Neural contrastive anomaly detection

Abstract

Systems and methods are described for detecting anomalies within data, such as time series data. In one example, unlabeled data, such as time series data, may be obtained. At least one data point, representing an artificial anomaly, may be inserted into the data. The data may then be divided into a number of different windows. The windows may have a fixed size and may at least partially overlap in time. The data contained within different windows may be compared, to each other and to the injected data point, to determine an anomaly score for individual windows. The anomaly score may indicate a likelihood that a given window contains an anomaly. In a specific example, a convolution neural network may be trained based on the data and inserted data points representing anomalies, where a contrastive loss function is used to represent different portions of the data in the neural network.