公开(公告)号：US20220342861A1

公开(公告)日：2022-10-27

申请号：US17239261

申请日：2021-04-23

Applicant: Capital One Services, LLC

Inventor： Vannia Gonzalez Macias ,  Talha Koc ,  Mark Davis ,  Prarthana Bhattarai ,  Mark Roberts ,  Alan Rozet ,  Mengfei Shao

IPC: G06F16/215 ,  G06F16/21

Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

公开(公告)号：US11789915B2

公开(公告)日：2023-10-17

申请号：US17239261

申请日：2021-04-23

Applicant: Capital One Services, LLC

Inventor： Vannia Gonzalez Macias ,  Talha Koc ,  Mark Davis ,  Prarthana Bhattarai ,  Mark Roberts ,  Alan Rozet ,  Mengfei Shao

IPC: G06F16/215 ,  G06F16/21

CPC classification number: G06F16/215 ,  G06F16/211

Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.