Elastic Net Penalized Quantile Regression Model and Empirical Mode Decomposition for Improving the Accuracy of the Model Selection-Reference-Cited by-同舟云学术

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Elastic Net Penalized Quantile Regression Model and Empirical Mode Decomposition for Improving the Accuracy of the Model Selection

Published:2023 Issue: Volume:11 Page:26152-26162
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ambark Ali S. A.¹^ORCID,Ismail Mohd Tahir¹^ORCID,Al-Jawarneh Abdullah S.²^ORCID,Karim Samsul Ariffin Abdul³^ORCID

Affiliation:

1. School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia

2. Department of Mathematics, Faculty of Science, Jerash University, Jerash, Jordan

3. Software Engineering Program, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia

Funder

Research Management Center (RMC), Universiti Malaysia Sabah

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10068498.pdf?arnumber=10068498

Reference38 articles.

1. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

2. Lasso and ridge quantile regression using cross validation to estimate extreme rainfall;zaikarina;Global J Pure Appl Math,2016

3. Optimal Causal Decision Trees Ensemble for Improved Prediction and Causal Inference

4. Quantile regression

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1. Enhancing Model Selection by Obtaining Optimal Tuning Parameters in Elastic-Net Quantile Regression, Application to Crude Oil Prices;Journal of Risk and Financial Management;2024-07-26

2. Empirical mode decomposition and Hilbert–Huang transform-based eccentricity fault detection and classification with demagnetization in 120 kW interior permanent magnet synchronous motors;Expert Systems with Applications;2024-05

3. Ultra-Short-Term Wind Power Prediction Based on eEEMD-LSTM;Energies;2024-01-03

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