US 11,636,125 B1 · 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.

Claims20

Dependent claims

A computer-implemented method, comprising:

obtaining time series data, the time series data comprising unlabeled data;
injecting at least one data point into the time series data, the at least one data point representing a first anomaly relative to the time series data;
segmenting the time series data into a plurality of overlapping windows, wherein a first window of the plurality of windows is further separated into a first context window and a first suspect window;
training a neural network to detect anomalies in the time series data using at least a portion of the time series data and the at least one data point;
and
determining an anomaly score for the first suspect window based on a comparison of the time series data in the first window with the time series data in the first context window using the trained neural network, the anomaly score indicating a likelihood that the first suspect window contains a second anomaly.

3 more elements

A non-transitory computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:

inject at least one data point into time series data, the at least one data point representing a first anomaly relative to the time series data;
segment the time series data into a plurality of windows, wherein a first window of the plurality of windows is further separated into a first context window and a first suspect window;
and
determine an anomaly score for the first suspect window based on a comparison of the time series data in the first window with the time series data in the first context window and based on the at least one data point, the anomaly score indicating a likelihood that the first suspect window contains a second anomaly.

1 more element

Dependencies are inferred from the claim text.

Neural contrastive anomaly detection

Life of this patent

Neural contrastive anomaly detection

Life of this patent

Abstract

Claims20