@inbook{abd67a6c1fb74e558a10e568a2efa736,
title = "Multi-Regional Surrogate Model Selection (MRSMS) approach for the analysis and optimal fitting of univariate responses",
abstract = "This study focuses on the development of a Multi-Regional Surrogate Model Selection (MRSMS) approach for the optimal fitting and analysis of univariate responses. Using a library of simple curve fitting and regression models, the proposed approach can be used to provide surrogate model recommendations at each section of the response based on the user specified selection of the residual error metric and its corresponding data fit range. The efficacy of the proposed approach is validated using the Henry Hub Natural Gas price dataset and its fitting performance is compared with Piecewise Linear (PL), Neural Network (NN) and Support Vector Regression (SVR) models. It was found that proposed MRSMS approach outperformed the fitting performance of the considered models.",
keywords = "Optimal Data Fitting, Piecewise Linear Model, Surrogate Model, Univariate Responses",
author = "Srinivas, \{Srikar V.\} and Karimi, \{I. A.\}",
note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",
year = "2022",
month = jul,
day = "30",
doi = "10.1016/B978-0-323-85159-6.50154-8",
language = "English",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier B.V.",
pages = "925--930",
booktitle = "Computer Aided Chemical Engineering",
}