribution, n being an integer represent a number of different PM data, wherein the n different PM data relate to a component, device, or link in the telecommunications network, utilize one or more forecast models to match the 1-dimensional distribution and to extrapolate the 1-dimensional distribution towards future time, and display a graphical user interface of a graph of the 1-dimensional distribution and the extrapolated 1-dimensional distribution, wherein the graph displays a probability of the component, device, or link being normal versus time. Also, techniques are described herein for labeling of PM data for use in supervised Machine Learning (ML).

Abstract: Systems and methods implemented by a computer to detect abnormal behavior in a network include obtaining Performance Monitoring (PM) data including one or more of production PM data, lab PM data, and simulated PM data; determining a model based on machine learning training with the PM data; receiving live PM data from the network; utilizing the live PM data with the model to detect an anomaly in the network; and causing an action to address the anomaly.