Unsupervised anomaly detection, diagnosis, and correction in multivariate time series data
Methods and systems for anomaly detection and correction include generating original signature matrices that represent a state of a system of multiple time series. The original signature matrices are encoded using convolutional neural networks. Temporal patterns in the encoded signature matrices are modeled using convolutional long-short term memory neural networks for each respective convolutional neural network. The modeled signature matrices using deconvolutional neural networks. An occurrence of an anomaly is determined using a loss function based on a difference between the decoded signature matrices and the original signature matrices. A corrective action is performed responsive to the determination of the occurrence of the anomaly.
NEC CORPORATION
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Dongjin Cristian Chuxu Haifeng Yuncong
Song Lumezanu Zhang Chen Chen