US 12,340,317 B2
Anomaly detection for non-stationary data
Azadeh Moghtaderi, San Jose, CA (US); Gagandeep Singh Bawa, Campbell, CA (US); and David Schwarzbach, San Jose, CA (US)
Assigned to EBAY INC, San Jose, CA (US)
Filed by eBay Inc., San Jose, CA (US)
Filed on Sep. 12, 2019, as Appl. No. 16/569,181.
Application 16/569,181 is a continuation of application No. 14/588,355, filed on Dec. 31, 2014, granted, now 10,445,644.
Prior Publication US 2020/0082284 A1, Mar. 12, 2020
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06N 5/04 (2013.01) [G06N 20/00 (2019.01)] 17 Claims
OG exemplary drawing
 
1. A method comprising:
incorporating one or more anomaly detection applications into a computing system, the one or more anomaly detection applications configuring one or more computer processors of the computing system to perform operations for generating a user interface for representing a health of a process executing within the computing system, the operations comprising:
receiving an initial time series of data points;
receiving a training time series of data points representing a recent subset of data points in the initial time series of data points;
modifying an outlier data point in the training time series of data points to bring the outlier data point into a range of other data points in the recent subset of data points, the modifying of the outlier data point comprising capping the outlier data point;
training at least one prediction method using at least the training time series of data points;
measuring a first actual data point, the first actual data point being based on data at a later time than the last data point in the training time series;
using the at least one prediction method to determine a predicted value corresponding to the first actual data point;
determining whether the first actual data point is anomalous based on a calculation of whether the first actual data point is statistically different from the predicted data point; and
performing the generating of the user interface, the generating comprising providing a visual representation comprising the first actual data point, the predicted data point, a visual indication of a first determination of corresponding to whether the first actual data point is anomalous, a visual indication of a second determination corresponding to whether the second actual data point is anomalous, the visual indication of the second determination representing a relative strength of the second determination, the relative strength of the second determination being represented by a size of the visual indication of the second determination relative to a size of the visual indication of the first determination.