Future Wei Technologies, Inc.

Huawei Technologies Co., Limited

Yirui Hu

Nandu Gopalakrishnan

System and method embodiments are provided for adaptive anomaly detection based predictor for network data. In an embodiment, a computer-implemented method in a network component for predicting values of future network time series data includes receiving, with one or more receivers, network time series data; determining, with one or more processors, whether an anomaly is detected in the network time series data; generating, with the one or more processors, a prediction associated with the network data according to a primary predictor when no anomaly is detected in the network time series data; generating, with the one or more processors, the prediction associated with the network data according to an alternative predictor when an anomaly in the network time series data is detected; and sending, with one or more transmitters, the prediction to a network controller, wherein the network controller uses the prediction to adjust network parameters.

Future network condition predictor for network time series data utilizing a hidden Markov model for non-anomalous data and a gaussian mixture model for anomalous data

An anomaly detection apparatus, method, and computer program product are provided using a probabilistic latent semantic analysis (PLSA). In use, data is received, and a PLSA is performed, based on the data. Further, one or more anomalies are detected in the data, based on the PLSA. Still yet, information identifying the one or more anomalies is stored and/or displayed.

Anomaly detection apparatus, method, and computer program using a probabilistic latent semantic analysis

A method includes receiving an anomaly data point and comparing the anomaly data point to a magnitude bounding box to produce a first comparison. The method also includes comparing the anomaly data point to a principal component analysis (PCA) bounding box to produce a second comparison and classifying the anomaly data point in accordance with the first comparison and the second comparison to produce a classification.

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Piscataway, NJ, United States of America

Yirui Hu