Seasonality-Based Rules for Data Anomaly Detection

• US 20110218836A1
• Filed: 03/04/2010
• Published: 09/08/2011
• Est. Priority Date: 03/04/2010
• Status: Active Grant

• Current Assignee: Amrinder Arora, Christopher Kakovitch, Lusine Yepremyan
• Original Assignee: Amrinder Arora, Christopher Kakovitch, Lusine Yepremyan
• Inventors: Arora, Amrinder, Kakovitch, Christopher, Yepremyan, Lusine
• Granted Patent: US 8,306,943 B2
• Time in Patent Office: Days
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

In one embodiment, we describe a method that generates seasonality rules for anomaly detection for a hierarchical/tree based data structure. A new algorithm for processing nodes in hierarchy, as well as business rules for nodes, is described. Variations and examples are given to describe different scopes and embodiments of the invention. Exclusion criteria and children nodes are used as some examples for the implementations, with flow charts to describe the methods of application, as examples.

Claims

1. A method for anomaly detection, using seasonality based rules, said method comprising:

receiving a set of parameters;

obtaining a set of criteria from a storage unit;

with respect to a product class, examining a first criteria to see if said first criteria is met;

if said first criteria is met, then examining a second criteria to see if said second criteria is met; and

if said second criteria is met, then checking for exclusions, applying a first treatment for non-excluded items, and creating rules;

otherwise, if said second criteria is not met, then examining a third criteria to see if said third criteria is met; and

if said third criteria is not met, then generating children nodes for a hierarchy, and returning said generated children nodes to said product class.

2. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

examining for existence of strong seasonality or weak seasonality.

3. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

applying a second treatment for non-excluded items, and creating rules.

4. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

receiving a list of trade parties.

5. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

adding to a list of non-excluded trade parties.

6. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

examining if there are more trade parties.

7. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

terminating a process.

8. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

generating a tree or hierarchical structure.

9. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

generating a parent node.

10. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

generating a child node.

11. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

assigning or choosing a class.

12. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

considering N different classes, wherein N is an integer bigger than 1.

13. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

calculating a first value for each attribute of a class.

14. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

determining a maximum value of a set of second values.

15. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

determining values beyond one or more thresholds.

16. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

determining anomalies.

17. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

determining odd shipments at a custom office.

18. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

reexamining odd shipments at a custom office.

19. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

generating seasonality rules.

20. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

aggregating reports for two or more trading parties.