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.
Patent No.:
Date of Patent:
Apr. 23, 2024
Filed:
Mar. 15, 2022
International Business Machines Corporation, Armonk, NY (US);
Long Vu, Chappaqua, NY (US);
Bei Chen, Blanchardstown, IE;
Xuan-Hong Dang, Chappaqua, NY (US);
Peter Daniel Kirchner, Putnam Valley, NY (US);
Syed Yousaf Shah, Yorktown Heights, NY (US);
Dhavalkumar C. Patel, White Plains, NY (US);
Si Er Han, Xi'an, CN;
Ji Hui Yang, Beijing, CN;
Jun Wang, Xi'an, CN;
Jing James Xu, Xi'an, CN;
Dakuo Wang, Cambridge, MA (US);
Gregory Bramble, Larchmont, NY (US);
Horst Cornelius Samulowitz, Armonk, NY (US);
Saket K. Sathe, Mohegan Lake, NY (US);
Wesley M. Gifford, Ridgefield, CT (US);
Petros Zerfos, New York City, NY (US);
International Business Machines Corporation, Armonk, NY (US);
Abstract
To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.