The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 27, 2022

Filed:

May. 12, 2020
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Xiang Yu Yang, Xi'an, CN;

Deng Xin Luo, Xi'an, CN;

Jing Du, Beijing, CN;

Zhong Fang Yuan, Xi'an, CN;

Tong Liu, Xi'an, CN;

Li Jia Lu, Xi'an, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06F 16/28 (2019.01); G06F 16/901 (2019.01); G06N 20/00 (2019.01); G06Q 30/04 (2012.01); G06Q 20/14 (2012.01); G06F 16/56 (2019.01);
U.S. Cl.
CPC ...
G06F 16/285 (2019.01); G06F 16/56 (2019.01); G06F 16/9024 (2019.01); G06N 20/00 (2019.01); G06Q 20/145 (2013.01); G06Q 30/04 (2013.01);
Abstract

Described are techniques for determining statistical properties of time series data. The techniques include a method comprising graphing, from time series data, a time series data graph. The method further comprises iteratively segmenting the time series data graph into respective pluralities of subgraphs using respective segmentation schemes until a first plurality of subgraphs generated by a first segmentation scheme exhibits a similarity between respective subgraphs of the first plurality of subgraphs satisfying a similarity threshold. The first segmentation scheme can be selected from: an equidistant segmentation scheme, a local extrema segmentation scheme, and a windowed segmentation scheme. The method further comprises associating a classification to the time series data based on the first segmentation scheme. The classification can be indicative of one selected from: stationarity of the time series data, periodicity of the time series data, and trending of the time series data.


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