- Patent Title: Anomaly detection for time series data having arbitrary seasonality
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Application No.: US15228570Application Date: 2016-08-04
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Publication No.: US11023577B2Publication Date: 2021-06-01
- Inventor: Shiv Kumar Saini , Natwar Modani , Balaji Vasan Srinivasan
- Applicant: ADOBE INC.
- Applicant Address: US CA San Jose
- Assignee: ADOBE INC.
- Current Assignee: ADOBE INC.
- Current Assignee Address: US CA San Jose
- Agency: Shook, Hardy & Bacon L.L.P.
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F21/55 ; G06F11/34

Abstract:
In various implementations, a method includes receiving a set of time series data that corresponds to a metric. A seasonal pattern is extracted from the set of time series data and the extracted seasonal pattern is filtered from the set of time series data. A predictive model is generated from the filtered set of data. The extracted seasonal pattern is filtered from another set of time series data where the second set of time series data corresponds to the metric. The filtered second set of time series data is compared to the predictive model. An alert is generated to a user for a value within the filtered second set of time series data which falls outside of the predictive model.
Public/Granted literature
- US20180039898A1 ANOMALY DETECTION FOR TIME SERIES DATA HAVING ARBITRARY SEASONALITY Public/Granted day:2018-02-08
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