| CPC G06N 3/088 (2013.01) [G06N 3/045 (2023.01); G06N 3/047 (2023.01)] | 13 Claims |
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1. A non-transitory computer-readable medium configured to store a program executable by a processing system, the program including instructions configured to cause the processing system to:
obtain raw, heterogeneous time-series data from multiple components of a communication network to be monitored at the processing system,
convert all of the raw, heterogeneous time-series data to a two-dimensional image format by constructing tensors from all of the raw, heterogeneous time-series data,
create a multi-dimensional matrix comprising a plurality of image windows formed from the raw, heterogeneous time-series data using the processing system,
detect or forecast at least one of a network threshold crossing, a network alarm, a network user quality-of-experience, or a network anomaly by detecting, utilizing a deep neural network, outliers of the raw, heterogeneous time-series data with respect to the multi-dimensional matrix using an unsupervised deep learning process on the two-dimensional image format of all of the raw, heterogeneous time-series data, the unsupervised deep learning process including training a first path utilizing a Generalized Adversarial Network (GAN) learning technique to obtain a discriminator from a trained GAN data and training a second path utilizing a Bidirectional GAN (BiGAN) learning technique to obtain an encoder from a trained BiGAN data and executed by the processing system to transfer knowledge of other experimented domains for imbalanced datasets anomaly detection by identifying portions of the raw, heterogeneous time-series data as normal by the absence of an indicator that the identified portions are abnormal such that a determined low probability that another portion of the raw, heterogeneous time-series data is within the normal portions of the raw, heterogeneous time-series data indicates an anomaly, wherein a first probability is determined from the discriminator, a second probability is determined from the encoder, and the detected outliers are determined based on a combination of the first probability and the second probability; and
localize the detected or forecasted at least one of the network threshold crossing, the network alarm, the network user quality-of-experience, or the network anomaly within the raw, heterogeneous time-series data by localizing the detected outliers in the obtained, raw, heterogeneous time-series data by detecting at least one image window of the plurality of image windows containing the detected outliers.
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