Intuit Inc.
CONVOLUTIONAL RECURRENT GENERATIVE ADVERSARIAL NETWORK FOR ANOMALY DETECTION

Abstract:

An anomaly detection service executed by a processor may receive multivariate time series data and format the multivariate time series data into a final input shape configured for processing by a generative adversarial network (GAN). The anomaly detection service may generate a residual matrix by applying the final input shape to a generator of the GAN, the residual matrix comprising a plurality of tiles. The anomaly detecting service may score the residual matrix by identifying at least one tile of the plurality of tiles having a value beyond a threshold indicating an anomaly. The processor may perform at least one remedial action for the anomaly in response to the scoring.