Multi-model blending
Abstract
A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.
- Inventors:
- Issue Date:
- Research Org.:
- International Business Machines Corp., Armonk, NY (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1329307
- Patent Number(s):
- 9471884
- Application Number:
- 14/291,720
- Assignee:
- INTERNATIONAL BUSINESS MACHINES CORPORATION (Armonk, NY)
- Patent Classifications (CPCs):
-
G - PHYSICS G01 - MEASURING G01W - METEOROLOGY
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- DOE Contract Number:
- EE0006017
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2014 May 30
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 99 GENERAL AND MISCELLANEOUS; 97 MATHEMATICS AND COMPUTING
Citation Formats
Hamann, Hendrik F., Hwang, Youngdeok, van Kessel, Theodore G., Khabibrakhmanov, Ildar K., and Muralidhar, Ramachandran. Multi-model blending. United States: N. p., 2016.
Web.
Hamann, Hendrik F., Hwang, Youngdeok, van Kessel, Theodore G., Khabibrakhmanov, Ildar K., & Muralidhar, Ramachandran. Multi-model blending. United States.
Hamann, Hendrik F., Hwang, Youngdeok, van Kessel, Theodore G., Khabibrakhmanov, Ildar K., and Muralidhar, Ramachandran. Tue .
"Multi-model blending". United States. https://www.osti.gov/servlets/purl/1329307.
@article{osti_1329307,
title = {Multi-model blending},
author = {Hamann, Hendrik F. and Hwang, Youngdeok and van Kessel, Theodore G. and Khabibrakhmanov, Ildar K. and Muralidhar, Ramachandran},
abstractNote = {A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {10}
}
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