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:
Jun. 16, 2020

Filed:

Dec. 24, 2019
Applicant:

Sas Institute Inc., Cary, NC (US);

Inventors:

Yue Li, Irvine, CA (US);

Michele Angelo Trovero, Cary, NC (US);

Phillip Mark Helmkamp, Apex, NC (US);

Jerzy Michal Brzezicki, Cary, NC (US);

Macklin Carter Frazier, Raleigh, NC (US);

Timothy Patrick Haley, Cary, NC (US);

Randy Thomas Solomonson, Cary, NC (US);

Sangmin Kim, Chapel Hill, NC (US);

Steven Christopher Mills, Raleigh, NC (US);

Yung-Hsin Chien, Apex, NC (US);

Ron Travis Hodgin, Roxboro, NC (US);

Jingrui Xie, Jingrui, NY (US);

Assignee:

SAS INSTITUTE INC., Cary, NC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06F 16/2458 (2019.01); G06F 16/28 (2019.01); G06N 3/04 (2006.01); G06F 16/242 (2019.01); G06F 16/248 (2019.01); G06F 16/26 (2019.01); H04L 12/24 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 16/248 (2019.01); G06F 16/2423 (2019.01); G06F 16/2474 (2019.01); G06F 16/26 (2019.01); G06F 16/285 (2019.01); G06N 3/0454 (2013.01); H04L 41/16 (2013.01);
Abstract

A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.


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