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 July 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 processor then identifies a subset of anomaly detection methods relevant to these known classes, filtering from a larger pool of methods. An anomaly detection score is calculated for each method based on the occurrence of false positives and false negatives, utilizing a sliding window approach across all datasets. This score is then compared against a calculated threshold to recommend the most suitable anomaly detection method for further application on similar time series data.

The claims further detail that the known classes may include factors such as seasonality, trend, 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 identify 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 diverse array 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 different domains.

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