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:
Oct. 08, 2019

Filed:

Dec. 01, 2015
Applicant:

Palo Alto Research Center Incorporated, Palo Alto, CA (US);

Inventors:

Ryan A. Rossi, Mountain View, CA (US);

Rong Zhou, San Jose, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/02 (2006.01); G06K 9/00 (2006.01); G06N 20/00 (2019.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); G06N 20/10 (2019.01); G06N 5/00 (2006.01); G06N 20/20 (2019.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06K 9/00 (2013.01); G06K 9/00496 (2013.01); G06K 9/00973 (2013.01); G06K 9/469 (2013.01); G06K 9/6273 (2013.01); G06N 20/10 (2019.01); G06N 5/003 (2013.01); G06N 5/02 (2013.01); G06N 20/20 (2019.01);
Abstract

System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.


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