Verint Systems. has been granted a patent for a method that automates the selection of anomaly detection techniques for various classes of time series data. The process involves classifying datasets, filtering detection methods, and evaluating their effectiveness to recommend the most suitable method for identifying anomalies. GlobalData’s report on Verint Systems gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Verint Systems, Network traffic analysis was a key innovation area identified from patents. Verint Systems's grant share as of June 2024 was 61%. Grant share is based on the ratio of number of grants to total number of patents.
Anomaly detection method selection for time series data
The patent US12032543B2 outlines a method and system for automatically determining an appropriate anomaly detection method tailored for specific classes of time series data. The process begins with a processor receiving multiple datasets, each linked to distinct predefined time series characteristics and classified into known categories based on their expected properties. The system then identifies a subset of anomaly detection methods relevant to these known classes, filtering from a broader set of techniques. An anomaly detection score is calculated for each method, taking into account false positives and negatives through a sliding window analysis. This score is compared against a calculated threshold to recommend the most suitable anomaly detection method for further application on similar time series data.
The patent also specifies that the known classes may include factors such as seasonality, trends, concept drift, or missing time steps. The anomaly detection scores can be derived from metrics like the windowed F-score or the Numenta anomaly benchmark (NAB) score. Additionally, the datasets may be pre-annotated to highlight anomalies, with some datasets containing unlabeled data during a probationary period. The system is designed to accommodate various types of anomalies, including collective, point, and contextual types, and utilizes a range of anomaly detection methods, such as windowed Gaussian, SARIMAX, and Facebook Prophet, among others. This comprehensive approach aims to enhance the accuracy and efficiency of anomaly detection in time series analysis across diverse domains.
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